307 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 present disclosure is directed to methods for measuring proteomes of single cells. In one aspect, methods comprise disposing cells on a surface, synthesizing hydrogel chambers around each of the cells, lysing the cells to release intracellular proteins for adsorption onto the surface bounded by the hydrogel chambers, depolymerizing the hydrogel chambers, and quantifying the adsorbed proteins with detection antibodies to determine the single cell proteomes.

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.

Methods for assessing disease, such as multi-omics methods, are described herein. A multi-omics method can integrate proteomic data, transcriptomic data, genomic data, lipidomic data, or metabonomic data. The method screens for a disease or a disease state. Also described herein are methods of screening a disease or a disease state from a biological sample. Multi-omics databases and methods of using them are also described herein.

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 relates to an individual future health state prediction method and device based on proteomics, and a medium. The method comprises the following steps: obtaining and preprocessing proteomics data; constructing a shared network: constructing an individual past and future common disease condition prediction neural network based on a twin network framework; backbone network construction: constructing a health specific outcome prediction neural network based on a multilayer perceptron method; and the health assessment network synthesizes the shared network and the backbone network architecture, carries out fusion and fine tuning on the extracted features in a potential space, and outputs the future risk assessment probability of various health specific outcomes through further training, updating and fine tuning of network parameters. Compared with the prior art, the method focuses on a proteomics data processing and modeling method, takes past and future health estimation as prior information, and realizes individualized health condition evaluation with multiple disease species and death as the outcome.

The invention discloses an application of CLASP2 protein in preparation of a medicine for promoting spinal cord injury repair. According to the invention, a bioinformatics and phosphorylated proteomics technology combined method is utilized to screen out that the functional protein of curcumin for promoting spinal cord injury repair is CLASP2 protein. The molecular biological technique verifies that curcumin regulates and controls microtubule stability by increasing phosphorylation of CLASP2 protein to promote functional repair after spinal cord transection injury. Therefore, the curcumin can regulate and control microtubules by promoting the phosphorylation of the CLASP2 to dynamically promote the motor function repair after the spinal cord injury of the mouse. The method has an important application value.

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.

The invention belongs to the technical field of biological analysis, and particularly relates to a co-extraction method for mRNA and protein in a trace sample and application. The co-extraction method mainly comprises the following steps: capturing messenger RNA by adopting Oligo dT magnetic beads so as to separate the messenger RNA from protein, and then selecting a proteome pretreatment working process based on SP3 magnetic beads. According to the technology, Oligo dT magnetic beads are innovatively selected to separate messenger RNA. The surface of the Oligo dT magnetic bead is modified with an Oligo dT sequence, so that the Oligo dT magnetic bead can selectively capture mRNA. The mRNA separation and purification technology has the characteristics of high efficiency, high purity and simplicity and convenience in operation. The problems that mRNA and protein in a trace sample are difficult to separate, the RNA amount is small, follow-up analysis cannot be met, and the proteomics pretreatment process is low in efficiency can be solved.

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 plasma protein-based stomach cancer early-stage multi-molecular diagnosis model construction method, and aims to solve the problems that the existing stomach cancer early-stage diagnosis standard and minimally invasive or non-invasive method are imperfect, the sensitivity and specificity of the existing diagnosis molecular marker are low and the like, an Olink proteomics PEA technology is utilized and a specific detection panel is selected to construct a stomach cancer early-stage multi-molecular diagnosis model. And identifying the protein markers of gastric cancer, early gastric cancer and gastric precancerous lesions in a discovery queue (n = 88). In a discovery queue, a group of multi-protein molecular marker models consisting of 13 proteins are successfully identified by utilizing an LASSO regression model, and the multi-protein molecular marker models have good diagnostic performance for distinguishing gastric cancer, early gastric cancer and pregastric lesion and contrast in the discovery queue and verification pair. Furthermore, in a verification queue sample, the diagnosis model also shows good diagnosis performance, and the marker model is expected to become a powerful tool for early diagnosis of gastric cancer.

The invention provides a pharmaceutical composition for treating MSS intestinal cancer, and belongs to the technical field of biological medicine, the pharmaceutical composition is used for deeply researching a treatment scheme for effectively inhibiting the MSS intestinal cancer, carrying out basic research from protein purification, proteomics, siRNA libraries and the like, and trying to develop a brand new treatment scheme and a treatment mode using nitixinone, sorafenib and PD1 antibodies; the three medicines are combined for use, so that a remarkable synergistic effect is achieved, and the tumor is successfully changed from cold to hot. The pharmaceutical composition provided by the invention has an important clinical value, breaks through the current difficulty in treatment of MSS intestinal cancer, and opens a brand new direction of application of immunotherapy in clinical advanced metastatic colorectal cancer.

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。