RNA (Ribonucleic Acid) representation acquisition method and device, electronic equipment and storage medium

Abstract

The invention provides an RNA representation obtaining method and device, electronic equipment and a storage medium, and relates to the technical field of artificial intelligence and biological information, in particular to the technical field of biological computing. According to the specific implementation scheme, the method comprises the following steps: obtaining a structural discretization expression of RNA according to structural information of the RNA; obtaining sequence discretization representation of the RNA according to the sequence information of the RNA; and fusing the structure discretization expression and the sequence discretization expression of the RNA to obtain the target expression of the RNA.

Description

Technical Field

[0001] The present disclosure relates to the fields of artificial intelligence and bioinformatics, specifically to the field of biocomputing technology, and in particular to a method, device, electronic device and storage medium for obtaining RNA representation. Background Art

[0002] Ribonucleic Acid (RNA) representation learning uses mathematical and computational methods to convert RNA sequences into feature vectors that can be used for machine learning. This technology is widely used in fields such as RNA sequence classification, structure prediction, and drug design, helping to reveal the functions and regulatory mechanisms of RNA in biological processes and promoting research progress in bioinformatics and drug development. However, in existing technologies, RNA sequence conversion does not take into account the structural information of RNA, resulting in poor tertiary structure prediction results. Summary of the invention

[0003] The present disclosure provides a method, device, electronic device and storage medium for obtaining RNA representation.

[0004] According to one aspect of the present disclosure, a method for obtaining a representation of RNA is provided, comprising: obtaining a structural discretization representation of the RNA based on structural information of the RNA; obtaining a sequence discretization representation of the RNA based on sequence information of the RNA; and fusing the structural discretization representation of the RNA and the sequence discretization representation to obtain a target representation of the RNA.

[0005] According to another aspect of the present disclosure, a device for acquiring a representation of RNA is provided, comprising: a first acquisition module, used to acquire a structural discretization representation of the RNA according to structural information of the RNA; a second acquisition module, used to acquire a sequence discretization representation of the RNA according to sequence information of the RNA; and a fusion module, used to fuse the structural discretization representation of the RNA and the sequence discretization representation to obtain a target representation of the RNA.

[0006] According to another aspect of the present disclosure, an electronic device is provided, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the RNA representation acquisition method described in the above-mentioned first embodiment.

[0007] According to another aspect of the present disclosure, a non-transitory computer-readable storage medium storing computer instructions is provided, on which a computer program / instructions are stored, and the computer instructions are used to enable the computer to execute the RNA representation acquisition method described in the above-mentioned embodiment.

[0008] According to another aspect of the present disclosure, a computer program product is provided, including a computer program / instruction, wherein when the computer program / instruction is executed by a processor, the method for obtaining the representation of RNA described in the embodiment of the first aspect is implemented.

[0009] It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present disclosure, nor is it intended to limit the scope of the present disclosure. Other features of the present disclosure will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] The accompanying drawings are used to better understand the present solution and do not constitute a limitation of the present disclosure.

[0011] Figure 1 A schematic diagram of a method for obtaining a representation of RNA provided in an embodiment of the present disclosure;

[0012] Figure 2 A schematic diagram of another method for obtaining RNA representation provided in an embodiment of the present disclosure;

[0013] Figure 3 A schematic diagram of a flow chart of a training process of a VQ-VAE model in a method for obtaining a representation of RNA provided in an embodiment of the present disclosure;

[0014] Figure 4 A schematic diagram of another method for obtaining RNA representation provided in an embodiment of the present disclosure;

[0015] Figure 5 A schematic diagram of a process of training a target language model in a method for obtaining a representation of RNA provided in an embodiment of the present disclosure;

[0016] Figure 6 A schematic diagram of another method for obtaining RNA representation provided in an embodiment of the present disclosure;

[0017] Figure 7 A schematic diagram of the structure of the target representation of obtaining RNA provided in the embodiment of the present disclosure;

[0018] Figure 8 A schematic diagram of the structure of an RNA representation acquisition device provided in an embodiment of the present disclosure;

[0019] Fig. 9 The present invention is a block diagram of an electronic device for implementing the RNA representation acquisition method according to an embodiment of the present disclosure. DETAILED DESCRIPTION

[0020] The following is a description of exemplary embodiments of the present disclosure in conjunction with the accompanying drawings, including various details of the embodiments of the present disclosure to facilitate understanding, which should be considered as merely exemplary. Therefore, it should be recognized by those of ordinary skill in the art that various changes and modifications may be made to the embodiments described herein without departing from the scope and spirit of the present disclosure. Similarly, for the sake of clarity and conciseness, descriptions of well-known functions and structures are omitted in the following description.

[0021] The following describes the RNA representation acquisition method, device and electronic device of the embodiments of the present disclosure with reference to the accompanying drawings.

[0022] Artificial Intelligence (AI) is a discipline that studies how computers can simulate certain thought processes and intelligent behaviors of human beings (such as learning, reasoning, thinking, planning, etc.). It includes both hardware-level technologies and software-level technologies. AI hardware technologies generally include computer vision technology, speech recognition technology, natural language processing technology, as well as learning / deep learning, big data processing technology, knowledge graph technology, and other aspects.

[0023] Biocomputing is a field that borrows principles and mechanisms from biological systems to solve computational problems. It applies some biological properties and processes to computing systems to improve computational efficiency and performance. The goal of biocomputing is to take inspiration from biological systems and translate it into new computational methods and technologies to solve complex problems. It has a wide range of applications in optimization, pattern recognition, data analysis, and simulation, and is constantly developing and expanding.

[0024] Figure 1 A schematic diagram of a process for obtaining a representation of RNA provided in an embodiment of the present disclosure.

[0025] like Figure 1 As shown, the method for obtaining the RNA may include:

[0026] S101, obtaining a discretized representation of the structure of the RNA according to the structural information of the RNA.

[0027] It should be noted that the execution subject of the RNA representation acquisition method in the disclosed embodiment may be a hardware device with data processing capabilities and / or the necessary software required to drive the hardware device to work. Optionally, the execution subject may include a server, a user terminal and other intelligent devices. Optionally, the user terminal includes but is not limited to a mobile phone, a computer, an intelligent voice interaction device, etc. Optionally, the server includes but is not limited to a network server, an application server, and may also be a server of a distributed system, or a server combined with a blockchain, etc. The disclosed embodiment is not specifically limited.

[0028] In some implementations, the structural information of RNA includes secondary structure information and tertiary structure information. Among them, the secondary structure information of RNA mainly describes the pairing between bases in the RNA molecule, which is determined by the hydrogen bond interaction between bases in the RNA chain. The tertiary structure information of RNA further describes the shape and conformation of the RNA molecule in three-dimensional space. For example, the tertiary structure information of RNA can be the three-dimensional coordinates of the molecule in RNA.

[0029] Optionally, the discretized structural representation of the RNA may be determined based on the secondary structure information and / or tertiary structure information of the RNA.

[0030] In some implementations, the structure discretization representation of RNA can be determined based on a pre-constructed discretization vocabulary and the structure information of RNA. The discretization vocabulary contains multiple structure discretization representations. The similarity between RNA and the structure discretization representation is calculated, and the structure discretization representation with the highest similarity is used as the structure discretization representation of RNA.

[0031] In some implementations, a coding model may be pre-trained and used to encode the structural information of RNA to obtain a discretized representation of the structure of RNA. The structural information of RNA is input into the pre-trained coding model, the coding model encodes the RNA, and the similarity between the encoded RNA and the discretized representation of the structure in the discretized vocabulary is calculated to determine the discretized representation of the structure of RNA.

[0032] S102, obtaining a discretized representation of the RNA sequence according to the RNA sequence information.

[0033] It is understood that the discretization of RNA sequences is a process of converting each nucleic acid in the RNA sequence into a numerical value or symbol. In the discretization, each nucleic acid is assigned a unique identifier, usually an integer or a specific character.

[0034] Alternatively, the nucleic acids of the RNA, such as adenine, cytosine, guanine and uracil, can be determined from the sequence information of the RNA, and then these four nucleic acids can be mapped to a number. For example, if adenine is A, cytosine is C, guanine is G, and uracil is U, then A can be mapped to 1, B to 2, C to 3, and D to 4. That is, if the RNA sequence is AUGCAU, the corresponding RNA sequence discretization representation is 132142.

[0035] S103, fusing the structure discretization representation and the sequence discretization representation of RNA to obtain the target representation of RNA.

[0036] Optionally, the structure discretization representation and the sequence discretization representation of RNA can be spliced ​​to achieve fusion to obtain the target representation of RNA. Different weights can also be assigned to the structure discretization representation and the sequence discretization representation of RNA, and weighted summation is performed to achieve fusion to obtain the target representation of RNA.

[0037] Optionally, a language model may be trained to extract structural features and sequence features from the structural discretization representation and the sequence discretization representation, and the features may be fused to generate a target representation of the fused RNA.

[0038] According to the RNA representation acquisition method provided by the embodiment of the present disclosure, by obtaining the structure discretization representation of RNA and the sequence discretization representation of RNA, and fusing the structure discretization representation of RNA and the sequence discretization representation of RNA, the target representation of RNA can be obtained, and the structural information and sequence information of RNA are fused, providing more comprehensive and richer information, which helps to more accurately describe the characteristics of RNA molecules and improve the accuracy and reliability of predicting RNA functions. Furthermore, by fusing the structural information and sequence information of RNA for representation learning, a theoretical basis can be provided for the diagnosis and treatment of diseases, and it is helpful to learn the interaction mechanism between RNA and protein, thereby accelerating the development of drugs.

[0039] Figure 2 A schematic diagram of a process for obtaining a representation of RNA provided in an embodiment of the present disclosure.

[0040] like Figure 2 As shown, the method for obtaining the RNA may include:

[0041] S201, generating a structure diagram of RNA according to the structure information of RNA.

[0042] In some implementations, the RNA-based structural diagram can intuitively display the spatial conformation of the RNA molecule, which helps to intuitively understand the RNA structure and helps to predict the RNA structure. In other words, the RNA structural diagram can be generated based on the RNA structural information.

[0043] Optionally, the structural information of the RNA includes at least one of secondary structure information and tertiary structure information of the RNA. That is, a structural diagram of the corresponding RNA can be generated based on the secondary structure information, a structural diagram of the corresponding RNA can be generated based on the tertiary structure information, and a structural diagram of the corresponding RNA can be generated based on the secondary structure information and the tertiary structure information.

[0044] That is, in response to the structural information including secondary structural information and tertiary structural information, a first structural graph of RNA is constructed according to the secondary structural information, and a second structural graph of RNA is constructed according to the tertiary structural information.

[0045] In some implementations, different ways of representing the structure of RNA may be selected to generate a structure diagram of RNA, for example, a dotted bracket representation, a computed tomography (CT) file representation, or a graphical representation may be selected to generate a structure diagram of RNA.

[0046] It can be understood that the dot-bracket representation uses dots and brackets to represent the secondary structure of RNA, where dots represent unpaired bases and paired brackets represent paired bases. The CT file representation is a file format that contains detailed information about the RNA structure, including the total number of nucleotides, folding energy, and base pairing. The graphical representation uses professional bioinformatics software or drawing tools to display the structure of RNA in the form of graphics, including two-dimensional plane diagrams and three-dimensional spatial diagrams.

[0047] S202, determining a discretized representation of the structure of the RNA based on the RNA structure graph and a pre-constructed discretized vocabulary.

[0048] In some implementations, the discretization vocabulary includes multiple structural discretization representations. The similarity between the structure graph of RNA and the structural discretization representation can be calculated, and the structural discretization representation with the highest similarity is used as the structural discretization representation of RNA.

[0049] Optionally, by obtaining the quantization vector corresponding to the structure graph of the RNA and calculating the similarity between the quantization vector and the structure discretization representation to determine the structure discretization representation of the RNA, the complexity of the structure graph of the RNA can be reduced, and redundant information in the structure graph of the RNA can be removed, thereby achieving effective compression of the structure graph.

[0050] Optionally, the latent space representation of RNA is obtained according to the structure graph of RNA, and then the latent space representation is quantized to obtain a quantized vector, and the structure discretization representation with the highest similarity to the quantized vector is obtained from a pre-constructed discretization vocabulary. Optionally, the similarity between the quantized vector and each structure discretization representation can be calculated, and the similarities can be sorted based on the similarity size, and the structure discretization representation with the greatest similarity can be used as the structure discretization representation with the highest similarity.

[0051] Furthermore, it can be determined that the structural discretization representation with the highest similarity is the structural discretization representation of RNA.

[0052] In some implementations, a coding model may be pre-trained, and the coding model may be used to encode and quantize the structure graph of RNA, obtain a quantization vector corresponding to the structure graph of RNA, and match the discretization representation of the structure with the highest similarity from the discretization vocabulary according to the quantization vector as the discretization representation of the structure of RNA. The coding model may be a vector quantized variational autoencoder (VQ-VAE) model, the structure of which includes an encoder and a quantization layer.

[0053] That is, by inputting the structure graph of RNA into the pre-trained first vector quantized variational autoencoder VQ-VAE model, the encoder in the first VQ-VAE model encodes the structure graph to obtain a latent space representation. Optionally, a graph neural network (GNN) can be used to encode the structure graph.

[0054] Furthermore, the quantization layer in the first VQ-VAE model quantizes the latent space representation and matches the quantized vector in the discretized vocabulary to obtain the structural discretized representation of RNA, so as to effectively compress the RNA data and improve the generation efficiency and quality of the model for the structural discretized representation, which is particularly suitable for the storage and processing of large-scale data.

[0055] S203, obtaining a discretized representation of the RNA sequence according to the RNA sequence information.

[0056] S204, fusing the structure discretization representation and the sequence discretization representation of the RNA to obtain a target representation of the RNA.

[0057] The relevant contents of steps S203 - S204 can be found in the above embodiment and will not be described again here.

[0058] According to the RNA representation acquisition method provided by the embodiment of the present disclosure, a corresponding structure diagram can be generated based on the RNA structural information, and the structure diagram can be quantified to determine the structure discretization representation of RNA, and then the structure discretization representation of RNA and the sequence discretization representation of RNA can be fused to obtain the target representation of RNA, thereby realizing the fusion of RNA structural information and sequence information, providing more comprehensive and richer information, and helping to more accurately describe the characteristics of RNA molecules, and improving the accuracy and reliability of predicting RNA functions. Furthermore, by fusing RNA structural information and sequence information for representation learning, a theoretical basis can be provided for the diagnosis and treatment of diseases, and it is helpful to learn the interaction mechanism between RNA and protein, thereby accelerating the process of drug development.

[0059] Based on the above embodiments, the present disclosure can explain the training process of the VQ-VAE model, such as Figure 3 As shown, the training process of the VQ-VAE model may include:

[0060] S301, obtaining sample RNA, and obtaining sample structure information of the sample RNA.

[0061] Alternatively, the sample RNA may be extracted from sample cells or sample tissues, and an extraction reagent may be used to extract the sample RNA. For example, RNA may be extracted from animal or plant cells, tissues, or blood as the sample RNA.

[0062] Furthermore, bioinformatics tools can be used to determine the sample structure information of the sample RNA. The secondary structure of the sample RNA can be predicted to obtain the sample structure information. X-ray crystallography, cryo-electron microscopy or nuclear magnetic resonance can also be used to analyze the tertiary structure of the sample RNA as the sample structure information.

[0063] S302, input the sample structure information into the initial VQ-VAE model, and the encoder in the VQ-VAE model encodes the sample structure information to obtain the sample latent space representation.

[0064] In some implementations, the model structure of the initial VQ-VAE model includes: an encoder, a decoder, and a quantization layer. By inputting the sample structure information into the initial VQ-VAE model, the encoder in the model encodes the sample structure information to obtain the sample latent space representation.

[0065] Optionally, the encoder obtains a sample latent space representation by mapping the sample structure information to a continuous vector representation in the latent space.

[0066] S303, the quantization layer in the VQ-VAE model quantizes the sample latent space representation, and matches the sample quantization vector in the reference discretization vocabulary to obtain the sample discretization sequence of the sample RNA.

[0067] In some implementations, the main function of the quantization layer is to map the continuous latent space representation output by the encoder into a discrete latent space, that is, by quantizing the sample latent space representation, a sample quantization vector is obtained, and the similarity between the sample quantization vector and the discretization representation in the reference discretization vocabulary is calculated, and based on the similarity, the sample quantization vector and the reference discretization vocabulary are matched to obtain the sample discretization sequence of the sample RNA.

[0068] Optionally, the distance between the sample quantization vector and the discretization representation in the reference discretization vocabulary, such as the Euclidean distance, can be calculated, and the discretization representation with the closest distance, that is, the most similar discretization representation, can be determined.

[0069] S304, the decoder in the VQ-VAE model decodes the sample discretized sequence to obtain the reduced RNA.

[0070] In some implementations, the decoder in the VQ-VAE model can decode the sample discretization sequence by receiving the sample discretization sequence sent by the quantization layer to reconstruct the sample discretization sequence and obtain the restored RNA.

[0071] Optionally, the reduced RNA may be generated based on a nonlinear transformation, such as by performing a convolution transformation on a discretized sequence of the sample to generate the reduced RNA.

[0072] S305, adjusting the model parameters of the VQ-VAE model according to the sample RNA and the reduced RNA, as well as the sample latent space table and the sample discretization sequence, and continuing the training until the end condition is met, to obtain a second VQ-VAE model, wherein the first VQ-VAE model only includes the encoder and quantization layer in the second VQ-VAE model.

[0073] In some implementations, the loss function of the model can be determined based on the sample RNA and the reduced RNA, as well as the sample latent space table and the sample discretized sequence, and the model parameters can be adjusted based on the loss function to obtain an adjusted VQ-VAE model, and the adjusted VQ-VAE model can be used to continue training until the end condition is met to obtain a second VQ-VAE model. The second VQ-VAE model includes an encoder, a decoder, and a quantization layer. When using the trained second VQ-VAE model, only an encoder and a quantization layer are required, that is, the first VQ-VAE model in the above embodiment only includes an encoder and a quantization layer.

[0074] Optionally, the training end condition may be that the number of training times reaches a number threshold, the training end condition may also be that the loss function is less than a set threshold, or the training end condition may also be that the accuracy of the model is greater than an accuracy threshold.

[0075] In some implementations, the loss function of the VQ-VAE model includes reconstruction loss and quantization loss. The reconstruction loss can be determined based on the sample RNA and the reduced RNA, and the quantization loss can be determined based on the sample latent space table and the sample discretization sequence. Among them, the reconstruction loss is used to measure the reconstruction ability of the decoder; the quantization loss encourages the encoder to learn to map the continuous latent vector to the reference discretization vocabulary.

[0076] Optionally, mean square error and cross entropy can be used as reconstruction loss of the VQ-VAE model. Square loss and cross entropy loss can be used as quantization loss of the VQ-VAE model.

[0077] Furthermore, the model parameters of the VQ-VAE model can be adjusted according to the reconstruction loss and the quantization loss. In other words, the model parameters are optimized collaboratively through the reconstruction loss and the quantization loss, so that the VQ-VAE model can not only learn the discrete representation of RNA, but also significantly reduce the storage requirements and improve the quality of the generated data while retaining the important features of RNA.

[0078] According to the RNA representation acquisition method provided by the embodiment of the present disclosure, the initial VQ-VAE model is trained according to the sample RNA and the sample structure information of the sample RNA, and the model parameters are adjusted using the loss function until a trained VQ-VAE model is obtained. This allows the model to automatically process the structural graph of the received RNA, improve processing efficiency, and obtain a highly accurate discretized structural representation of the RNA.

[0079] Figure 4 A schematic diagram of a process for obtaining a representation of RNA provided in an embodiment of the present disclosure.

[0080] like Figure 4 As shown, the method for obtaining the RNA may include:

[0081] S401, obtaining a discretized representation of the structure of the RNA according to the structural information of the RNA.

[0082] S402, obtaining a discretized representation of the RNA sequence according to the RNA sequence information.

[0083] The relevant contents of steps S401-S402 can be found in the above embodiment and will not be repeated here.

[0084] S403, inputting the structure discretization representation and the sequence discretization representation into a pre-trained target language model, and the target language model fuses the structure discretization representation and the sequence discretization representation to obtain a target representation of RNA.

[0085] In some implementations, a target language model may be pre-trained, and the target language model may be used to fuse the structure discretization representation and the sequence discretization representation to obtain the target representation of RNA. Alternatively, structural features and sequence features may be extracted from the structure discretization representation and the sequence discretization representation, and the structural features and sequence features may be fused to obtain the target representation of RNA.

[0086] Optionally, the features of the structure discretization representation and the sequence discretization representation can be fused through methods such as feature concatenation, feature weighting, and attention mechanism.

[0087] According to the method for obtaining the representation of RNA provided by the embodiment of the present disclosure, by obtaining the structure discretization representation of RNA and the sequence discretization representation of RNA, and using the pre-trained target language model, the structure discretization representation of RNA and the sequence discretization representation of RNA are fused, and the target representation of RNA can be obtained, which realizes the fusion of the structure information and sequence information of RNA, provides more comprehensive and richer information, helps to more accurately describe the characteristics of RNA molecules, and improves the accuracy and reliability of predicting RNA functions. Further, by fusing the structure information and sequence information of RNA for representation learning, a theoretical basis can be provided for the diagnosis and treatment of diseases, and it is helpful to learn the interaction mechanism between RNA and protein, thereby accelerating the development of drugs.

[0088] Based on the above embodiments, the present disclosure can explain the training process of the target language model. Figure 5 As shown, the training process of the target language model may include:

[0089] S501, segmenting the sample discretization representation of the sample RNA to obtain structural segmentation.

[0090] S502, segmenting the discretized representation of the sample to obtain sequence segmentations.

[0091] In some implementations, each structure in the sample discretized representation of the sample RNA may be segmented to obtain a structural segmentation of the sample RNA. For example, each hairpin loop, stem, or loop is considered as a structural segmentation. Sequences are extracted from the discretized representation of the sample RNA, and the extracted subsequences are used as sequence segmentations.

[0092] S503, training the language model based on the structural segmentation and the sequence segmentation until the training is completed to obtain the target language model.

[0093] In some implementations, the structural segmentation and sequence segmentation can be concatenated, and the concatenated segmentation sequence can be used to train the language model until the training is completed to obtain the target language model, so as to improve the target language model's ability to understand RNA structure and sequence, and further improve the accuracy of RNA structure prediction using the target language model.

[0094] Optionally, the structural segmentation and the sequence segmentation can be directly concatenated to obtain a first concatenated segmentation sequence, and the language model is trained based on the first concatenated segmentation sequence. For example, the structural segmentation is a structural token, and the sequence segmentation is a sequence token, then the first concatenated segmentation sequence is a structural token + a sequence token.

[0095] Optionally, the structural segmentation words and the sequence segmentation words may be concatenated to obtain a first concatenated segmentation sequence, and the structural segmentation words and the sequence segmentation words in the first concatenated segmentation sequence may be cross-masked to obtain a second concatenated segmentation sequence, and then the language model may be trained based on the second concatenated segmentation sequence.

[0096] Optionally, the training can be terminated when the number of training times reaches a set number, and the accuracy of the model output result greater than an accuracy threshold can be regarded as a training combination.

[0097] According to the RNA representation acquisition method provided by the embodiment of the present disclosure, by performing word segmentation on the discretized representation of the sample to obtain structural word segmentation and sequence word segmentation, and splicing the structural word segmentation and sequence word segmentation, and using the spliced ​​word segmentation sequence to train the language model, a target language model can be obtained, which can predictably reduce the amount of calculation of the model during the training process, help to accelerate the training process and improve the performance of the model, and further improve the accuracy of the target language model in predicting specific functional areas of RNA sequences and predicting gene expression levels.

[0098] Figure 6 A schematic diagram of a process for obtaining a representation of RNA provided in an embodiment of the present disclosure.

[0099] like Figure 6 As shown, the method for obtaining the RNA may include:

[0100] S601, generating a structure diagram of RNA according to the structure information of RNA.

[0101] S602, determining a discretized representation of the structure of the RNA based on the RNA structure graph and a pre-constructed discretized vocabulary.

[0102] S603, obtaining a discretized representation of the RNA sequence according to the RNA sequence information.

[0103] S604, inputting the structure discretization representation and the sequence discretization representation into a pre-trained target language model, and the target language model fuses the structure discretization representation and the sequence discretization representation to obtain a target representation of RNA.

[0104] The relevant contents of steps S601-S604 can be found in the above embodiment and will not be repeated here.

[0105] According to the RNA representation acquisition method provided by the embodiment of the present disclosure, by obtaining the structure discretization representation of RNA and the sequence discretization representation of RNA, and fusing the structure discretization representation of RNA and the sequence discretization representation of RNA, the target representation of RNA can be obtained, and the structural information and sequence information of RNA are fused, providing more comprehensive and richer information, which helps to more accurately describe the characteristics of RNA molecules and improve the accuracy and reliability of predicting RNA functions. Furthermore, by fusing the structural information and sequence information of RNA for representation learning, a theoretical basis can be provided for the diagnosis and treatment of diseases, and it is helpful to learn the interaction mechanism between RNA and protein, thereby accelerating the development of drugs.

[0106] like Figure 7 Schematic diagram of the structure of RNA target representation is shown. Figure 7 The method includes a structure representation module and a target representation module. The structure representation module includes a first VQ-VAE model, and the target representation module includes a target language model. The structure information of RNA is input into the structure representation module, and the first VQ-VAE model in the structure representation module encodes and matches the structure information to determine the structure discretization representation of RNA. The sequence discretization representation of RNA is obtained, and the sequence discretization representation of RNA and the structure discretization representation of RNA are input into the target representation module, and the target language model in the target representation module fuses the sequence discretization representation of RNA and the structure discretization representation of RNA to obtain the target representation of RNA.

[0107] Corresponding to the RNA representation acquisition methods provided in the above-mentioned embodiments, an embodiment of the present disclosure further provides an RNA representation acquisition device. Since the RNA representation acquisition device provided in the embodiment of the present disclosure corresponds to the RNA representation acquisition methods provided in the above-mentioned embodiments, the implementation methods of the above-mentioned RNA representation acquisition methods are also applicable to the RNA representation acquisition device provided in the embodiment of the present disclosure, and will not be described in detail in the following embodiments.

[0108] Figure 8 A schematic diagram of the structure of an RNA representation acquisition device provided in an embodiment of the present disclosure.

[0109] like Figure 8 As shown, the RNA representation acquisition device 800 of the embodiment of the present disclosure includes a first acquisition module 801, a second acquisition module 802 and a fusion module 803.

[0110] A first acquisition module 801 is used to acquire a discretized representation of the structure of the RNA according to the structure information of the RNA;

[0111] A second acquisition module 802 is used to acquire a sequence discretization representation of the RNA according to the sequence information of the RNA;

[0112] The fusion module 803 is used to fuse the structure discretization representation and the sequence discretization representation of the RNA to obtain the target representation of the RNA.

[0113] In one embodiment of the present disclosure, the first acquisition module 801 is further used to: generate a structural diagram of the RNA according to the structural information of the RNA; and determine a discretized structural representation of the RNA based on the structural diagram of the RNA and a pre-constructed discretized vocabulary.

[0114] In one embodiment of the present disclosure, the first acquisition module 801 is further used to: in response to the structural information including the secondary structure information and the tertiary structure information, construct a first structural graph of the RNA according to the secondary structure information, and construct a second structural graph of the RNA according to the tertiary structure information.

[0115] In one embodiment of the present disclosure, the first acquisition module 801 is further used to: acquire a latent space representation of the RNA according to the structure diagram of the RNA; quantize the latent space representation to obtain a quantization vector, and acquire a structural discretization representation with the highest similarity to the quantization vector from a pre-constructed discretization vocabulary; and determine the structural discretization representation with the highest similarity as the structural discretization representation of the RNA.

[0116] In one embodiment of the present disclosure, the first acquisition module 801 is further used to: input the structure graph of the RNA into a pre-trained first vector quantization variational autoencoder VQ-VAE model, and the encoder in the first VQ-VAE model encodes the structure graph to obtain the latent space representation; the quantization layer in the first VQ-VAE model quantizes the latent space representation, and matches the quantized vector in the discretized vocabulary to obtain a discretized structural representation of the RNA.

[0117] In one embodiment of the present disclosure, the first acquisition module 801 is further used to: acquire sample RNA, and acquire sample structure information of the sample RNA; input the sample structure information into the initial VQ-VAE model, and the encoder in the VQ-VAE model encodes the sample structure information to obtain a sample latent space representation; the quantization layer in the VQ-VAE model quantizes the sample latent space representation, and matches the sample quantization vector in a reference discretization vocabulary to obtain a sample discretization sequence of the sample RNA; the decoder in the VQ-VAE model decodes the sample discretization sequence to obtain a reduced RNA; adjust the model parameters of the VQ-VAE model according to the sample RNA and the reduced RNA, as well as the sample latent space table and the sample discretization sequence, and continue training until the end condition is met to obtain a second VQ-VAE model, wherein the first VQ-VAE model only includes the encoder and quantization layer in the second VQ-VAE model.

[0118] In one embodiment of the present disclosure, the first acquisition module 801 is further used to: determine the reconstruction loss based on the sample RNA and the reduced RNA; determine the quantization loss based on the sample latent space table and the sample discretization sequence; and adjust the model parameters of the VQ-VAE model based on the reconstruction loss and the quantization loss.

[0119] In one embodiment of the present disclosure, the fusion module 803 is further used to: input the structure discretization representation and the sequence discretization representation into a pre-trained target language model, and the target language model fuses the structure discretization representation and the sequence discretization representation to obtain the target representation of the RNA.

[0120] In one embodiment of the present disclosure, the fusion module 803 is further used to: perform word segmentation on the sample discretization representation of the sample RNA to obtain structural word segmentation; perform word segmentation on the sample discretization representation to obtain sequence word segmentation; and train a language model based on the structural word segmentation and sequence word segmentation until the training is completed to obtain the target language model.

[0121] In one embodiment of the present disclosure, the fusion module 803 is further used to: concatenate the structural segmentation and the sequence segmentation to obtain a first concatenated segmentation sequence; and train the language model based on the first concatenated segmentation sequence.

[0122] In one embodiment of the present disclosure, the fusion module 803 is further used to: concatenate the structural segmentation words and the sequence segmentation words to obtain a first concatenated segmentation sequence; cross-mask the structural segmentation words and the sequence segmentation words in the first concatenated segmentation sequence to obtain the second concatenated segmentation sequence; and train the language model based on the second concatenated segmentation sequence.

[0123] According to the RNA representation acquisition device provided by the embodiment of the present disclosure, by acquiring the structure discretization representation of RNA and the sequence discretization representation of RNA, and fusing the structure discretization representation of RNA and the sequence discretization representation of RNA, the target representation of RNA can be obtained, and the structural information and sequence information of RNA are fused, providing more comprehensive and richer information, which helps to more accurately describe the characteristics of RNA molecules and improves the accuracy and reliability of predicting RNA functions. Further, by fusing the structural information and sequence information of RNA for representation learning, a theoretical basis can be provided for the diagnosis and treatment of diseases, and it is helpful to learn the interaction mechanism between RNA and protein, thereby accelerating the development of drugs.

[0124] In the technical solution disclosed herein, the acquisition, storage and application of user personal information involved are in compliance with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0125] According to an embodiment of the present disclosure, the present disclosure also provides an electronic device, a readable storage medium and a computer program product.

[0126] Fig. 9 A schematic block diagram of an example electronic device 900 that can be used to implement an embodiment of the present disclosure is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present disclosure described and / or required herein.

[0127] like Fig. 9As shown, the device 900 includes a computing unit 901, which can perform various appropriate actions and processes according to a computer program / instruction stored in a read-only memory (ROM) 902 or a computer program / instruction loaded from a storage unit 906 to a random access memory (RAM) 903. In the RAM 903, various programs and data required for the operation of the device 900 can also be stored. The computing unit 901, the ROM 902, and the RAM 903 are connected to each other via a bus 904. An input / output (I / O) interface 905 is also connected to the bus 904.

[0128] A number of components in the device 900 are connected to the I / O interface 905, including: an input unit 906 such as a keyboard, a mouse, etc.; an output unit 907 such as various types of displays, speakers, etc.; a storage unit 908 such as a disk, an optical disk, etc.; and a communication unit 909 such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 909 allows the device 900 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0129] The computing unit 901 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the computing unit 901 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, digital signal processors (DSPs), and any appropriate processors, controllers, microcontrollers, etc. The computing unit 901 performs the various methods and processes described above, such as the RNA representation acquisition method. For example, in some embodiments, the RNA representation acquisition method may be implemented as a computer software program, which is tangibly contained in a machine-readable medium, such as a storage unit 906. In some embodiments, part or all of the computer program / instructions may be loaded and / or installed on the device 900 via ROM 902 and / or communication unit 909. When the computer program / instructions are loaded into RAM 903 and executed by the computing unit 901, one or more steps of the RNA representation acquisition method described above may be performed. Alternatively, in other embodiments, the computing unit 901 may be configured to perform the RNA representation acquisition method in any other appropriate manner (e.g., by means of firmware).

[0130] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs / instructions that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0131] The program code for implementing the method of the present disclosure may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that the program code, when executed by the processor or controller, enables the functions / operations specified in the flow chart and / or block diagram to be implemented. The program code may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0132] In the context of the present disclosure, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or equipment, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0133] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0134] The systems and techniques described herein can be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), the Internet, and a blockchain network.

[0135] A computer system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The relationship of client and server is generated by computer programs / instructions running on respective computers and having a client-server relationship with each other. The server may be a cloud server, a server of a distributed system, or a server combined with a blockchain.

[0136] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps recorded in the disclosure can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solutions disclosed in the disclosure can be achieved, and this document does not limit them here.

[0137] The above specific implementations do not constitute a limitation on the protection scope of the present disclosure. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present disclosure shall be included in the protection scope of the present disclosure.

Claims

1. A method for obtaining a representation of ribonucleic acid RNA, wherein: The method comprises: According to the structural information of the RNA, obtaining a discretized structural representation of the RNA; According to the sequence information of the RNA, obtaining a sequence discretization representation of the RNA; The structure discretization representation and the sequence discretization representation of the RNA are fused to obtain a target representation of the RNA.

2. The method according to claim 1, wherein: The step of obtaining a discretized representation of the structure of the RNA according to the structural information of the RNA comprises: Generating a structural diagram of the RNA according to the structural information of the RNA; Based on the structure graph of the RNA and a pre-constructed discretization vocabulary, a structural discretization representation of the RNA is determined.

3. The method according to claim 2, wherein: The structural information of the RNA includes at least one of secondary structure information and tertiary structure information of the RNA, and generating a structural diagram of the RNA according to the structural information of the RNA includes: In response to the structural information including the secondary structural information and the tertiary structural information, a first structural graph of the RNA is constructed according to the secondary structural information, and a second structural graph of the RNA is constructed according to the tertiary structural information.

4. The method according to claim 2, wherein: The step of determining the discretization representation of the structure of the RNA based on the structure graph of the RNA and a pre-constructed discretization vocabulary comprises: Obtaining a latent space representation of the RNA based on the structure graph of the RNA; quantizing the latent space representation to obtain a quantized vector, and obtaining a discretized representation of the structure having the highest similarity to the quantized vector from a pre-constructed discretized vocabulary; The structural discretization representation with the highest similarity is determined as the structural discretization representation of the RNA.

5. The method according to claim 3, wherein: The method comprises: Inputting the structure graph of the RNA into a pre-trained first vector quantized variational autoencoder VQ-VAE model, and encoding the structure graph by an encoder in the first VQ-VAE model to obtain the latent space representation; The latent space representation is quantized by the quantization layer in the first VQ-VAE model, and the quantized vector is matched in the discretized vocabulary to obtain a structural discretized representation of the RNA.

6. The method according to claim 4, wherein: The training process of the VQ-VAE model includes: Obtaining sample RNA and obtaining sample structure information of the sample RNA; Inputting the sample structure information into an initial VQ-VAE model, and encoding the sample structure information by an encoder in the VQ-VAE model to obtain a sample latent space representation; The sample latent space representation is quantized by a quantization layer in the VQ-VAE model, and the sample quantization vector is matched in a reference discretization vocabulary to obtain a sample discretization sequence of the sample RNA; The decoder in the VQ-VAE model decodes the sample discretization sequence to obtain a reduced RNA; According to the sample RNA and the reduced RNA, the sample latent space table and the sample discretization sequence, the model parameters of the VQ-VAE model are adjusted, and the training is continued until the termination condition is met to obtain a second VQ-VAE model, wherein the first VQ-VAE model only includes the encoder and quantization layer in the second VQ-VAE model.

7. The method according to claim 6, wherein: The adjusting of the model parameters of the VQ-VAE model according to the sample RNA and the reduced RNA, the sample latent space table and the sample discretization sequence comprises: determining a reconstruction loss based on the sample RNA and the reduced RNA; Determining a quantization loss according to the sample latent space table and the sample discretization sequence; According to the reconstruction loss and the quantization loss, the model parameters of the VQ-VAE model are adjusted.

8. The method according to claim 1, wherein: The step of fusing the structure discretization representation of the RNA and the sequence discretization representation to obtain the target representation of the RNA includes: The structure discretization representation and the sequence discretization representation are input into a pre-trained target language model, and the target language model fuses the structure discretization representation and the sequence discretization representation to obtain a target representation of the RNA.

9. The method according to claim 8, wherein: The training process of the target language model includes: Perform word segmentation on the sample discretization representation of the sample RNA to obtain structural word segmentation; Performing word segmentation on the discretized representation of the sample to obtain sequence word segmentation; Based on the structural segmentation and the sequence segmentation, the language model is trained until the training is completed to obtain the target language model.

10. The method according to claim 8, wherein: The training of the language model based on the structural segmentation and the sequence segmentation includes: splicing the structural participles and the sequence participles to obtain a first spliced ​​participle sequence; The language model is trained based on the first concatenated word segmentation sequence.

11. The method according to claim 8, wherein: The training of the language model based on the structural segmentation and the sequence segmentation includes: splicing the structural participles and the sequence participles to obtain a first spliced ​​participle sequence; Cross-masking the structural segmentation words and the sequence segmentation words in the first concatenated segmentation sequence to obtain the second concatenated segmentation sequence; The language model is trained based on the second concatenated word segmentation sequence.

12. A device for obtaining RNA expression, wherein: The device comprises: A first acquisition module is used to acquire a discretized structural representation of the RNA according to the structural information of the RNA; A second acquisition module is used to acquire a sequence discretization representation of the RNA according to the sequence information of the RNA; A fusion module is used to fuse the structure discretization representation of the RNA and the sequence discretization representation to obtain a target representation of the RNA.

13. The device according to claim 12, wherein: The first acquisition module is further used for: Generating a structural diagram of the RNA according to the structural information of the RNA; Based on the structure graph of the RNA and a pre-constructed discretization vocabulary, a structural discretization representation of the RNA is determined.

14. The device according to claim 13, wherein: The first acquisition module is further used for: In response to the structural information including the secondary structural information and the tertiary structural information, a first structural graph of the RNA is constructed according to the secondary structural information, and a second structural graph of the RNA is constructed according to the tertiary structural information.

15. The device according to claim 13, wherein: The first acquisition module is further used for: Obtaining a latent space representation of the RNA based on the structure graph of the RNA; quantizing the latent space representation to obtain a quantized vector, and obtaining a discretized representation of the structure having the highest similarity to the quantized vector from a pre-constructed discretized vocabulary; The structural discretization representation with the highest similarity is determined as the structural discretization representation of the RNA.

16. The device according to claim 14, wherein: The first acquisition module is further used for: Inputting the structure graph of the RNA into a pre-trained first vector quantized variational autoencoder VQ-VAE model, and encoding the structure graph by an encoder in the first VQ-VAE model to obtain the latent space representation; The latent space representation is quantized by the quantization layer in the first VQ-VAE model, and the quantized vector is matched in the discretized vocabulary to obtain a structural discretized representation of the RNA.

17. The device according to claim 15, wherein: The first acquisition module is further used for: Obtaining sample RNA and obtaining sample structure information of the sample RNA; Inputting the sample structure information into an initial VQ-VAE model, and encoding the sample structure information by an encoder in the VQ-VAE model to obtain a sample latent space representation; The sample latent space representation is quantized by a quantization layer in the VQ-VAE model, and the sample quantization vector is matched in a reference discretization vocabulary to obtain a sample discretization sequence of the sample RNA; The decoder in the VQ-VAE model decodes the sample discretization sequence to obtain a reduced RNA; According to the sample RNA and the reduced RNA, the sample latent space table and the sample discretization sequence, the model parameters of the VQ-VAE model are adjusted, and the training is continued until the termination condition is met to obtain a second VQ-VAE model, wherein the first VQ-VAE model only includes the encoder and quantization layer in the second VQ-VAE model.

18. The device according to claim 17, wherein: The first acquisition module is further used for: determining a reconstruction loss based on the sample RNA and the reduced RNA; Determining a quantization loss according to the sample latent space table and the sample discretization sequence; According to the reconstruction loss and the quantization loss, the model parameters of the VQ-VAE model are adjusted.

19. The device according to claim 12, wherein: The fusion module is further used for: The structure discretization representation and the sequence discretization representation are input into a pre-trained target language model, and the target language model fuses the structure discretization representation and the sequence discretization representation to obtain a target representation of the RNA.

20. The device according to claim 19, wherein The fusion module is further used for: Perform word segmentation on the sample discretization representation of the sample RNA to obtain structural word segmentation; Performing word segmentation on the discretized representation of the sample to obtain sequence word segmentation; Based on the structural segmentation and the sequence segmentation, the language model is trained until the training is completed to obtain the target language model.

21. The device according to claim 19, wherein The fusion module is further used for: splicing the structural participles and the sequence participles to obtain a first spliced ​​participle sequence; The language model is trained based on the first concatenated word segmentation sequence.

22. The device according to claim 19, wherein The fusion module is further used for: splicing the structural participles and the sequence participles to obtain a first spliced ​​participle sequence; Cross-masking the structural segmentation words and the sequence segmentation words in the first concatenated segmentation sequence to obtain the second concatenated segmentation sequence; The language model is trained based on the second concatenated word segmentation sequence.

23. An electronic device, comprising: at least one processor; as well as a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method according to any one of claims 1 to 11.

24. A non-transitory computer-readable storage medium storing computer instructions, wherein: The computer instructions are used to cause the computer to execute the method according to any one of claims 1-11.

25. A computer program product comprising a computer program / instructions, characterized in that When the computer program / instructions are executed by a processor, the method according to any one of claims 1 to 11 is implemented.

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