104 results about "Molecular sequence" patented technology

The invention provides a multi-modal characterization molecular property prediction method based on hierarchical bidirectional cross attention, and relates to the technical field of machine learning assisted organic chemistry, and the method comprises the following steps: S10, generating same-molecule multiple sequences for data enhancement; s20, coding the sequence features through a pre-trained molecular language model MolBERT; s30, performing multi-modal feature fusion through a layered bidirectional cross attention mechanism; s40, establishing a prediction head; s50, in the reasoning stage, only the feature extraction and fusion steps are executed, and a molecular property prediction result is output through the trained prediction head. According to the method, the molecular sequence, the topological graph structure and the fingerprint features are effectively integrated, so that the prediction precision of the model on a plurality of MoleculeNet (molecular network benchmark) public data sets is superior to that of an existing method.

The invention provides a new drug molecule design method and device fusing convex optimization and evolutionary learning, and belongs to the field of biological information.The method comprises the steps that in a generative adversarial network, a generator is adopted to generate a small molecule sequence, and the generator is established by adding an attention mechanism model in a long-short-term memory network; evaluating the authenticity of the small molecule sequence by adopting a discriminator, wherein the discriminator is established by adopting a convolutional neural network improved based on convex optimization; updating the parameters of the generator by adopting a strategy gradient method, and updating the parameters of the discriminator by adopting a gradient descent method; and generating a final small molecule sequence by the generator corresponding to the final generator parameter. According to the method, the small molecules which are highly similar to original samples and have higher effectiveness and innovativeness can be generated, the diversity and quality of the generated molecules are improved, and particularly, the method has excellent performance in the aspect of generating molecules with specific functions.

The invention belongs to the field of bioinformatics, and relates to a parallel full-connection network-based drug and target affinity prediction method, which comprises the technologies of deep learning, a parallel full-connection network, an attention mechanism and the like. The method comprises the following steps: firstly, preprocessing data of a drug molecule sequence and a protein sequence, performing feature extraction on the molecule sequence by using a Morgan fingerprint model, and performing feature extraction on the protein sequence by using a pre-trained protein language model ESM-2; secondly, respectively inputting the processed molecular sequence and protein sequence into a parallel full-connection network for training, and storing a trained model; and finally, inputting a drug and a target spot which need to be predicted into the model, and calculating a cosine distance as a prediction result. According to the method provided by the invention, the accuracy of predicting the combination of the drug on the target protein is effectively improved, and the discovery process of the drug is promoted.

The invention relates to a molecular property prediction method and system based on multi-task pre-training and multi-modal fusion. The method comprises the following steps: collecting a data set and performing molecular conversion; performing multi-task pre-training, which comprises the following steps: generating a heterogeneous enhanced view; constructing a pseudo label; performing comparative learning according to the structure enhanced view and the heterogeneous enhanced view, and constructing a maximum similar task; capturing semantic differences among molecules according to the pseudo labels; carrying out multi-modal fusion, namely introducing functional group structure information, and extracting molecular sequence characteristics based on Transform and Mamba2 to obtain fused molecular multi-modal representation; and analyzing and predicting the classification or regression task. A heterogeneous enhanced view is established in a multi-task pre-training stage, multi-task self-supervision is performed in combination with multi-granularity features of molecular fingerprints, and functional group structure information is introduced in a multi-modal fusion stage, so that deep cross-modal interaction is realized, downstream prediction performance is improved, and candidate drug screening and molecular property evaluation processes are accelerated.

A computer-implemented method for identifying a consensus molecular sequence from full length and partial length read sequencing data of a sample. The method includes sorting the sequencing data into full length and partial length reads with 5' and / or 3' binding sites, clustering the full length reads and the partial length reads, the clustering based on a position of binding site(s), and comparing a partial length read cluster to a full length read cluster to determine a positional distance between the compared clusters. In response to determining that the positional distance between the compared clusters falls within a redetermined threshold, merging the compared clusters, and identifying a consensus molecular sequence of the sample based on the merged clusters.

The invention provides a library building method for direct sequencing of single-stranded nucleic acid, the library building method comprises providing a to-be-detected sequence fragment, and a preparation method of the to-be-detected sequence fragment comprises cutting a double-stranded molecular sequence formed by complementary pairing of internal bases of the single-stranded nucleic acid by using transposase so as to form the to-be-detected sequence fragment. Compared with an existing library building method of RNA direct sequencing, the library building method has the advantages that a pairing structure is formed by complementary bases of RNA or single-stranded DNA, and a transposase compound can be used for shearing and connecting the pairing structure. This is an attempt which has not been made before, and the library building efficiency is greatly improved through the library building mode.

The invention discloses a high-precision molecular property prediction method and device based on multi-modal information. The method comprises the following steps: acquiring a molecular map, a molecular sequence and a molecular three-dimensional geometric configuration of a molecule; decomposing each molecular graph into a group of motifs, constructing a multi-stage pre-training framework to capture multi-scale information in the molecule, and performing molecular graph training on the molecule to obtain a molecular graph feature vector; designing an information transmission mechanism based on distance and angle, and carrying out learning training on the molecular three-dimensional geometric configuration to obtain a molecular geometric feature vector; preprocessing the molecular sequence feature vector of the molecule, and learning the molecular sequence information of the molecule to obtain a molecular sequence feature vector; and according to the molecular map feature vector, the molecular geometric feature vector and the molecular sequence feature vector, designing a molecular property prediction model based on a cross-modal cross attention mechanism to perform molecular property prediction. The method can enhance the robustness and accuracy of molecular property prediction.

The invention discloses a molecular multi-mode characterization method based on attention fusion, and the method comprises the following steps: firstly, carrying out the independent feature extraction of the one-dimensional, two-dimensional and three-dimensional modes of a molecule through a molecular sequence encoder, a molecular map encoder and a molecular conformation encoder, and carrying out the feature extraction of the one-dimensional, two-dimensional and three-dimensional features of the molecule; based on natural language text description, multi-level contrast learning is adopted, and gradual alignment of each mode and the text is achieved; secondly, multi-modal consistency constraint is introduced, the characterization distance of the same molecule in different modals is shortened, and semantic consistency is ensured; thirdly, adaptively integrating multi-modal features through a modal attention fusion module to obtain uniform molecular semantic representation, and further performing final alignment with a text embedding space; and finally, completing tasks such as molecule-text bidirectional retrieval and molecule attribute prediction by utilizing fusion representation. According to the method, under the condition that the model does not need to be retrained for different tasks, natural language-driven molecular retrieval, editing and property prediction can be realized, and the method has relatively high robustness, expansibility and universality.

The application discloses a soybean salt-tolerant related gene GmRLP15, a coding protein thereof and application thereof. The gene GmRLP15 is an NDA molecule as described in 1), 2) or 3) below: 1) a DNA molecule with a genomic sequence as shown in SEQ ID NO. 1; 2) a DNA molecule with a CDS sequence as shown in SEQ ID NO. 2; 3) a DNA molecule hybridized with the DNA sequence defined in 1) or 2) under stringent conditions and encoding the DNA molecule. The application provides a genetic engineering application of the gene GmRLP15 in regulating soybean salt tolerance, specifically overexpressing the aforementioned gene GmRLP15 to improve the salt tolerance of soybean.

The application discloses a biological macromolecule sequence structure prediction method, and relates to the technical field of computers. In the diffusion process, a geometric position coding gating mechanism is introduced, and a multi-scale attention broadcast mechanism is used to broadcast the attention weights corresponding to different attention types of the target information processed in different broadcast range modes, and the attention weights in different time steps in the broadcast range are reused, so that the same attention weight is used in the steps of the broadcast range, the calculation amount is reduced, the model efficiency performance is improved, the perception ability of the model to the amino acid position information in the protein sequence can be effectively improved, the model can fully consider the position relationship between amino acids when calculating the attention weight, the model can better identify the long-chain amino acid interaction, the structure prediction error caused by the missing position information is reduced, and the prediction precision is improved.

Embodiments of the present invention provide a system for detecting real-time anomalies in transmission path of resources within an entity network via DNA computing. The system is configured for determining that an application received a resource from a source system to be transmitted to an end application via an entity network, extracting metadata from the resource, wherein the metadata is in a binary format, converting the metadata in the binary format to a DNA format, generating predicted molecular sequence associated with predicted transmission path of the resource within the entity network based on the DNA format of the metadata, monitoring real-time transmission path of the resource, generating real-time molecular sequence associated with the real-time transmission path of the resource, comparing the predicted molecular sequence with the real-time molecular sequence to determine an anomaly, and transmitting alerts associated with the anomaly.

The invention discloses a molecular sequence and a primer for sex identification of grassland caterpillars and application of the molecular sequence and the primer. The nucleotide sequences of the molecular marker are as shown in SEQ ID NO: 1 and SEQ ID NO: 2; wherein a nucleotide sequence as shown in SEQ ID NO: 1 is female, and a nucleotide sequence as shown in SEQ ID NO: 2 is male. The invention also provides a primer group for amplifying the molecular fragment, and the nucleotide sequences of the primer group are shown as SEQ ID NO: 3 and SEQ ID NO: 4. Wherein the size of a strip capable of being amplified by a female individual is 484 bp, and the size of a strip capable of being amplified by a male individual is 199 bp. According to the molecular marker and the primer group provided by the invention, sex identification can be carried out on grassland caterpillars, larvae, incomplete pupae and cocoons with unknown sex, the accuracy is high, the speed is high, and a reliable method is provided for monitoring the field sex ratio of the grassland caterpillars.

The invention aims to disclose an application of a rice salt tolerance related gene OsSWEET15 in regulation and control of salt tolerance. The gene OsSWEET15 is a DNA molecule as shown in 1) or 2): 1) a DNA molecule with a genomic sequence as shown in SEQ ID NO.1; 2) a DNA molecule with a CDS sequence as shown in SEQ ID NO. 2. The invention relates to genetic engineering application of OsSWEET15 in regulation and control of rice salt tolerance, in particular to knockout of the gene OsSWEET15, or reduction of expression quantity of protein encoded by the gene OsSWEET15, or transfer of a knockout vector of the gene OsSWEET15 into rice, and improvement of the rice salt tolerance.

An analysis method and apparatus for immune repertoire sequencing data, and an electronic device. The analysis method comprises: acquiring sequencing data to be analyzed (S102); filtering the sequencing data to be analyzed on the basis of an embryogenic gene combination database to obtain a first filtered sequence (S104), the embryogenic gene combination database being a database constructed on the basis of a recombination result by performing sequence recombination on an embryogenic gene reference sequence; and performing iteration comparison processing on the first filtering sequence for multiple times to obtain a target molecule sequence corresponding to the sequencing data to be analyzed (S106).

The invention discloses a specific molecular sequence and a method for rapidly detecting enterococcus hirai by using the specific molecular sequence. The base sequence of the specific molecular sequence is as shown in SEQ ID No.1-SEQ ID No.26. The invention further discloses a method for rapidly detecting enterococcus hirai by using the specific molecular sequence. The detection method comprises the following steps: designing a specific amplification primer pair according to any one molecular sequence in base sequences SEQ ID NO.1-SEQ ID NO.26; extracting DNA of a sample, and amplifying by a PCR method; and detecting an amplification product by gel electrophoresis, if a corresponding single amplification band appears in an electrophoresis result, judging that the sample contains the target bacteria, otherwise, judging that the sample does not contain the target bacteria. The detection method has the advantages of short detection time, low cost, simplicity in operation, high specificity and the like, can detect the enterococcus hillatus without biochemical identification, and is more practical.

The purpose of this invention is to disclose a rice salt tolerance-related gene ZOS2-02, its encoded protein, and its applications. The gene ZOS2-02 is a DNA molecule as described in either 1) or 2) below: 1) a DNA molecule with a genomic sequence as shown in SEQ ID NO.1; 2) a DNA molecule with a CDS sequence as shown in SEQ ID NO.2; 3) a DNA molecule that hybridizes to the DNA sequence defined in 1) or 2) under stringent conditions and encodes the protein. The genetic engineering application of the gene ZOS2-02 provided by this invention in regulating rice salt tolerance specifically involves knocking out the aforementioned gene ZOS2-02 to improve rice salt tolerance.

The invention relates to a general protein-RNA (Ribonucleic Acid) combination prediction method based on multi-molecular modal fusion, which comprises the following steps: carrying out initial characterization on preprocessed protein and RNA sequences through a generalized biological basic model, and then training a constructed neural network model based on an improved cross attention mechanism; in the online stage, the trained model is adopted to output a predicted value of the combination possibility of the two molecular sequences according to the arbitrarily input protein sequence and RNA molecular sequence. According to the method, a general biological basic model is used for carrying out initial characterization on two molecules of protein and RNA, effective information extraction and fusion are carried out on characterization of the two molecules through an improved cross attention mechanism, and an artificial neural network trained based on the effective fusion information can more accurately predict a protein and RNA binding result; and the method has stronger generalization ability, and can cope with combination prediction scenes and tasks of various proteins.

A computer-implemented method for identifying a genomic variant is provided. The method includes obtaining one or more reference molecular sequences and sequencing data pertaining to a biological sample. One or more candidate variant positions are determined from the sequence reads for the biological sample. A classifier model is applied to each candidate variant position for selecting between a haplotype-aware or haplotype-agnostic variant analysis respectively. The classifier model is trained from haplotype structure and / or sequence reads identified with germline variants in a plurality of regions. Based on the application of the classifier model, respectively applying a haplotype-aware or haplotype-agnostic variant analysis to generate a variant identification for each candidate variant position.

The present application relates to a kind of for nucleic acid amplification, composition or kit and method of capture oligonucleotide.It specifically, the capture oligonucleotide includes successively from 5' end to 3' end: first universal sequence (U2a), folding sequence (1s), second universal sequence (U1a) and binding capture sequence (2a);Wherein, (1) the folding sequence is at least partially identical with the 5' end sequence of target molecule;(2) the binding capture sequence is complementary with the 3' end sequence of target molecule;(3) the capture oligonucleotide further includes nucleic acid extension blocking modification located in the 3' end of binding capture sequence;And (4) the capture oligonucleotide further includes nucleic acid extension blocking modification between folding sequence and second universal sequence, the universal sequence is irrelevant with the sequence of target molecule.The product and method of the present application can realize specific, multiplex nucleic acid detection with high sensitivity.

The present invention relates to capture oligonucleotides, compositions or kits and methods for nucleic acid amplification. Specifically, the capture oligonucleotide sequentially comprises a first universal sequence (U2a), a folding sequence (1s), a second universal sequence (U1a) and a binding capture sequence (2a) from the 5'end to the 3 'end, wherein (1) the folding sequence is at least partially the same as the 5'terminal sequence of the target molecule; (2) the binding capture sequence is complementary to the 3'terminal sequence of the target molecule; (3) the capture oligonucleotide further comprises nucleic acid extension blocking modification located at the 3'end of the binding capture sequence; and (4) the capture oligonucleotide further comprises a nucleic acid extension blocking modification located between the folding sequence and a second universal sequence, and the universal sequence is irrelevant to the target molecule sequence. The product and the method provided by the invention can realize multiple nucleic acid detection with good specificity and high sensitivity.

A system for designing vaccines includes one or more processors, and computer storage storing executable computer instructions in which, when executed by the one or more processers, cause the one or more processors to perform one or more operations. The one or more operations include applying, to a first temporal sequence data set, a plurality of driver models configured to generate output data representing one or more molecular sequences. The one or more operations include, for each of the plurality of driver models, training the driver model. The one or more operations include selecting, based on one or more trained translational responses, a set of trained driver models of the plurality of driver models. The one or more operations include selecting, based on second translational response data, a subset of trained driver models of the set of trained driver models.

The embodiment of the invention provides an object molecule sequence data processing method and a model training method. The object molecule sequence data processing method comprises the following steps: determining at least two object molecule sequence data; at least two pieces of object molecule sequence data are input into the feature extraction model, predicted molecule sequence features of each piece of object molecule sequence data in the at least two pieces of object molecule sequence data are obtained, the predicted molecule sequence features comprise element vectors of molecule elements, and the object molecule sequence data are formed according to the molecule elements; inputting the predicted molecule sequence feature of each object molecule sequence data into a molecule interaction prediction model to obtain an interaction result between the at least two object molecule sequence data, and according to the correlation between element vectors contained in the predicted molecule sequence feature of each object molecule sequence data, obtaining the interaction result between the at least two object molecule sequence data. And outputting an interaction result between the at least two object molecule sequence data. And molecular interaction prediction based on artificial intelligence is realized.

The application discloses a drug interaction prediction method based on graph attention sampling, which firstly acquires drug pair data to be predicted, pre-processes the drug pair data to be predicted, obtains pre-processed drug pair data and drug molecular sequences, converts the pre-processed drug molecular sequences into molecular structure graphs, atomic vector matrices and molecular-atomic relationship lists through a molecular sequence conversion model, constructs an atomic adjacency matrix, a drug interaction network and a drug adjacency matrix, inputs the atomic vector matrix, the atomic adjacency matrix, the molecular-atomic relationship list, the drug interaction network and the drug adjacency matrix into a pre-trained drug interaction prediction model, and obtains a final drug interaction prediction result. The application can solve the technical problem that the existing drug interaction prediction method based on graph representation learning cannot consider both the drug molecular structure characteristics and the relationship information contained in the interaction network.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for visualizing molecular sequences and structures. One of the methods includes receiving, from a sequence-oriented collection, data representing a sequence of molecular components, wherein the data specifies a different respective value for each of the molecular components in the sequence. Data representing an attachment molecule that is to be connected to one of the molecular components in the sequence is received from a structure-oriented collection. A user interface presentation that displays the sequence of molecular components using a different graphical representation for each molecular component of the sequence and that visually distinguishes a particular molecular component to which the attachment molecule is connected is generated.

The purpose of this invention is to disclose a soybean salt tolerance-related gene GmCIPK12, its encoded protein, and its applications. The gene GmCIPK12 is an DNA molecule as described in 1), 2), or 3) below: 1) a DNA molecule with a genomic sequence as shown in SEQ ID NO.1; 2) a DNA molecule with a CDS sequence as shown in SEQ ID NO.2; 3) a DNA molecule that hybridizes to the DNA sequence defined in 1) or 2) under stringent conditions and encodes the aforementioned DNA molecule. The genetic engineering application of the gene GmCIPK12 provided by this invention in regulating soybean salt tolerance specifically involves overexpressing the aforementioned gene GmCIPK12 to improve soybean salt tolerance.

The embodiment of the present application provides a method, device, equipment and product for preparing a multi-target targeted capture probe, which relates to the field of medical biotechnology. On the basis of obtaining the full-length molecular sequence and molecular identifier of the target molecule, M original probe sequences are generated; N connecting arm sequences are obtained; an initial probe sequence is generated based on the M original probe sequences and the N connecting arm sequences; a feature analysis is performed on the initial probe sequence to generate a probe sequence feature; based on the probe sequence feature, at least one base in the initial probe sequence is replaced to generate a target probe sequence; based on the probe application information and the target probe sequence, a multi-target targeted capture probe is prepared; the target molecule is captured by the multi-target targeted capture probe, which solves the problem of spatial arrangement conflict and kinetic competition between the mixed single-target targeted capture probes, that is, solves the problem of low capture efficiency of target molecules caused by the solution of the prior art.

The invention relates to a toadstool bifunctional glycopeptide for targeted immunoregulation and tumor resistance, and belongs to the technical field of biology. The molecular sequence of the morchella glycopeptide provided by the invention is LPS (Fuc) PMLLPQ, and the morchella glycopeptide molecule is obtained by connecting fucose to serine hydroxyl of a peptide chain through an alpha-glucosidic bond in an O-connection glycosylation mode. The morchella glycopeptide is identified from a morchella extract obtained by extracting morchella with pectinase by taking morchella as a substrate, is derived from edible mushrooms and is safe to use; the compound can be effectively combined with TLR4 (Toll-like receptor 4), block inflammatory response and effectively combine and inhibit VEGFR2 (vascular endothelial growth factor receptor-2), and has anti-tumor activity; meanwhile, release of LPS-induced macrophage cytokines can be inhibited, and it is further shown that the morchella glycopeptide has immunoregulation and anti-tumor activity.

A method for analyzing molecules of interest by use of an adaptor attached to the molecule of interest is described, wherein said adaptor comprises a barcode molecule, wherein the barcode molecule is one instance of a set of defined sequences of molecules. The method comprises the steps of: - partitioning the electrical signal derived by electrically measuring at least of the part of a molecule into segments, - temporarily aligning segments of the partitioned electrical signal related to an unknown barcode molecule derived by electrically measuring at least of the part of the molecule of interest comprising the barcode molecule and a set of respective signals for known barcode molecules stored in a data memory, and - predicting the instance of barcode molecule from the similarity between the temporarily aligned segments of the electrical signal and the respective signals for known barcode molecules, wherein the electrical signal is temporarily aligned by compressing or stretching the temporal axis and the instance of barcode molecule is predicted automatically in a data processing unit. The method is also directed to a process of barcode design based on the similarity of temporarily aligned segments of electrical signals related to the candidate barcode molecules and a set of designed target signals.

The invention discloses a biomacromolecule sequence structure prediction method, and relates to the technical field of computers. The attention weights corresponding to different attention types of the processed target information are broadcasted in different broadcast range modes by using a multi-scale attention broadcast mechanism, and the attention weights in different time steps in the broadcast range are repeatedly used, so that the same attention weight is adopted in the steps of the broadcast range, the calculation amount is reduced, and the efficiency is improved. The efficiency performance of the model is improved, and the perception ability of the model to amino acid position information in a protein sequence can be effectively improved, so that the model can fully consider the position relationship between amino acids when calculating the attention weight, and the model can be helped to better identify the interaction of long-chain amino acids; the structure prediction error caused by the missing of the position information is reduced, and the prediction precision is improved.

The invention discloses a drug target binding affinity prediction method based on multi-modal diagram learning driven by a large model. The method comprises the following steps: firstly, inputting protein sequence information and drug small molecule SMILES sequence information which are about to be subjected to drug target binding affinity prediction, wherein the residue number of the protein sequence information is L1 and the length of the protein sequence information is L2; then, protein representation captured by an ESM-GearNet model is used, a pdb file predicted by AlphaFold is combined to jointly construct a protein structure diagram, and word segmentation mapping operation is carried out on a protein sequence; secondly, constructing a drug molecular structure diagram by using an RDKit toolkit, and performing word segmentation mapping operation on the SELFIES sequence which is converted into the SELFIES sequence; thirdly, constructing a neural network of GinConv and CNN to extract and fuse sequences and structural features of proteins and drug molecules; and finally, building a KNN neural network prediction model, and taking the fused features as input to obtain a predicted value of the drug target binding affinity. According to the method, the prediction efficiency and accuracy of the drug target binding affinity are improved.