11 results about "Sequence annotation" patented technology

The invention discloses a bidirectional reversible conversion method and system between a peptide molecule SMILES and a sequence expression. The core innovation lies in that a new sequence description syntax is defined to retain information of a polypeptide special bond and specific modification of amino acid; a main chain atom index and adjacency traversal topology identification algorithm is adopted, and end group and topology integrated detection and coding are carried out; a residue recognition algorithm for main chain cutting and template library matching is compatible with any standard or non-standard amino acid residues, an extensible end group library / monomer template library and an automatic increment mechanism, and automatic recognition and sequence annotation of S-S disulfide bonds; the invention relates to a high-fidelity assembly algorithm of HELM anchor points and topology aware cyclic peptide processing. The method solves the problems of incapability of supporting a complex polypeptide topological structure, poor reversibility, insufficient expansibility of a monomer library and the like in the prior art, can be widely applied to scenes of quantitative structure-activity relationship model construction, large-scale polypeptide data cleaning and the like, and has remarkable practicability and innovativeness.

The invention belongs to the technical field of computers, and discloses a script processing method and device, equipment and a readable storage medium, and the method comprises the steps: determining a target code line meeting a preset tracking condition from an original script; inserting an output command for outputting a line number in front of a target code line of the original script to obtain a test script; executing the test script to obtain execution process information, and sequentially extracting line numbers output in the execution process from the execution process information to obtain a line number sequence; and inserting the execution sequence number annotation behind the target code line of the original script by utilizing the line number sequence to obtain a marked script with an execution sequence annotation. According to the method and the device, the marked script with the execution sequence annotation can be obtained, identification, analysis and correction of logic or grammar errors in a program can be assisted, and real and reliable execution sequence information is provided for script debugging.

Provided are a difference quantification comparison method for an adaptive immune system, and the use thereof. The method comprises obtaining at least one of a BCR heavy chain sequence of a B cell, a BCR light chain sequence of the B cell and a TCR β chain sequence of a T cell of a sample to undergo comparison, and sequencing same; comparing the determined sequence with a gene sequence in the IMGT database to obtain sequence annotation information of the corresponding sequence, and further constructing a 3D graph displaying the adaptive immune condition of the sequence; then by means of using a minimum transformation cost method, quantifying an immune state difference between samples to be tested and between a sample to be tested and the database that has undergone the test. The method for quantifying immune differences can be used for evaluating the immune state of biological samples, and can also be used for evaluating influences of various therapy and intervention methods on the immune system, so as to judge the effects of the therapies and interventions, including but not limited to the drug efficacy, the vaccine efficacy, etc.

The present application relates to the technical field of bid evaluation system, in particular to a bid document structured information extraction and intelligent bid evaluation integrated system, which comprises a rule analysis module, a constraint calculation module, a field construction module, an element integration module and a consistency verification module.In the present application, semantic boundary recognition and sequence annotation are performed on the scoring clause text to make the scoring condition expression structured and analyzable, a constraint parameter strength analysis is introduced to form a weight sequence, different conditions are presented with a distinguishing degree in the review, the bid document field comparison is focused on high correlation content, and the element correlation relationship is constructed in combination with the paragraph and chapter position information, the consistency and conflict review is carried out at the bid evaluation element level, the redundant field influence is compressed, the scoring basis integrity and logical coherence are enhanced, the review process expression is clearer, and the result formation path is more checkable and stable.

The present invention relates to a method for ancient Chinese character image recognition and semantic parsing. First, an original image containing irregular deformation and material diversity is obtained from the surface of a cultural relic. Noise and light interference are eliminated through adaptive filtering. The main inclination angle is then calculated based on the distribution of stroke features, and inclination correction is performed to obtain a processed image. A region growing algorithm based on stroke features is then used to separate independent characters, and a deep convolutional network is used to extract feature vectors for recognition. Finally, a conditional random field algorithm is used to optimize sequence annotations based on contextual information from an ancient Chinese character corpus to obtain semantic output. If the result is unsatisfactory, the inclination correction parameters are adjusted retroactively and iteratively optimized to improve overall recognition accuracy. This method can effectively address the problems of irregular deformation and material diversity in ancient Chinese character images, improving recognition accuracy and semantic parsing reliability.

The invention discloses a semantic generation method and device, equipment, a medium and a product. The method comprises the steps that a labeled sentence pattern set corresponding to a text corpus of a vehicle-mounted service is generated through a sequence labeling model; constructing tree structures of all sentence patterns in the labeled sentence pattern set to obtain all tree structures corresponding to the labeled sentence pattern set; and combining all the tree structures based on the editing distance to obtain corpus semantics of the text corpus, and if the corpus semantics do not meet a preset condition, correcting the corpus semantics to obtain corrected corpus semantics. The annotated sentence pattern set corresponding to the text corpus of the vehicle-mounted service is generated through the sequence annotation model, the annotation efficiency is improved, and the sentence patterns are clearly presented by constructing the tree structures of all the sentence patterns in the annotated sentence pattern set, so that all the tree structures are conveniently combined through the editing distance, the combined tree structures are more accurate, and the annotation efficiency is improved. And thus, the obtained corpus semantics are more accurate.

The invention discloses a method and equipment for constructing a bar code gene database for eDNA analysis and a medium, and belongs to the technical field of gene database construction. According to the method, a standardized preprocessing process is deployed for each data source, and a taxonomy mapping system based on same-object different-name analysis and multi-level backtracking inference is combined, so that the sequence annotation success rate and integration efficiency of the heterogeneous data sources are improved, high fault-tolerant analysis of non-standard and incomplete taxonomy names is realized, and data waste is reduced. Meanwhile, taxonomy information synchronous updating and traceability checking are constructed, batch optimization and correction of historical data are supported while the quality of newly added data is guaranteed, and long-term accuracy and scientificity of a database are guaranteed.

The application discloses an application method of a sequence search tool CircBLAST considering gene sequence evolution rearrangement, and belongs to the technical field of bioinformatics. The method flow comprises the following steps: firstly, all protein sequences are cut according to the length of a required word_size, a data set is constructed in combination with sequence annotation data, and is written into a database; then, a request sequence is prepared, and is cut into small fragments of word_size; further, a search matching, construction of a circular sequence and sequence alignment are carried out to complete a retrieval process; finally, an alignment result containing matching fragments, a similarity score and the like information is generated, is used for presenting to a user for viewing and judging the reliability of matching. The application considers the evolution rearrangement of gene sequences, significantly improves the accuracy of sequence alignment, and can find more reordered sequences.

The application discloses a bidirectional reversible conversion method and system between peptide molecules SMILES and sequence expressions, and the core innovation is that: a new sequence description grammar is defined to retain the information of special bonds of polypeptides and specific modifications of amino acids; a topological identification algorithm of main chain atom index and adjacency traversal, end group and topological integration detection and coding; a residue identification algorithm compatible with any standard or non-standard amino acid residue of main chain cutting and template library matching, an expandable end group library / monomer template library and an automatic increment mechanism, automatic identification and sequence annotation of S-S disulfide bond; a high-fidelity assembly algorithm of HELM anchor points and topologically-aware cyclic peptide processing. The application solves the problems that the prior art cannot support complex polypeptide topological structure, has poor reversibility, and has insufficient monomer library expansion, and can be widely applied to quantitative structure-activity relationship model construction, large-scale polypeptide data cleaning and other scenes, and has obvious practicability and innovation.

The present invention discloses a method for entity recognition based on meta-learning. First, meta-training data and meta-testing data are prepared, and a sequence annotation model for entity recognition is constructed. The meta-training data is then input into the model, and the loss value is returned to obtain new model parameters and saved. The meta-testing data and the new model parameters are input into the model, and the adjusted model parameters obtained by returning the loss value are used to update the sequence annotation model parameters to complete a round of training. The training is continued until a preset number of cycles is reached, and a meta-model is obtained. Finally, the trained meta-model is used to train on the target domain. After the training is completed, the final model is obtained, and the final model is used to predict and identify unlabeled sample data in the target domain. The present invention is used for entity recognition with a small number of labeled samples. By training on a source domain corpus with a certain amount of labeled samples, a higher accuracy rate can be achieved when training on a target domain corpus with a small number of labeled samples.

This invention proposes an event argument detection method and system based on label sequence consistency modeling. It mainly includes word sequence semantic encoding, word label sequence annotation, error-prone label sequence generation, and contrastive learning regularization. Word sequence semantic encoding uses BERT and a trained language model to learn semantic representations of preprocessed words, incorporating event type information into the representation vector. Word label sequence annotation uses a fully connected network to predict the probability distribution of the label corresponding to each word. Error-prone label sequence generation generates error-prone label sequences according to a certain strategy based on the probability distribution of the word label sequences. Contrastive learning regularization constructs a regularization loss based on the contrastive learning between error-prone label sequences and correct label sequences, improving the consistency of word sequence labels.