6158results about "Sequence analysis" patented technology

The invention discloses a benign and malignant nodule grading evaluation system based on large model fusion ultrasonic imaging and thyroid gene markers, which can organically fuse non-invasive examination and serological detection, can simulate and diagnose multi-grade risk probability information provided by a gold standard, realizes similar risk grading estimation in a non-invasive mode, and has a wide application prospect. The thyroid nodule risk assessment method can provide visual explanation conforming to clinical logic based on comprehensive information of iconography and molecular biology, can significantly improve the accuracy of thyroid nodule risk assessment, can also effectively improve clinical decision-making efficiency and patient credibility, and has important clinical application prospects. The system comprises a data acquisition module, an ultrasonic image feature extraction module, a gene marker feature extraction module, a multi-modal fusion and hierarchical reasoning module and a generation module.

This disclosure relates to methods for spatial profiling of analytes present in a biological sample. Also provided are methods for using spatially barcoded substrates to detect a biological analyte in a cell culture, an organism, and organoid. Also provided are methods for using spatially barcoded substrates to detect the temporal profile of a biological analyte.

The invention discloses a drug and target interaction prediction method based on multi-scale convolution feature fusion, which comprises the following steps: acquiring drug molecule data, target protein data and drug and target interaction data, constructing a drug molecule map according to the drug molecule data, and coding a target protein sequence according to the target protein data; inputting the drug molecular map into a model, and obtaining drug features through a multi-scale map convolutional network and a dynamic gating attention mechanism; inputting a target protein sequence into the model, and obtaining target features through hierarchical cavity convolution and a bidirectional gating cycle unit; through multi-head cross attention, the drug features are aligned with the target features, local and global cross-modal fusion is carried out, and drug target fusion features are obtained; based on the drug target fusion features, outputting a drug and target interaction prediction probability; and training the model according to the drug and target interaction data and the prediction probability, and applying the trained model to drug and target interaction prediction.

The invention relates to the technical field of drug research and development, and discloses a drug-like molecule screening system and method based on deep learning, and the system comprises a physical and chemical characteristic analysis module which is used for calculating molecular descriptors of compounds and generating a visual characteristic distribution chart; and the drug-like molecule screening module integrates a Lipinski rule, a Veber rule, a Ghose rule and a Muegge rule, and supports a user to customize a screening rule. Compound characteristics are comprehensively analyzed through the physical and chemical characteristic analysis module, flexible screening is achieved through the drug-like molecule screening module, toxicity risks are accurately recognized through the toxicity assessment module, the affinity assessment module is combined to assess the binding capacity in multiple ways, and the synthesis feasibility assessment module plans an efficient and low-cost synthesis path. The dynamic feedback optimization module continuously improves the system performance; all the modules work cooperatively, the problems of scattered tools, insufficient precision, low planning efficiency, optimization deficiency and the like in the prior art are effectively solved, the medicine research and development efficiency is greatly improved, and the research and development risk and cost are reduced.

The invention discloses a biological hazard big data analysis and monitoring early warning system, and relates to the technical field of public health safety, and an analysis subsystem in the system comprises a core logic module comprising a quality control unit, an error correction unit, an assembly unit and a box separation unit; the unit analysis module comprises a pathogen analysis unit, a resistance gene unit, a virulence evaluation unit and an evolution development unit; the flora integrated analysis module comprises a traceability analysis unit, a mutation characteristic unit, a propagation evolution unit and a transformation management and control unit; the early warning subsystem comprises a risk assessment and early warning system design unit, a dynamic research and propagation analysis unit, an assessment model construction unit, a pathogen evolution and function research unit, a toxicity and propagation risk comprehensive prediction unit, a pathogen risk monitoring network unit and an unknown pathogen and potential risk identification unit. According to the method, the biological hazard data can be comprehensively, efficiently and accurately analyzed.

The invention discloses a spatial domain identification method based on a multi-view weighted fusion GCN network, and the method comprises the steps: taking the gene expression and spatial position of spatial transcriptome data as input, and combining the spatial information based on different similarity measurement modeling with the gene expression after modeling to construct a multi-view weighted fusion graph convolutional network model; and reconstructing a gene expression matrix by using a decoder, capturing global information of ST data, calculating spatial regularization constraint loss by using similarity information and spatial neighbor information, and completing identification of a spatial transcriptome spatial domain. According to the method provided by the scheme, the model can extract the spatial information from the whole situation and the local situation, so that the spatial information is more fully and comprehensively mined, and accurate spatial domain identification is realized.

The invention provides a genetic disease gene detection data analysis method and system based on big data, and relates to the technical field of gene detection data analys.The genetic disease gene detection data analysis method comprises the steps that quality control and duplicate removal are conducted on gene sequencing original data, sequence comparison and variation detection are conducted, and a standard variation detection report is generated; performing multi-scale feature sampling and optimization by using an improved random field diffusion model, inputting high-dimensional feature distribution into a dual self-activation iterative network for feature extraction and integration to obtain a feature mapping matrix and a feature evolution trajectory, and inputting the feature mapping matrix and the feature evolution trajectory into an integrated predictor to obtain an integrated predictor; and in combination with Gaussian mixture process regression analysis and an improved Bayesian reasoning network, establishing a risk association network and training a deep hierarchical decision tree, and outputting a multi-dimensional risk assessment report.

The invention discloses an intestinal cancer metastasis prediction method and device based on spatial omics, a medium and equipment, and the method comprises the steps: collecting original multi-omics data, and carrying out modal alignment and quality control processing to obtain pre-processed multi-omics data comprising second spatial transcriptome data, second single-cell RNA sequencing data and second pathological image data; performing cross-modal semantic embedding on the second spatial transcriptome data based on the second single-cell RNA sequencing data to generate a spatial enhanced expression profile; performing multi-scale graph construction on the second spatial transcriptome data and the second pathological image data, and extracting spatial heterogeneity features; inputting the spatial enhancement expression spectrum and the spatial heterogeneity features into a pre-trained metastasis risk prediction model, and outputting a liver metastasis probability spatial heat map and a key driving feature list; and finally generating a clinical prediction report containing high-risk area positioning. According to the method, through dynamic optimization of spatial resolution and multi-scale feature collaborative modeling, the sensitivity of early transfer detection is remarkably improved.

Described herein are methods such as multi-omic methods for assessing a disease such as cancer. The multi-omic methods may integrate proteomic, transcriptomic, genomic, lipidomic, or metabolomic data. The method screening diseases or disease states. Also described herein are methods for screening for diseases or disease states from biological samples. The methods may include assessing whether a nodule, mass, or cyst is cancerous.

The invention discloses an aquatic organism diversity rapid evaluation system and method based on GIS and eDNA technologies, and the method comprises the steps: selecting a plurality of sampling points in a target region to collect water samples, and integrating and processing the geographic information data of the sampling points and surrounding regions by adopting a GIS system; extracting eDNA from the collected water sample, performing amplification and sequencing on the eDNA by adopting a high-throughput sequencing technology, obtaining species and relative abundance of the species existing at the sampling point, and forming a species list; importing species distribution information corresponding to the species list into a GIS system, and obtaining a species distribution area and species types; according to the method, a species distribution prediction model is established, aquatic organism diversity change trends of the hot spot area and the potential ecological threat area are monitored in real time, an evaluation report and protection suggestions are formed, and protection measures are updated in time, so that the data acquisition and processing efficiency is improved, and the accuracy of species identification and the comprehensiveness of diversity evaluation are enhanced; and a real-time monitoring and early warning mechanism is provided.

The invention provides an UTR (Untranslated Region) element H2202 P1-G as well as a construction method and application thereof, and relates to the technical field of mRNA (messenger ribonucleic acid). According to the present invention, the ribosome load prediction and the secondary structure optimization are performed on the natural 5 'UTR of the HIV TAT 202 gene through the BaidleHelix platform, and the obtained HTAT 202 P1 sequence avoids the inhibitory hairpin structure so as to significantly improve the luciferase expression quantity compared to the natural UTR; an ncRNA sequence without a secondary structure is introduced on the basis of the HTAT 202 P1, translation inhibition of a 5 'cap region is further relieved, and the protein expression quantity of the constructed H2202 P1-G mutant (the DNA sequence of the H2202 P1-G is as shown in SEQ NO 1, and the RNA sequence is as shown in SEQ NO 2) is further improved.

The invention discloses a protein optimization design and screening method and device based on an artificial intelligence algorithm, and the method takes fungal luciferase as an example, and integrates multi-dimensional bioinformatics analysis and deep learning technology to realize efficient protein engineering transformation and optimization. The method comprises the following steps: firstly, identifying a binding domain of luciferase and fluorescein by utilizing an AI-driven molecular docking simulation method, and determining a key conservative site by combining literature, evolutionary analysis and structural prediction; then, generating a protein functional domain skeleton under the constraint of a fixed site by adopting a diffusion model, and performing protein sequence prediction by utilizing a graph neural network model; and finally, scoring and screening the generated sequences to obtain high-stability candidate variants. According to the method, a conservative site dynamic fusion strategy is innovatively constructed, a fixed region is optimized through logic of structure prediction, evolution site intersection priority and literature site union set expansion, the diversity and adaptability of a protein design sequence are met, and the efficiency bottleneck of a traditional scheme is broken through.

The invention discloses an aquaculture organism health state monitoring system based on multi-dimensional feature association, and belongs to the technical field of aquaculture, and the system comprises a visual feature collection layer which is composed of an image collection module, an image preprocessing module, a target analysis module and a feature extraction module; the molecular biology verification layer is composed of an immune gene monitoring module and a metabolic gene monitoring module; the data association prediction layer is composed of a standardization module, a feature screening module, a deep learning module and an evaluation output module; a self-adaptive optimization mechanism, a fault-tolerant mechanism and an environment adaptability adjustment mechanism. According to the aquaculture organism health state monitoring system based on multi-dimensional feature association, intelligent monitoring of the health state of aquaculture organisms is achieved by integrating a computer vision technology and a molecular biology detection method, and a scientific basis is provided for aquaculture production management.

Platforms, systems, and methods for genetic generalization in synthetic biology development. According to one aspect, there is provided a method for predicting performance associated with genetic edits, the method comprising: receiving, by a platform, information about a strain of a microorganism, wherein the information about the strain comprises information describing a plurality of genetic edits to a base strain of the microorganism; generating, by the platform, a set of genetic embeddings based on the information about the strain, wherein the generating comprises processing the information about the strain using one or more embedding models, wherein each of the one or more embedding models: receives the information about the strain of the microorganism as input; and applies computational transformations to the input using a corresponding embedding model to generate a multi-dimensional vector representation for each of the plurality of genetic edits.

Methods for making synthetic gene clusters are described.

The invention discloses a biomedical data analysis method based on adaptive multi-modal data fusion, and belongs to the technical field of multi-modal feature data processing. The method comprises the following steps: acquiring multi-modal data, and preprocessing the multi-modal data; extracting the preprocessed multi-modal data by adopting a modal specific neural network model; performing primary fusion on each extracted modal feature to generate a primary fusion feature; introducing an adaptive fusion module based on an attention mechanism to obtain final fusion features; and performing classification prediction on the final fusion features by using an FCNN model to complete classification of the multi-modal data. According to the invention, through dual mechanisms of primary average fusion and adaptive fusion, high-precision identification is maintained under the condition of multi-modal data missing, and performance reduction caused by incomplete data is avoided; meanwhile, the modal weight is dynamically adjusted through an attention mechanism, the fusion process can be optimized according to the quality and integrity of each modal data, and the feature expression ability is remarkably improved.

The invention discloses an antibacterial peptide recognition method and system based on a sequence-structure dual-channel neural network, and the method comprises the steps: splicing amino acid features and amino acid-level manual features extracted by ProtT5 to obtain peptide embedding, and transmitting the peptide embedding to a sequence channel composed of a plurality of Transform blocks to extract the sequence features of the peptide; predicting a three-dimensional structure of the peptide by using ESM-Fold to construct an adjacency graph, taking amino acid features obtained by ESM-2 as node features of the adjacency graph, and performing layer-by-layer extraction and enhancement by fusing structural channels of multi-head graph attention, a residual network, layer normalization and a feedforward neural network; and carrying out maximum pooling and splicing on the sequence features and the structural features, and then, carrying out antibacterial peptide prediction. According to the method, a multi-feature fusion strategy is adopted, meanwhile, the three-dimensional structure information of the antibacterial peptide is introduced, and the sequence and the structural features are fused through a two-channel architecture, so that the recognition accuracy of the antibacterial peptide is effectively improved.

The invention relates to the technical field of pharmaceutical knowledge maps, in particular to a pharmaceutical knowledge map construction method and system based on artificial intelligence, and the method comprises the following steps: querying and collecting a molecular structure of a drug and a corresponding target protein sequence through a database, and carrying out the numerical coding of the molecular structure data of the drug, molecular fingerprints and protein structural domain features are extracted, and a drug and protein feature set is formed by combining drug chemical attributes and protein sequence features. According to the invention, through accurate analysis of the molecular structure of the drug and the target protein sequence thereof, the innovative scheme significantly enhances the understanding of the interaction of the drug and the protein, so that researchers can directly extract key features from data and monitor the dynamic change of the drug effect, thereby not only accelerating the development process of the drug, but also improving the development efficiency of the drug. By dynamically tracking the interaction between the side effect of the medicine and the pathological characteristics, the scheme provides powerful data support for personalized medical treatment.

The invention discloses a rapid sequencing and traceability analysis system for input infectious diseases, which relates to the field of customs quarantine and comprises a sample preprocessing unit, a high-throughput sequencing unit, a data processing and quality control unit, a rapid comparison and annotation unit and a traceability analysis unit. According to the input infectious disease rapid sequencing and traceability analysis system, a complete link is formed from sample preprocessing, library construction, data cleaning, pathogen recognition and transmission map construction, module splitting and manual intervention are avoided, and tasks can be automatically completed in large-scale and emergency scenes.

The present invention relates to a method for detecting a target analyte in a sample or a method for determining a quantification cycle (Cq) value for a target analyte in a sample. The present invention uses, in detecting a target analyte, an intersection point between a function representing an amplification curve for the target analyte and a first-order function obtained by connecting a first pole of an n-th order differential function or (n+1)-th order differential function for the function representing the amplification curve for the target analyte to a second pole of the (n+1)-th order differential function, thereby having excellent quantitative accuracy and precision in comparison to an existing method in spite of deviations between real-time PCR reaction experiments.

The invention provides an UTR element NHP1 as well as a construction method and application thereof, and relates to the technical field of mRNA. A 5 'UTR with a good expression effect is designed by integrating dominant sequences of a human high-expression gene and a pathogen natural UTR, a chimeric structure NHP1 with high ribosome load is predicted through a calculation model, a DNA sequence of the NHP1 is as shown in SEQ NO 1, and an RNA sequence of the NHP1 is as shown in SEQ NO 2; an EGFP report system is adopted on the DNA level to rapidly screen UTR; the translation efficiency is quantitatively evaluated on the RNA level through luciferase mRNA (N1-methyl pseudouridine modification); and the particle size is controlled by a microfluidic technology, so that the optimized UTR-mRNA is efficiently expressed after being delivered.

The present disclosure provides in some embodiments, a multi -objective binder design framework by aligning autoregressive molecular foundation models (e.g., protein language models (pLMs)) to different objectives, such as binding and developability considerations. In some embodiments, direct preference optimization (DPO) can be utilized in the methods and systems described herein to encode multiple design objectives in the language model through direct optimization on expert curated preference sequence datasets comprising preferred and dispreferred distributions. In some embodiments, utilizing the described framework can enable molecular foundation models (e.g., protein language models), such as ProtGPT2, to effectively design binders conditioned on specified receptors and one or more drug developability criteria. Besides being multi -objective, in some embodiments, the methods and systems provided herein conceive online lab-in-the-loop design pipelines by actively incorporating experimental feedback.

Systems and methods for sequencing polynucleotides using nanopores are disclosed. In some embodiments, a polynucleotide including a single-stranded region and a double-stranded region, in which the single-stranded region is disposed through a nanopore. The polynucleotide can be moved relative to the nanopore by electric forces while one or more structural locks keep the polynucleotide close to the nanopore. A characteristic signal based on nanopore ionic current blockade and associated with the regions of the polynucleotide at or near the nanopore recognition zone is measured and used to infer the nucleobase sequence of the polynucleotide. In some examples, the double-stranded region is extended by a polymerase, and the polymerase is removed from the polynucleotide. In some examples, signals measured under different applied voltages provide nonredundant information regarding the polynucleotide sequence.

The invention discloses a tea tree liquid phase chip and application thereof, and relates to the technical field of molecular detection. The invention discloses a tea tree liquid phase chip and application thereof, according to site screening requirements and probe design principles, the tea tree liquid phase chip comprises 5781 SNP sites, and tea tree resource genetic typing can be realized based on a target interval genome sequence liquid phase capture accurate positioning sequencing typing technology. The tea tree liquid chip can realize low-cost genetic typing, is mainly specific to tea trees, can realize variety identification and genetic relationship analysis of the tea trees, scientifically guides hybridization improvement work of the tea trees and assists protection and development of germplasm resources of the tea trees, and has relatively high application values in multiple fields of tea tree breeding. The tea tree liquid phase chip can be used for tea tree genetic diversity evaluation, germplasm resource and genetic relationship identification, genetic map construction and gene localization, whole genome association analysis and tea tree molecular marker assisted breeding.

The invention relates to the technical field of bioinformatics, and provides a crop whole genome phenotype prediction method and system fused with an environmental indicator gene, and the method comprises the following steps: collecting re-sequencing data, and carrying out genome comparison to obtain variation site data; performing whole genome association analysis by using the variation site data to obtain phenotype association site information; carrying out gene expression quantity measurement on samples of the crop population material in different environments to obtain gene expression quantity data; performing differential expression analysis on the gene expression quantity data to screen environmental indicator genes to obtain an environmental indicator gene set; constructing a phenotype prediction model of double-branch fusion; and predicting a to-be-predicted material through the phenotype prediction model to obtain phenotype prediction results for different environments. According to the method, environmental factors are incorporated into the whole genome selection model, so that the phenotype prediction precision in different environments is improved.

The invention relates to the technical field of computer-aided drug research and development, and provides an antibacterial peptide function recognition optimization method and system based on deep learning and LLM. Systematic innovation is performed in multiple links such as feature extraction, data generation and reasoning verification by introducing a fusion mechanism of a large language model and a deep learning model; and the accuracy and robustness of antibacterial peptide function prediction are obviously improved. On one hand, a strict feature extraction process and a structured cue word template are constructed, sequence information and physicochemical property indexes of the antibacterial peptide and a prediction result of a deep learning model can be effectively integrated, and the prediction result is reasonably corrected and optimized through the reasoning ability and knowledge verification mechanism of a large language model. The misjudgment risk of a traditional deep learning method under the condition of insufficient samples or incomplete features is effectively reduced, and the prediction accuracy and stability are remarkably improved.

The invention provides a topology and biological feature fusion-based virulence gene prediction method, which comprises the following steps of: obtaining a to-be-detected gene; inputting the to-be-detected gene into a trained DAVGAE model, and predicting the correlation degree of the to-be-detected gene and the disease to obtain a disease gene correlation prediction conclusion; wherein the DAVGAE model comprises a data enhancement module, an encoder and an inner product decoder. The problems of data sparsity and heterogeneous data integration in gene-disease association prediction can be effectively solved at least through a DAVGAE model formed by a data enhancement module, an encoder and an inner product decoder.

The invention relates to a method for classifying antihypertensive peptides by fusing sequence and structure multi-modal features and combining contrast-generative combined optimization. The method comprises the following steps: extracting sequence feature representation and structure feature representation of peptide fragments; performing multi-modal feature enhancement of comparison-generative joint optimization on the sequence feature representation and the structural feature representation; and performing peptide classification on the enhanced feature representation by using a preset Kan-Conv structure and tag smooth joint optimization classification model. According to the method, high-precision recognition of the functional activity of the antihypertensive peptide is achieved, information in the three aspects of the sequence, the structure and the generative potential space is creatively and comprehensively utilized, the blank that the structure-sequence synergistic effect is ignored in the existing peptide function prediction field is filled, and the screening efficiency and prediction reliability of the antihypertensive peptide are remarkably improved.

The invention provides an scRNA-seq data clustering method, system and device based on ZINB distribution and graph attention. The scRNA-seq data clustering system mainly comprises three core modules: a ZINB auto-encoder, which is used for modeling scRNA-seq data based on zero-expansion negative binomial distribution, generating robust potential representation through a denoising auto-encoder, and accurately capturing sparsity, excessive discreteness and shedding events of gene expression; the residual image attention auto-encoder is used for constructing a cell relation graph by using a Pearson correlation coefficient, dynamically learning a neighborhood weight in combination with a multi-head attention mechanism, retaining original features through residual connection, and relieving the excessive smoothness problem of image convolution; and a deep clustering model is self-optimized: target distribution and soft label distribution are minimized through KL divergence, and end-to-end joint optimization of embedded learning and clustering is realized.

A sequence of polymer units in a polymer (3), eg. DNA, is estimated from at least one series of measurements related to the polymer, eg. ion current as a function of translocation through a nanopore (1), wherein the value of each measurement is dependent on a k-mer being a group of k polymer units (4). A probabilistic model, especially a hidden Markov model (HMM), is provided, comprising, for a set of possible k-mers: transition weightings representing the chances of transitions from origin k-mers to destination k-mers; and emission weightings in respect of each k-mer that represent the chances of observing given values of measurements for that k-mer. The series of measurements is analysed using an analytical technique, eg. Viterbi decoding, that refers to the model and estimates at least one estimated sequence of polymer units in the polymer based on the likelihood predicted by the model of the series of measurements being produced by sequences of polymer units. In a further embodiment, different voltages are applied across the nanopore during translocation in order to improve the resolution of polymer units.