253 results about "Proteomics" patented technology

Deep learning tools such as convolutional neural networks (CNNs) and transformers have spurred great advancements in computational biology. However, existing methods are constrained architecturally in context length, computational complexity, and model size. This application introduces a sub-quadratic architecture for modeling, which combines projected gated convolutions and structured state spaces to achieve local and global context with, for example, single-nucleotide resolution. These models outperform CNN-, GPT-, BERT-, and long convolution-based models in many tested genomics tasks without pre-training and with 4×-781× fewer parameters. In the proteomics domain, these models similarly outperform pretrained attention-based models, including ESM-1B and TAPE-BERT, on remote homology prediction without pre-training and while using 3,308×-23,636× fewer parameters.

The invention relates to the technical field of biomedicine, in particular to a hepatic cell differentiation degree evaluation method based on multi-omics data, which comprises the steps of sample collection and preprocessing, transcriptomics, proteomics and metabonomics data analysis, multi-omics data integration and modeling and result output. By integrating multi-level biological information, a multi-dimensional scoring model is constructed, the liver cell differentiation state is quantitatively evaluated, and a standardized grading system is provided for clinic. The method can solve the limitation of single omics analysis, improves the evaluation accuracy and reliability, has universality, can be popularized to other malignant tumor research, and assists precise medical development.

The invention provides a gene detection and phenotype verification health detection method and system, and the method comprises the following steps: collecting multi-source data of a user, locking genetic variation highly related to disease and individual specificity through gene sequencing, and building an individualized genetic risk scoring system; wherein the multi-source data comprises gene sequencing and proteomics data of a user; performing gene phenotype coupling analysis, integrating multi-dimensional data of genes, proteins or phenotypes of the user, and constructing a health assessment system with high confidence; the intervention decision generation is used for generating a decision to intervene the user; through gene-phenotype coupling analysis, the disease risk prediction specificity of healthy people is improved, compared with a conventional health monitoring technology, risks are found in advance, the health behavior compliance rate of a user is increased, seamless connection with mainstream wearable equipment and an electronic medical record system is supported, and the utilization rate of data is increased.

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 relates to the technical field of biomedicine, in particular to a method for constructing a plasma ctDNA organ distribution characteristic spectrum of advanced colorectal cancer. The method comprises the following steps: collecting a peripheral blood sample at multiple time points, separating plasma by adopting a double-centrifugal method, and extracting circulating tumor DNA (Deoxyribose Nucleic Acid); carrying out whole exome sequencing based on ctDNA to obtain genome variation information and calculating variation allele frequency, and synchronously detecting the expression quantity of immune-related proteins by adopting an Olink proteomics technology; integrating the genome data, the protein expression data and the clinical pathological features, and constructing a multi-dimensional feature data matrix; and taking the organ metastasis condition confirmed by iconography as a supervision label, training a model by applying a machine learning algorithm, screening key prediction factors, constructing a quantitative prediction model, and finally generating a visual organ metastasis tendency prediction map. According to the method, early and accurate prediction of the advanced colorectal cancer organ metastasis tendency is realized through multi-omics data collaborative analysis and machine learning modeling.

Techniques are disclosed herein that encompass image pre-processing and a semi-supervised clustering for optimization and analysis of immune-enriched single-cell proteomics data generated via multiplexed imaging technologies. This is achieved through an image pre-processing pipeline, which converts image data contained in one type of file (e.g., .mcd) into another type of file (e.g., .tiff) and removes artifact signals from the image data using various algorithms to generate improved image data. Thereafter, a semi-supervised clustering pipeline analyzes the improved image data using various techniques, including implementing a supervised algorithm to identify metaclusters such as general immune phenotypes (e.g., CD4−T-cells, Macrophages, Neutrophils, etc.) as well as non-immune phenotypes while implementing an unsupervised algorithm that enables the identification of specific subclusters and a more in-depth cellular status characterization.

The invention relates to the technical field of bioinformatics, in particular to a method for typing acute respiratory distress syndrome biological subtypes. The method comprises the following steps: a) acquiring multi-omics data and carrying out standardized preprocessing; the multi-omics data comprises transcriptomics data, proteomics data and metabonomics data of a biological sample source; b) constructing a similarity network of each group by using a similarity fusion network (SNF), and obtaining a uniform sample similarity matrix through multi-group network fusion and iteration; multiple collaborative principal component analysis (MCIA) is adopted to carry out dimension reduction on multi-omics data so as to realize visualization of a clustering result; carrying out multi-omics joint discrimination modeling under the guidance of SNF clustering by using a data integration analysis (DIABLO) method so as to identify key feature variables; and carrying out biological subtype classification based on the clustering result of the steps.

The present disclosure relates to proteomics, including the detection of immune cell target proteins as examples that the disclosed methods can be used for protein detection. Methods, compositions, and systems are described for identifying the presence of proteins, including detecting immune cell proteins from a sample using a combination of multiplex immuno-PCR with antibodies conjugated to oligonucleotides comprising an antibody-specific barcode flanked by PCR priming sites; detection of barcoded amplicons obtained from immuno-PCR with encoded padlock probes comprising codes specific for the barcodes / target protein; rolling circle amplification (RCA) of the circularized padlock probes; and determination of the sequence of the codes amplified by RCA by next generation sequencing or using fluorescently labeled hybridization probes.

The invention discloses an abdominal aortic aneurysm risk screening method based on HbA1c multiple risk markers, and aims to solve the problems that AAA screening in the prior art depends on a single marker, is lack of nonlinear analysis and is insufficient in application tool, a comprehensive risk prediction model is constructed by integrating HbA1c, proteomics data and non-genetic factors, a nonlinear risk relationship is revealed, and the risk of the abdominal aortic aneurysm is predicted to be the risk of the abdominal aortic aneurysm of the abdominal aortic aneurysm of the abdominal aortic aneurysm. And an application tool is developed, visual display of the morbidity probability and the risk level of n years is provided, a personalized screening strategy is supported, and the accuracy and practicability of AAA early screening are improved.

The invention relates to the technical field of tumor markers, in particular to a pancreatic cancer early screening marker combination based on blood protein and application of the pancreatic cancer early screening marker combination. Through proteomics analysis and bioinformatics screening, 12 kinds of proteins such as NME3, HPGDS, CEACAM5, TNFSF12, SCG3, VTCN1, GFRA1, KRT19, TMPRSS12, NELL2, KAZALD and MMP12 are determined to serve as the potential early screening markers of the pancreatic cancer. Researches show that the combination of the proteins can effectively distinguish pancreatic cancer patients from non-pancreatic cancer patients, and has good sensitivity and specificity. A screening model constructed based on the protein combination can be applied to auxiliary diagnosis, risk assessment and population screening of pancreatic cancer, and has important clinical transformation value.

The invention discloses a method for annotating a vesicle cell subset based on monovesicle membrane proteomics, and belongs to the technical field of vesicle cell annotation. The method comprises the following steps: 1, carrying out exosome collection on a plasma sample to obtain a sample; 2, coding and labeling the antibody probe, and adding a protein tag and a labeled DNA sequence into the antibody probe to obtain a labeled antibody probe; 3, preparing a corresponding combined product; 4, adding a labeled antibody probe into the sample to obtain an exosome compound; step 5, capturing an exosome conjugate through CTB; 6, adding the binding product into the exosome conjugate to generate a sequence; 7, constructing a sequence library and sequencing to obtain a sequencing sequence; 8, removing a sequencing sequence with low expression quantity to obtain an exosome expression data matrix, and standardizing the exosome expression data matrix; 9, carrying out clustering analysis to find out an exosome core subgroup; and step 10, determining the source of the exosome core subgroup, and annotating the single vesicle subgroup.

The invention discloses a kit and a method for detecting pepsin based on polypeptide, and belongs to the technical field of proteomics. The amino acid sequence of the polypeptide is as shown in SEQ ID No. 1. The polypeptide is obtained based on enzymolysis of pepsinogen PGA4 and screening, the polypeptide and isotope labeled polypeptide are utilized to establish a standard curve, a biological sample is further subjected to enzymolysis to obtain a peptide fragment sample, and pepsin in the biological sample can be quantified by detecting the content of the polypeptide in the peptide fragment sample. By utilizing the method disclosed by the invention, the pepsin in the biological sample can be rapidly quantified, so that pathological reflux and physiological reflux are diagnosed and distinguished, and the accuracy rate is high.

The invention belongs to the technical field of biology, and relates to a proteomics-based adenoid hypertrophy grading diagnosis biomarker and application thereof. Comprising BET1, RPS6KA4, HSPA1A, DPP4, OSTC, MRPL21, CMC4, RPL37, PSPH, PTPRS, MFAP5, KIAA1143, EMC4, ZNF460, ABHD13, SH3BP5L, GPC3, DNAJC19, DTD1, H3-7, MKNK1, MFAP2, WRAP73, IGLV4-69, P4HA1, S100A9, QSOX2, RNF25, RMND5A and MRPS28. The invention further discloses a preparation method of the high-efficiency and high-efficiency biomedical kit. The screened biomarker can accurately diagnose the severity of adenoid hypertrophy in a non-large-area invasive manner, and especially can accurately distinguish severe adenoid hypertrophy from moderate adenoid hypertrophy.

The invention belongs to the technical field of in-vitro diagnostic reagents, and particularly relates to application of a detection reagent for GDF15 protein in extracellular vesicles in preparation of a prostate cancer diagnosis and / or staging kit. According to the invention, enrichment and proteomics detection are carried out on plasma EVs of patients with prostatic cancer (PCa) and benign prostatic hyperplasia (BPH), and through bioinformatics analysis, a potential EV protein marker GDF15 which can be used for early diagnosis of PCa is screened for the first time. The GDF15 protein is verified in a plurality of groups of new clinical queues by further adopting an immunological technology and a targeted quantitative mass spectrometry technology, and the distinguishing ability of the GDF15 protein to PCa and BPH and the distinguishing ability of the GDF15 protein to different PCa stages are determined. The method is expected to promote noninvasive precise diagnosis of clinical PCa, and has a good application prospect.

A diagnostic platform for detecting and analyzing brain tumors includes a portable fiber Surface Enhanced Raman spectroscopy (SERS) module, a removable fiber probe for intraoperative use, disposable nanoplasmonic cartridges for individual sample loading, and an updatable spectral library collected from healthy and diseased samples. The platform is designed to analyze blood and tissue signatures and employ artificial intelligence (Al) models to classify SERS spectra based on an updatable proteomics and SERS spectral library encompassing distinct brain tumor types. The platform is further configured to utilize artificial intelligence (Al) algorithms for the analysis and classification of the recorded SERS spectra.

The invention belongs to the technical field of biological materials and biological information, and provides a space phosphorylation modification omics detection method. According to the detection method disclosed by the invention, proteomics analysis can be carried out on trace sample phosphorylation, 4259 phosphorylation sites can be identified by 5 micrograms of peptide fragments, and the credibility of 3506 sites is greater than 0.75.

The invention discloses a multi-layer heterogeneous network unicellular organism network inference method based on meta-path enhancement, which mainly comprises a gene regulation knowledge base enhanced multi-layer heterogeneous network construction module for integrating an external gene interaction network and multiple omics data such as scRNA-seq, scATAC-seq, ST and the like; constructing a single-cell multi-omics multilayer heterogeneous network containing cell-cell, cell-gene and gene-gene relationships, and fusing spatial constraints to consider cell positions and tissue structures; and the feature enhancement module based on the meta-path explores complex semantics of the network by designing a multi-hop meta-path mode, designs an adaptive multi-view learning framework and a multi-round enhancement mechanism, and optimizes feature representation by using cell-gene interaction and cross-modal attention fusion. The unicellular biological network can be effectively deduced, the deduction accuracy and biological interpretation are remarkably improved, the method plays an important role in understanding the cell biological process, developing and treating diseases and the like, has good expandability, and can further integrate multi-modal omics data such as proteomics and metabonomics.

The invention discloses a disease-related anomaly localization protein prediction method based on deep learning, and the method comprises the steps: carrying out the protein prediction based on the proteomics expression data of normal and disease samples and a known protein interaction network in a normal state; respectively constructing a protein interaction network under the activity characteristics of the sample pathway and the disease state; the proteomics expression data and the pathway activity characteristics are fused through a cross attention mechanism, and protein characterization characteristics with pathway perception ability are constructed; respectively predicting protein subcellular localization under the normal and disease states by using a graph attention network model based on the protein characterization characteristics and the protein interaction network corresponding to the normal and disease states; disease-related abnormal localization proteins are identified by comparing predicted protein subcellular localization in normal and disease states. The method can efficiently and accurately identify the abnormal localization protein related to the disease, and has important scientific research value and application prospect.

The invention discloses an AKI, CKD and AKI-CKD progress general prediction model based on plasma proteomics and a construction method thereof. The construction method comprises the following steps: selecting a patient which has plasma proteomics data when a baseline which meets the inclusion standard of a research group, has eGFR greater than or equal to 60mL / min / 1.73 m < 2 > when the baseline exists, and has no AKI or CKD medical history; collecting clinical prediction factors and blood proteomics data of the modeling crowd; and randomly dividing the selected modeling crowd data into a training set and a verification set, screening out protein prediction markers such as protein WFDC2 and protein GDF15 shared with the development of the AKI, the CKD and the AKI-CKD, and establishing a general prediction model for the development of the AKI, the CKD and the AKI-CKD. Proteomics is used for predicting AKI-CKD progress for the first time, three renal function outcomes can be predicted at the same time, prediction indexes are simple and easy to obtain, and the model is a universal model with high prediction capacity.

The invention discloses a breast milk fat globule membrane protein simulation degree evaluation method based on multi-dimensional similarity weighted fusion, and the method comprises the steps: constructing a breast milk MFGM proteomics database, and obtaining the proteomics data of a to-be-evaluated sample; the method comprises the following steps: obtaining a reference matrix through row screening and column screening based on a breast milk MFGM proteomics database, performing logarithmic transformation on the reference matrix, calculating a mean value and a standard deviation of each feature in the reference matrix, and constructing a Z-score matrix and a missing mask matrix corresponding to the reference matrix; calculating abundance distribution similarity and missing mode similarity of the to-be-evaluated sample; setting a shrinkage coefficient based on the actual overlapping feature number, screening a core protein set according to a set threshold value, and comparing to obtain the missing MFGM protein type of the to-be-detected sample; and constructing a Top-K neighbor center, calculating the feature deviation contribution and abundance difference of the to-be-evaluated sample, and outputting key defect proteins and formula optimization suggestions according to preset feature importance indexes. And the similarity simulation accuracy is improved.

The invention provides a biomarker for predicting the recurrence risk of papillary thyroid carcinoma and application of the biomarker, and a proteomics method is utilized to analyze proteins with significant abundance level difference in blood of two groups of people with papillary thyroid carcinoma recurrence and non-recurrence after papillary thyroid carcinoma patients are subjected to operative treatment, so that the papillary thyroid carcinoma recurrence risk can be predicted, and the papillary thyroid carcinoma recurrence risk can be predicted. According to the method, a biomarker capable of being used for predicting the papillary thyroid carcinoma recurrence risk is screened out, and a multi-marker joint detection model is further constructed, so that the papillary thyroid carcinoma recurrence risk can be accurately, noninvasively and efficiently predicted, and clinical requirements are met.

本发明实施例公开了一种基于蛋白质组学的脓毒症休克患者液体管理策略预测方法。所述方法包括：收集脓毒症休克患者的血浆样本，检测蛋白质标志物的表达水平，所述蛋白质标志物为GAL、NFASC、MICB_MICA、MAP2K6、JCHAIN和CSF3的组合；基于亚组识别模型计算效益得分，获得脓毒症休克患者的液体管理策略结果。本发明通过收集脓毒症休克患者的临床数据和蛋白质表达数据，采用LASSO回归分析，构建基于上述蛋白质标志物的亚组识别模型，进而获得效益评分，基于效益评分能预测患者液体管理策略方案的适用性，为脓毒症休克患者的液体治疗提供了可靠的方案，实现个体化和精准的液体治疗，在临床具有较好的应用前景。

The invention belongs to the technical field of biological detection and analysis, and particularly relates to a high-fidelity biotinylation probe as well as a preparation method and application thereof in polysulfide detection. Specifically, the invention develops a novel biotinylation probe HPB, and the probe is formed by coupling HPE-IAM and biotin. Experimental results show that HPB is superior to IAB and other traditional reagents in the aspects of maintaining and efficiently labeling polysulfide, and a high-specificity solution is provided for Pr-SnH analysis at the proteomics level. By optimizing a marking scheme and introducing a precipitation-redissolution step, the detection accuracy of the polysulfide modified protein is further improved, so that a reliable tool is provided for protein polysulfide modification detection research under a complex cell background, and the method has a good practical application value.

The invention discloses a method for synthesizing millions of unique DNA tags through primer combination and application of the method, and relates to the field of molecular biology. According to the method, partial complementary pairing characteristics of F, X and Y primers are utilized, and under the synergistic effect of T4 DNA polymerase and ligase, millions of unique DNA tags are generated through one-time reaction through annealing-filling-ligation three-step reaction. The label structure comprises 5'end functional modification sites (such as amino and biotin) and a customizable sequencing joint, and is suitable for high-throughput labeling scenes such as single-cell multiomics, space proteomics and antibody coupling (AOC). Compared with a traditional one-by-one synthesis method, the method has the advantages that the single-tag synthesis cost is reduced by 80% or above, a reaction system is compatible with automatic operation, the tag combination complexity can reach 5.6 * 10 (based on 384 * 384 * 384 primer combination), and an efficient solution is provided for large-scale molecular marking.

The invention discloses an application of a reagent for detecting AP3S2 in preparation of a diagnostic reagent for neoadjuvant immunochemotherapy resistance of gastric cancer in a local progression stage, which comprises the following steps: screening differential expression proteins in neoadjuvant immunochemotherapy resistance tissues of gastric cancer in the local progression stage through proteomics; a new marker is provided for diagnosis of the local progression stage gastric cancer immunochemotherapy drug resistance, and a basis is also provided for development and future development of a local progression stage gastric cancer immunochemotherapy drug resistance diagnosis method.

The present application relates to a kind of individual future health state prediction method, device and medium based on proteomics, wherein the method comprises the following steps: obtaining proteomics data and preprocessing;Shared network construction: construct individual past and future comorbidity condition prediction neural network based on twin network framework;Trunk network construction: construct health-specific outcome prediction neural network based on multilayer perceptron method;Health assessment network integrates the shared network and trunk network architecture, and extracts features thereof to fuse and fine-tune in latent space, updates fine-tuning network parameters by further training, and outputs the risk assessment probability of multiple health-specific outcomes in the future.Compared with prior art, the present application focuses on proteomics data processing and modeling method, uses past and future health estimates as prior information, and realizes individual health condition assessment with multiple diseases and death as outcome.

The application discloses a marker for determining the severity of IgAN kidney tissue lesions and application thereof, and inventors find that ACTN4, ACADS and COL1A1 are significantly differentially expressed proteins in kidney tissues. The significant down-regulation of ACTN4 may cause the change of actin cytoskeleton of IgAN glomerular podocytes; the significant down-regulation of ACAD may indirectly participate in the occurrence process of abnormal structure and function of IgAN renal tubular epithelial cells by affecting fatty acid metabolism in the cells; and the significant up-regulation of COL1A1 may participate in the accumulation of extracellular matrix in the renal interstitium of IgAN, and play a certain role in promoting the renal interstitial fibrosis. The combination of ACTN4, ACADS and COL1A1 has good prediction efficiency for the diagnosis of the severity of IgAN kidney tissue lesions, and the area under the ROC curve is 0.815, P 0.043. In addition, the immunohistochemical results also confirm the expression trend of the three proteins ACTN4, ACADS and COL1A1 in the corresponding kidney tissue substructures in proteomics.

A method and system for diagnosing a subject with respect to a nasopharyngeal carcinoma (NPC) disease state. Peptide structure data corresponding to a biological sample obtained from the subject is received. The peptide structure data is analyzed using a supervised machine learning model to generate a disease indicator that indicates whether biological sample evidences the NPC disease state based on at least 3 peptide structures selected from a group of peptide structures identified in Table 1A and / or 1B. The group of peptide structures in Table 1A and / or 1B comprises a group of peptide structures associated with the NPC disease state. The group of peptide structures is listed in Table 1A and / or 1B with respect to relative significance to the disease indicator. A diagnosis output is generated based on the disease indicator.

The invention relates to the technical field of forest tree hybridization, and discloses a forest tree hybridization parent precise matching method based on multi-omics analysis, which comprises the following steps: S1, obtaining multi-omics data: performing genome sequencing, transcriptome analysis, proteomics analysis and metabonomics analysis on forest tree population individuals; a plurality of omics data such as genetic variation sites, gene expression quantity, protein expression abundance and metabolite spectrums are obtained. According to the forest tree cross parent accurate matching method based on multi-omics analysis, forest tree genetic characteristics are analyzed comprehensively through multi-omics data, genomics, transcriptomics, proteomics and metabonomics data are deeply fused, genetic factors closely associated with target traits are accurately identified, and the accuracy of forest tree cross parent matching is improved. According to the method, the scientificity of parent matching in forest tree cross breeding on the molecular level is remarkably improved, the fuzziness and uncertainty of traditional judgment only according to phenotype and experience are abandoned from the source, the parent matching accuracy is greatly improved, and the breeding work is more targeted and efficient.

The invention belongs to the technical field of proteomics detection, and particularly relates to a method for detecting mouse liver neomembrane proteome based on an orthogonal translation system and a mouse strain containing an SORT-KASM module. Comprising the following steps: (S.1) constructing a conditionally expressed SORT-KASM transgenic mouse strain; (S.2) activating the expression of the SORT-KAS M by a tissue specific Cre system; (S.3) giving unnatural amino acid ingestion to the mouse; and (S.4) carrying out biotin labeling coupling on non-natural amino acids in the proteome through an azide-alkyne click reaction. According to the detection method, the space and time high resolution of the liver tissue is achieved, the membrane protein has the high-coverage marking capacity, the low-abundance newborn protein has the high-sensitivity marking capacity, good stability is achieved, and the membrane protein participates in the important physiological processes such as substance transfer, energy metabolism and steady state maintaining; the innovation of the detection method provides a new thought for researching the liver cell membrane proteome.