105 results about "Biological information processing" patented technology

The invention provides a syndrome gene relationship prediction method fusing meta-path semantic dependence and transfer learning, and belongs to the technical field of biological information processing based on deep learning, the method takes a double-flow fine tuning structure in deep transfer learning as a core framework, and two transfer learning tasks are established: 1) a source domain is disease gene prediction; the target domain is syndrome gene prediction; 2) predicting a source domain as a symptom gene; and the target domain is syndrome gene prediction, and through training on a source domain task, migration to a syndrome gene prediction problem of the target domain is carried out, so that syndrome gene relationship prediction in a target domain zero sample scene is realized. In a subject network of transfer learning, through designed syndrome knowledge graph embedding learning, semantic embedding learning of meta-paths and embedding aggregation of multi-order meta-paths, semantic dependency learning of relation meta-paths is realized, and meanwhile, predictive scoring of a relation based on tensor decomposition is designed, so that predictive scoring of syndrome genes is realized.

The invention provides a disease risk characterization prediction system and method, and belongs to the technical field of biological information processing based on deep learning. Comprising an acquisition module used for acquiring a biomarker index of a to-be-detected target object; and the processing module is used for processing the obtained biomarker indexes of the target object to be detected by using a pre-trained disease risk assessment model to obtain a disease risk characterization result of the target object. According to the method, selection of biomarkers is incorporated into the construction process of the fusion model, and the fusion model uses REB as few as possible to represent most of feature information related to the disease; meanwhile, potential regulation effects among different biomarkers are obtained through feature derivation, so that the model obtains more effective characterization information; the fusion model balances the advantages and disadvantages of a plurality of basic learners, can mutually learn inconsistent information from the plurality of basic learners, and has better information extraction capability and stronger robustness.

The invention provides an interpretable machine learning-based giant coronary artery tumor prediction method in Kawasaki disease, and belongs to the technical field of biological information processing. Comprising the following steps: S1, collecting hospitalized KD child patient cases by adopting a retrospective queue research method, and excluding cases which do not meet requirements; s2, coronary artery lesion evaluation and classification; s3, performing variable preprocessing; s4, constructing a machine learning model; s5, performing variable screening; s6, model interpretation; s7, determining an optimal intervention threshold value; and S8, developing an online prediction tool. According to the method, an SVM kernel lab model with the optimal performance is screened out through multi-model comparison, and a group of key prediction molecules are determined in combination with an SHAP method and recursive feature elimination; based on a multi-center data verification model, the model applicability is improved; and early recognition and layered management of high-risk child patients can be realized. A webpage tool is developed, and a patient with high risk is suggested to intervene in advance.

The invention relates to the technical field of protein related data processing, in particular to a biological information processing system and method applied to synthetic biology. The method comprises the following steps: acquiring real-time proteomics data, and performing polymorphic context decoding to obtain a protein expression context matrix; analyzing interaction of behavior characteristics in the protein expression context matrix to obtain a multi-scale causal structure map; executing structure-function transformation rule extraction in the multi-scale causal structure atlas to obtain a function mapping unit set; performing evaluation based on the function mapping unit set so as to form a configuration decision diagram; simulating paths in the configuration decision diagram, and performing path adaptability scoring to obtain a path adaptability feedback table; and optimizing the path adaptability feedback table to obtain an optimal expression configuration set. According to the method, the precision, the stability and the controllability of protein information in the process of structural analysis, function recognition and regulation path construction can be improved.

The invention provides an IVIG drug resistance prediction method in KD based on an interpretable machine learning model, and belongs to the technical field of biological information processing. Comprising the following steps: S1, data acquisition; s2, data processing; s3, constructing a model; s4, a model interpretation module; and S5, webpage deployment. According to the invention, based on the large-scale electronic medical record data of the Kawasaki disease patient, a machine learning model with good interpretability is constructed for predicting the IVIG resistance, and an online webpage calculation tool is established for clinical doctors to input key indexes in real time and automatically output the IVIG resistance risk probability of the patient. According to the tool, 11.6% of the optimal prediction threshold value is set, doctors can be helped to identify high-risk individuals, treatment strategies can be adjusted in time, and coronary artery complications are reduced.

A biological information processing device includes a signal reception unit configured to receive a signal related to biological information reflected from at least one person to be measured, a candidate region identification unit configured to calculate an arrival direction of the signal and / or a distance to the at least one person to be measured from the signal received and identify a candidate region of the at least one person to be measured using the arrival direction and / or the distance calculated, an information generation unit configured to generate biological information corresponding to the candidate region of the at least one person to be measured from the signal received, and a biological information association unit configured to associate the at least one person to be measured with the biological information generated, on the basis of a feature of the biological information generated.

The invention provides a method for accurately and efficiently evaluating gene editing efficiency based on NGS data, a storage medium and electronic equipment, and belongs to the technical field of biological information processing. The method comprises the following steps: splitting NGS mixed pool sequencing data according to a bar code tag sequence, constructing a reference genome index and a dictionary, comparing NGS data of each sample with a reference genome, counting a comparison rate of a comparison result, constructing a comparison result index, converting the comparison result into a tsv format, and evaluating editing efficiency integration of a gene editing target site in each sample. The requirement for accurately and efficiently evaluating the gene editing efficiency based on NGS data is met. According to the method, the limitation of an existing tool on the size of a data file can be effectively avoided, the bar code label sequence can be customized for data splitting, and a high-accuracy comparison result is obtained by utilizing a mainstream comparison algorithm tool; and parallel operation is carried out by utilizing the advantages of the computing cluster, so that the aim of accurately and efficiently evaluating the gene editing efficiency of multiple genes and a large number of samples is fulfilled.

The invention discloses a secure biological privacy information verification system combining random storage and noise disturbance, and relates to the technical field of information verification, comprising a user registration module which collects and verifies biological characteristics of a user through a related identification system, generates basic information and biological information data, and sends the basic information and the biological information data to a storage module; the biological information processing module optimizes a biological characteristic matrix by using a differential privacy technology, then writes the biological characteristic matrix into a block chain by using a random storage strategy and generates encrypted position information, and the position encryption module encrypts the position information by using block index mapping, local disturbance encryption and elliptic curve encryption, and sends the encrypted position information to the block chain; the ID generation module generates a unique user ID in combination with the user personal information and the encrypted position information, the user verification module is used for verifying the information matching when the user logs in, and the information query module carries out safe query on the user information. According to the invention, the privacy of the biological information of the user can be effectively protected, and safe and efficient storage and management of the biological information are realized.

The invention relates to the technical field of biological information processing, in particular to a CIK cell preparation method and system.The CIK cell preparation method comprises the following steps that cell density data of a time sequence is collected, an autocorrelation function value and a partial autocorrelation function value are calculated, a first-order difference sequence and a second-order difference sequence are extracted, ADF statistics and KPSS statistics are calculated according to the difference sequences, and the CIK cell density data is obtained. And comparing the statistical magnitude range to judge the stationarity of the time sequence, and generating stationary cell density sequence data. According to the method, through extraction and optimization of the lag correlation value and the partial correlation value, the accuracy of model parameters is enhanced, reliable support is provided for dynamic correction and conjoint analysis of the future cell proliferation rate, abnormal points are accurately recognized based on analysis of the difference mean value and the weighted accumulation value, the anomaly detection efficiency is remarkably improved, and the method is suitable for large-scale popularization and application. Dynamic adjustment of culture conditions is realized by combining abnormal point indexes and drifting rate calculation of environmental parameters, the influence of environmental fluctuation on cell proliferation quality is reduced, and preparation quality and efficiency are guaranteed.

The invention discloses a lung cancer drug resistance prediction method based on user feature analysis, and relates to the technical field of biological information processing, and the method comprises the steps: calling a biological gene pool, analyzing and mining a gene interaction network based on gene variation statistics of targeted drugs, supervising and training a drug resistance detector based on a self-attention mechanism, receiving user inspection data, and predicting the drug resistance of the lung cancer; and controlling a sequencer to carry out gene sequencing detection, determining a user gene sequence, transmitting the user gene sequence to a drug resistance detector, and determining a variation trend of a targeted fragment so as to analyze drug resistance prediction data under a time sequence. The method and the device are used for solving the technical problem that the accuracy of a prediction effect is limited due to insufficient depth and comprehensiveness of drug resistance analysis in the prior art. Drug resistance analysis is carried out based on variation prediction of the target gene, and the accuracy of drug resistance prediction can be effectively improved.

This invention relates to the field of bioinformatics processing and medical laboratory data analysis, specifically a method for classifying hepatitis B antibody patterns based on plasma protein profiles. The method includes: acquiring host hardware information of the execution environment and extracting central processing unit (CPU) cache parameters; acquiring a high-dimensional sparse one-dimensional array and extracting non-zero feature indices; performing mapping calculations using a locality-sensitive hashing (LSH) algorithm to reconstruct the data into a locally dense two-dimensional matrix; dynamically segmenting the data into independent sub-blocks according to cache parameters and initial segmentation dimensions; performing low-rank tensor decomposition on the independent sub-blocks to extract local latent feature vectors; concatenating the sub-blocks, weighting them through a single-layer attention network, and inputting the result into a classifier function to output a target classification pattern vector; and generating dimension update instructions based on a preset dynamic adjustment mechanism to adjust subsequent segmentation dimensions. This invention achieves lower memory peaks, higher cache hit rates, and stable multi-label pattern output capabilities.

This invention relates to the field of bioinformatics processing and discloses a method and system for mining the structure-activity relationship (SMR) of porcine neurotrophic peptides based on sequence features. The method includes constructing an original sample index table and fusing multi-source production data, extracting peptide sequence features to generate a sequence feature matrix, constructing a sequence-process joint graph containing peptide nodes and process state nodes, training a structure-activity relationship graph neural network to mine SMR relationships, and deriving a process control decision table based on a process response sample set generated by the network, thereby achieving online optimization of the porcine neurotrophic peptide preparation process. This invention solves the problem of SMR mining caused by the separation of process parameters, sequence information, and activity data, achieving accurate characterization of the synergistic effect of sequence and process and reverse optimization of process parameters, thus improving the targeted enrichment efficiency and bioactivity retention level of target neurotrophic peptides.

The invention relates to the technical field of biological information processing and automatic sorting, and discloses a chick gender automatic identification method and system based on multi-modal fusion, and the method comprises the steps: collecting original three-dimensional point cloud data through a three-dimensional form sensing module, and calculating the key region coordinates of chicks; based on the coordinates, controlling the addressable micro sampling array to execute active sampling adaptation, and obtaining measurement response time sequence data of the target volatile matter; acquiring background response time sequence data by utilizing a background channel sensor array; and performing differential processing on the two to generate a differential biochemical response time sequence vector. And then, respectively extracting morphological feature vectors and biochemical fingerprint feature vectors, inputting the morphological feature vectors and the biochemical fingerprint feature vectors into respective independent classifiers to obtain prediction categories and confidence coefficients, finally, judging a bimodal prediction result through preset cross validation gating fusion logic, outputting a final decision, and controlling a sorting execution module to complete physical sorting. And the accuracy of biochemical sampling is improved through active sampling adaptation.

To diversify the environment for evaluating the measurement waveforms of biological parameters acquired from a subject.SOLUTION: The first data D1 corresponding to a measurement waveform of at least one biological parameter of a subject is acquired. The probability that the value of the biological parameter is measured correctly is inferred on the basis of the first data D1. The second data D2 in which the probability is given to a measured value of the biological parameter is generated. The second data D2 given with a probability exceeding a threshold is extracted as the third data D3. A condition sudden change score S representing a sudden change risk of the condition of the subject is determined on the basis of the third data D3. The threshold is changed on the basis of the condition sudden change score S.SELECTED DRAWING: Figure 4

To reduce a load on evaluating persons performing pain evaluation and reduce variations in the evaluation among the evaluating persons.SOLUTION: A biological information processing device includes an acquisition unit for acquiring blood vessel kinetic information indicating a blood circulation state of a subject, and a processing unit for acquiring an index value of pain evaluation for the subject by subjecting the blood vessel kinetic information to normalization processing. The processing unit calculates a reference value on the basis of the blood vessel kinetic information acquired in a reference period before starting a treatment event on the treatment for the subject, calculates an object value on the basis of the blood vessel kinetic information acquired after starting the treatment event, and calculates the ratio of the object value to the reference value as the index value as the normalization processing.SELECTED DRAWING: Figure 2

The invention belongs to the technical field of biological information processing, and provides a cancer typing method and system based on cross-modal attention and variational representation learning, and the method comprises the steps: obtaining the multi-modal medical data of a patient, coding the multi-modal medical data, mapping the multi-modal medical data to a modal specific representation space of a unified dimension, and obtaining the feature representation of a corresponding modal; a multi-head attention mechanism is introduced, for each attention head in the multi-head attention mechanism, an independent mapping space is constructed for each mode, output of the multiple attention heads is spliced on the feature dimension, and through linear transformation, multi-mode fusion representation aggregating cross-mode complementary information is obtained; deducing statistical parameters of potential posteriori distribution represented by multi-modal fusion, and sampling to obtain potential variables; based on the potential variables and the classifier, obtaining a cancer typing result through Softmax; and end-to-end accurate classification is realized.

The invention relates to the technical field of biological information processing, and discloses a target tracking method, device and equipment based on a neurodynamic model, and the method comprises the steps: obtaining the electrical activity data of a target neuron, generating a dynamic differential equation set according to the electrical activity data, carrying out the matrix arrangement of the dynamic differential equation set according to a target size, and obtaining a target neuron; and generating a two-dimensional neural field model, constructing a neurodynamic model based on at least one layer of two-dimensional neural field model, and inputting the to-be-identified data into the neurodynamic model to track a target object meeting a preset speed condition in the to-be-identified data. The problems of target detection and abnormal speed recognition in the field of computer vision are solved according to the biological vision intelligence principle, the parameter space dimension is reduced, training is not needed, the problem that a machine learning model is poor in anti-noise capacity is solved, and interpretability is achieved.

The application relates to the technical field of protein-related data processing, in particular to a biological information processing system and method applied to synthetic biology. The method comprises the following steps: acquiring real-time proteomics data, performing polymorphic context decoding, and obtaining a protein expression context matrix; analyzing the interaction of behavior characteristics in the protein expression context matrix, and obtaining a multi-scale causal structure graph; performing structure-function transformation rule extraction on the multi-scale causal structure graph, and obtaining a function mapping unit set; evaluating the function mapping unit set, thereby forming a configuration decision graph; simulating the path in the configuration decision graph, performing path adaptability scoring, and obtaining a path adaptability feedback table; and optimizing the path adaptability feedback table, thereby obtaining an optimal expression configuration set. The application helps to improve the accuracy, stability and controllability of protein information in the structural analysis, function identification and regulation path construction process.

This application discloses a method, apparatus, storage medium, and electronic device for cross-well identification, relating to the field of bioinformatics. It involves obtaining a first matrix corresponding to each well position in a sequencing chip, translating the first matrix along at least one target direction to obtain at least one second matrix, and comparing each second matrix with the first matrix to determine the sequence similarity between each well position and its adjacent well positions along the corresponding target direction. Based on the sequence similarity, cross-well behavior is identified. Thus, by mapping the sequence data of the chip well positions into a matrix structure and utilizing matrix translation operations to achieve batch alignment of adjacent well positions, this application achieves efficient identification and quantitative evaluation of cross-well behavior, solving the problems of high computational load and low efficiency in traditional point-by-point traversal methods, and improving the accuracy of cross-well behavior identification in sequencing chips.

This invention provides a method, storage medium, and electronic device for accurately and efficiently assessing gene editing efficiency based on NGS data, belonging to the field of bioinformatics processing technology. This invention integrates the following steps: splitting NGS pooled sequencing data according to barcode tag sequences; constructing a reference genome index and dictionary; aligning the NGS data of each sample with the reference genome; calculating the alignment rate; constructing an alignment result index; converting the alignment results to TSV format; and evaluating the editing efficiency of gene editing target sites in each sample. This approach meets the need for accurate and efficient assessment of gene editing efficiency based on NGS data. The method effectively avoids the limitations of existing tools on data file size, allows for custom barcode tag sequences for data splitting, utilizes mainstream alignment algorithms to obtain high-accuracy alignment results, and leverages the advantages of computing clusters for parallel computation, thereby achieving the goal of accurately and efficiently assessing the gene editing efficiency of multiple genes and a large number of samples.

To improve measurement accuracy and responsiveness without limiting the cycle of measurable biological information.SOLUTION: A control unit of a bedside terminal analyzes biological information signals of a plurality of slots having different lengths as analysis targets among biological information signals of a subject acquired from a sensor unit, and generates information on periodicity for each slot. Next, the control unit derives a reliability index indicating reliability of the information on the periodicity for each of the plurality of slots. Then, the controller selects the slot having the highest reliability index, and calculates and outputs the biological information of the subject based on the information relating to the periodicity of the selected slot.SELECTED DRAWING: Figure 4

The invention provides a biological information processing apparatus, an information processing system, a biological information processing method, a storage medium, and a program product. The biological information processing apparatus includes: a biological information acquisition unit that acquires biological information; a biological information using unit that uses the biological information and the features extracted from the biological information; an explicit agreement acquisition unit that acquires agreement of the use of the biological information of the person; a biological information use restriction unit that restricts the use of the biological information on the basis of the agreed value; and an agreement state estimation unit that estimates an agreement state indicating agreement of the use of the biological information of the person on the basis of the acquisition state when the biological information is acquired. Further, a biological information use restriction unit relaxes restrictions on the use of the biological information of the person and features extracted from the biological information on the basis of the agreement state estimated by the agreement state estimation unit.

A biometric information processing method based on biometric recognition, which method is applied to a client, wherein the client runs on a mobile terminal. The method comprises: detecting an instruction for indicating activation of at least two cameras to respectively collect biometric information (S210); in response to the instruction, activating the at least two cameras associated with a mobile terminal, in order to respectively obtain images containing biometric information that are acquired by the cameras (S220); and sending to a server side the images containing biometric information that are acquired by the cameras, such that the server side identifies the biometric information contained in the images to obtain at least two types of biometric information, and associating the at least two types of biometric information with the same user identifier (S230).

The application discloses a mechanism-driven nanobody-antigen binding prediction method and system, relates to the technical field of bio-information processing and artificial intelligence, and inputs amino acid sequences of nanobodies and antigens into a prediction system for processing, and a construction process of the prediction system comprises the following steps: after an encoder encodes the amino acid sequences of the nanobodies and the amino acid sequences of the antigens, global features and local features of respective complementarity determining regions are extracted and fused to obtain nanobody fusion features and antigen fusion features, and the nanobody fusion features and the antigen fusion features are subjected to global average pooling to obtain an encoder feature of the nanobodies and an encoder feature of the antigens; a combination prediction model models the interaction between the encoder feature of the nanobodies and the encoder feature of the antigens through Hadamard product modeling, generates an interaction feature, and predicts a combination probability based on the interaction feature. Through the cooperative work of the mechanism-driven encoder and the prediction system, high-precision and high-efficiency nanobody-antigen binding prediction is realized.

The invention relates to the technical field of biological information processing, in particular to a cattle embryo pre-implantation genetic evaluation method and system based on whole genome sequencing, and the method comprises the following steps: obtaining whole genome sequencing data of a male parent and a female parent, embryo trophoblast live detection sequencing data and culture solution free desoxyribonucleic acid sequencing data; performing variation detection and haplotype phasing on male and female parent data to form haplotype data, and calculating a haplotype transmission posterior at an anchor point based on trophoblast data; performing genome segmentation based on reading depth observation and allele frequency observation of a trophoblast and a culture solution, and performing Bayesian inference by combining haplotype transfer posteriori as priori to obtain posteriori probabilities of aneuploid, copy number variation, chimera, pathogenic homozygosis and pollution events; and outputting a passing / rechecking / elimination conclusion, adding sequencing and updating the posteriori during rechecking until ending, and outputting a final conclusion and a genome breeding value, so that the evaluation reliability and the decision interpretability are improved.

The invention belongs to the technical field of biological information processing, and particularly relates to a method and system for constructing a risk prediction model from Kawasaki disease to huge coronary artery tumor. The invention provides a Kawasaki disease MGCAA risk prediction model construction method and system, and the method comprises the steps: building a prediction model based on six conventional clinical variables through employing a random forest (RF ranger) algorithm; an SHAP explanation mechanism is introduced, so that the contribution of each variable to a prediction result can be visually displayed in global and individual levels, and the transparency and clinical interpretability of the model are improved; through external verification and intercept-only recalibration, the reliability and the applicability of the model in different groups of people can be ensured; according to the method, an online webpage tool (Shiny App) is deployed, and a doctor can input clinical data of a patient in real time and immediately obtain individualized risk prediction and explanation, so that clinical early recognition of an MGCAA high-risk child patient is realized, and formulation of an individualized treatment scheme and early intervention measures is assisted.

The invention relates to the technical field of biological information processing and artificial intelligence, and provides a dual-channel drug interaction prediction method of a positive definite non-commutative polynomial filter, which comprises the following steps: constructing a heterogeneous drug knowledge graph, and constructing a heterogeneous sub-graph by adopting a sampling and random walk strategy; a two-channel graph representation learning model is constructed, semantic embedded drug representation is obtained through a semantic channel, and topological embedded drug representation is obtained through a topological channel by adopting a frequency domain graph convolution method based on a positive definite non-exchangeable polynomial filter; alignment and splicing fusion are carried out through a mutual information alignment mechanism, and fusion features are obtained; nonlinear transformation is carried out on the fusion features through MLP, and prediction vectors are mapped into probability distribution of the drug interaction relation through a softmax function. According to the method, the limitation of traditional spatial domain convolution on high-order semantic capture and heterogeneous structure adaptability is made up, and the calculation efficiency and the structure expression ability of the model are remarkably improved while the prediction precision is ensured.

The present disclosure relates to a de-identification method, a bio-information processing method based on de-identification, and a device thereof. It relates to a technology for protecting visual features within an image, and a de-identification method receives an image including a face region, extracts feature information about the face region from the input image, transforms the extracted feature information to provide guidance for a part to be generated within the face region, and performs de-identification based on the provided guidance.

To reduce the burden on the subject when measuring blood pressure. [Solution] The biological information processing device 1 includes a blood pressure detection unit 13 that identifies a reference pulse wave from pulse waves measured in the past and detects a first reference blood pressure value, a second reference blood pressure value, and a third reference blood pressure value, based on the amplitude of the reference pulse wave; and a blood pressure estimation unit 14 that, when a part of the waveform including the rising edge of a newly measured pulse wave corresponds to a part of the waveform including the rising edge of the reference pulse wave, estimates at least one value or range of the first blood pressure value, second blood pressure value, and third blood pressure value based on the new pulse wave, based on the corresponding value among the first reference blood pressure value, second reference blood pressure value, and third reference blood pressure value.

The present invention relates to the field of biological information processing technology, specifically to a biological data visualization analysis system based on deep learning, the system comprising: collecting each biological data sample and corresponding attribute features, calculating the association strength value between samples, constructing a sample association network structure, quantifying the variation range of sample relationships in the sample association network structure, and generating biological data sample relationship analysis results. In the present invention, a multidimensional association relationship between samples is established by jointly extracting biological sample attributes and time information, and the edge strength calculation and edge variation range quantification processes are introduced in the process of constructing the sample network structure, thereby enhancing the discrimination of sample relationship expression, and cooperating with the progressive structure of the time series to introduce the variation trend of the sample in the time dimension into the feature expression, thereby enhancing the description capability of the sample behavior trajectory, and the recursive hierarchical structure further refines the separation between local features and global characteristics, so that the feature expression has multi-level analysis capabilities.