156 results about "Molecular typing" patented technology

The invention discloses a breast cancer immunohistochemical index and typing prediction method and system. The prediction method comprises the following steps: acquiring a breast ultrasound image and a breast magnetic resonance image of a patient; performing preprocessing and quality control on the mammary gland ultrasonic image and the mammary gland magnetic resonance image; performing breast lesion area segmentation by adopting a deep learning model to obtain segmented lesion areas; based on the segmented lesion area, extracting multi-modal radiomics characteristics of the breast ultrasonic image and the breast magnetic resonance image; fusing the multi-modal radiomics characteristics of the breast ultrasound image and the breast magnetic resonance image, constructing a machine learning model, and performing immunohistochemical index prediction to obtain an immunohistochemical index prediction result; and performing breast cancer molecular typing analysis according to the immunohistochemical index prediction result. By fusing the multi-modal image information, the tumor features can be described more comprehensively, and the accuracy of biomarker prediction is improved.

The invention relates to a lymphoma prognosis prediction method and device based on a PET-CT marker and a medium, and the method comprises the following steps: obtaining a PET / CT image of a patient, inputting the PET / CT image into a lymphoma prognosis prediction model based on image-clinical-molecular typing, and obtaining a prognosis risk category; the lymphoma prognosis prediction model based on image-clinical-molecular typing is constructed according to PET-CT markers, clinical indexes and genetic typing data, the PET-CT markers are obtained by calculating a segmentation result of a PET / CT image of a patient according to a bimodal lesion segmentation network, and prognosis significance verification is performed through a K-M survival curve; the bimodal lesion segmentation network comprises an encoder and a decoder, the encoder performs feature extraction through a plurality of convolutional layers, and a channel smoothing layer is introduced after the last convolutional layer to fuse the extracted features. Compared with the prior art, tumor information can be accurately captured from PET / CT images, and lymphoma prognosis prediction is carried out in combination with genomics and clinical medicine.

The invention provides an EGFR wild-type lung adenocarcinoma prognosis risk assessment method based on multi-omics and machine learning, and the method comprises the steps: obtaining multi-omics and clinical data of lung adenocarcinoma, obtaining a data set, and carrying out the multi-omics consensus clustering, and obtaining a molecular typing result; high-risk subtype specific candidate genes are identified, a candidate prognosis gene set is obtained, multi-algorithm machine learning comparison optimization is carried out, and a modeling strategy is obtained; performing feature screening and model training to obtain a multi-omics feature model so as to calculate an individual risk score of the to-be-tested sample; the individual risk score and the clinical staging information are utilized to obtain a clinical column diagram and a survival prediction result, then the flow of the multi-omics feature model, the individual risk score and the survival result is Web to obtain a clinical system, and a lung adenocarcinoma prognosis risk assessment result is output. The invention can realize an objective, accurate, generalizable and multifunctional prognosis evaluation and treatment guidance tool, and has important clinical application value and wide industrialization prospect.

The invention discloses a nucleic acid aptamer for accurate molecular typing of multi-parameter bladder cancer cells and application of the nucleic acid aptamer, and belongs to the technical field of biology. The technical problem to be solved in the invention is how to realize early accurate diagnosis of bladder cancer and molecular typing of bladder cancer in clinic. In order to solve the technical problem, the invention comprises nucleic acid aptamers including nucleic acid aptamers Seq12a, BCfsw1-3a, BCfsw1-4a, BCfsw1-10a, BCfsw2-19a and BCfsw2-19a, and a composition composed of the nucleic acid aptamers. According to the application, nucleic acid aptamer screening is carried out aiming at different levels of bladder cancer cells and bladder cancer clinical urine samples to obtain nucleic acid aptamer groups covering information of different types of bladder cancer, and cell molecular typing is realized on bladder cancer cells and normal cell lines; and the kit is preliminarily applied to clinical urine sample molecular typing and bladder cancer recurrence course monitoring.

The invention discloses a glioma radiotherapy postoperative risk assessment method based on a magnetic resonance image, particularly relates to the field of glioma radiotherapy patient health risk assessment, and is used for solving the problem that an existing assessment mode depends on manual interpretation and is difficult to predict bad clinical outcomes in advance. The method comprises the following steps: performing clinical data gridding reconstruction on a corticoid use cycle of a patient and tumor molecular typing, and combining morphological characteristics of an edema region in a magnetic resonance image to generate time-aligned clinical comprehensive characteristic vectors; mining a frequent association item set between the comprehensive feature vector and the pathological process to construct a mapping relation model; establishing a probability graph reasoning model of the bad outcome based on the pathological process vector sequence and the probability weight; and integrating the two types of models to form a causal reasoning network, and inputting a target patient feature vector to calculate an accumulated risk value of reaching a bad outcome. According to the method, an interpretable individual risk assessment result can be output, and a basis is provided for postoperative follow-up visit and intervention.

The invention relates to a molecular typing model construction and identification method of BRCA mutation breast cancer. The method comprises the following steps: performing unsupervised clustering analysis on a gene with most significant change in a common transcriptome expression profile of BRCA1 / 2 mutant breast cancer to obtain an optimal clustering number, and dividing the optimal clustering number into subtypes with different clinical prognosis and molecular characteristics of the optimal clustering number; based on the clustering result, a random forest algorithm is utilized to construct a molecular typing model, the model comprises a plurality of key classification genes, and the molecular typing model is verified through a verification set and an external data set so as to ensure the accuracy, the stability and the clinical applicability of the molecular typing model. Molecular typing can be rapidly and accurately carried out according to common transcriptome sequencing data of a BRCA1 / 2 mutant breast cancer patient tumor sample, and a scientific basis is provided for selection of an individualized treatment scheme.

The invention belongs to the technical field of histopathological image auxiliary diagnosis, and particularly relates to a breast cancer molecular typing method and system based on a histopathological image, and the method comprises the steps: obtaining a digital pathological image, carrying out the feature extraction of the digital pathological image, and obtaining the feature embedded representation of the digital pathological image; and inputting the feature embedded representation into a deep learning-based typing model to obtain the probability of each molecular subtype. In a deep learning-based typing model, a long-range dependency relationship is captured through an attention mechanism, a local space structure is extracted by a convolutional neural network module, and effective global and local fusion is further realized through an interaction mechanism. According to the method, the dynamic weighting of the attention mechanism is combined with the residual learning of the convolutional neural network module, so that the modeling capability of the model for a complex tissue mode is effectively improved, and the method has relatively high efficiency in processing high-dimensional spatial heterogeneity data, thereby providing relatively high efficiency and accuracy in triple negative breast cancer molecular subtype classification.

The invention belongs to the technical field of bioinformatics and computational biology, and relates to a random forest classifier and a molecular subtype classifier for esophageal squamous cell carcinoma molecular typing and a construction method. The number of molecular subtypes is four, and the molecular subtypes are respectively ECMS 1, ECMS 2, ECMS 3 and ECMS 4. The method is characterized in that: ECMS 1: when a metabolic pathway is abnormal and NFE2L2 is activated, an NFE2L2 inhibitor is selected as a drug; eCMS 2: tumor classical signal pathway is up-regulated, and methylation degree is low; eCMS 3 has the advantages of few copy number variation events, low tumor mutation load and high PD-1 expression, and is beneficial to immunosuppressor treatment; eCMS 4: the epithelial-mesenchymal transition pathway is activated, the overall survival rate and disease-free survival rate are low, and epithelial-mesenchymal transition and VEFG inhibitor treatment are benefited. According to the invention, heterogeneity of esophageal squamous carcinoma is analyzed, and each typing system is unified, so that molecular typing is realized, and popularization is easier.

The invention relates to the technical field of biology, and particularly discloses a tumor imaging biomarker quantitative analysis system based on multi-mode MRI voxel space coupling and application. According to the application, the spatial coupling relationship between blood brain barrier destruction and perfusion in a tumor can be quantitatively reflected through the voxel overlap region proportion of T1CE and CBF signals, the 1p / 19q state can be efficiently predicted, and accurate prediction of IDH mutation and WHO grading (2-3 grade vs.4 grade) is realized in cooperation with clinical factors, so that the application has a wide application prospect. And an efficient and potential noninvasive imaging biomarker is provided for ADG preoperative molecular typing and grading prediction.

The invention discloses a multi-modal medical image analysis system and method, and the system comprises a data obtaining and feature extraction module which is used for obtaining medical image data and clinical data of a patient, and extracting multi-modal features from the medical image data and the clinical data; the feature fusion and standardization module is used for performing decision-level fusion on the multi-modal features and performing standardization processing on the basis of pre-stored training set statistics so as to eliminate dimensional differences among different features and overcome the limitation of dependence on single-modal data in the prior art; and the multi-task prediction module is internally provided with a well-trained multi-task prediction model, can input the standardized multi-modal feature vectors at the same time, synchronously outputs prediction results of the muscular infiltration depth, histological type and molecular typing of the endometrial cancer, and outputs probability distribution corresponding to each prediction result at the same time. A built-in multi-task prediction model of the system adopts three independent parallel prediction paths, so that the comprehensiveness and the refinement degree of endometrial cancer preoperative evaluation are greatly improved.

The present invention provides a method, device, and application for detecting tumor molecular typing based on low-depth whole-genome sequencing. The method comprises: independently dividing low-depth whole-genome sequencing data into non-fixed bin groups and fixed bin groups; using the copy numbers of the non-fixed bin groups and the fixed bin groups to calculate the average copy number and mutation region ratio of each group; establishing a scoring model based on the average copy number and mutation region ratio to determine the copy number variation type at each structural level of the chromosome, and then deriving the corresponding molecular typing; wherein the non-fixed bin group includes a first target region belonging to the exon and an anti-target region outside the exon; the fixed bin group includes a second target region of 1M in length and a third target region of 10K in length obtained by continuous division. In this way, the copy number variation type of the chromosome at different hierarchical structural positions is obtained, thereby achieving more accurate molecular typing detection of the size of tumor chromosomes at each level.

The present invention discloses a method for screening tumor prognosis molecular markers specifically expressed by individual sample cell subpopulations. First, the gene expression data is analyzed and decomposed to obtain estimates of population cell subpopulation-specific expression; then, by combining it with clinical information, a prognostic evaluation of population cell subpopulation-specific expression is obtained, and cell subpopulations with significant survival differences are further decomposed to obtain estimates of individual sample cell subpopulation-specific expression; finally, by constructing a co-expression module of cell subpopulations, genes with a high degree of connectivity are obtained as tumor prognosis molecular markers for tumor molecular typing and prognosis evaluation. The invention is applied to breast cancer tumors. The tumor prognosis molecular markers obtained by this method are used to type breast cancer. This is a molecular typing of breast cancer at the level of individual cell subpopulation heterogeneity. This typing method is more capable of explaining the changes and development of the disease in a complex biological environment, laying the foundation for the design of targeted drugs.

The application provides a protein composition for predicting the prognosis effect of metastatic hormone-sensitive prostate cancer and application thereof, and belongs to the technical field of prognosis evaluation.The protein composition for predicting the prognosis effect of metastatic hormone-sensitive prostate cancer comprises protein molecules for evaluating drug resistance of a patient and protein molecules for evaluating sensitivity of the patient; the protein molecules for evaluating drug resistance of the patient comprise MCM4, NT5E, LTBP4, IAH1, HP, DOCK2 and EEF1A2; and the protein molecules for evaluating sensitivity of the patient comprise TFF3, FABP5 and LAMP2.The application constructs a molecular typing of mHSPC based on the above protein composition, evaluates the sensitivity of a mHSPC patient to ADT treatment in advance, and thus realizes precise treatment.

The invention discloses a method for constructing astrocytes serving as a smoke disease model, and belongs to the technical field of crossing of stem cells and neuroscience. The method comprises the following steps: S1) reprogramming CD34 + cells in in-vitro PBMCs (peripheral blood mononuclear cells) of smoke disease patients carrying and not carrying RNF213p.R4810K mutation to obtain induced pluripotent stem cells; and S2) directionally inducing and differentiating the induced pluripotent stem cells into astrocytes through a neural progenitor cell way, wherein the obtained astrocytes are the astrocytes capable of being used as the smoke disease model. The astrocyte model prepared by the invention can be used for researching pathogenesis, nerve-blood vessel interaction process and blood-brain barrier (BBB) related functions of smoke diseases, and can be further applied to molecular typing of diseases and in-vitro function evaluation of candidate drugs.

The invention relates to the technical field of biology, and provides a LanCL1 gene as a target for typing and prognosis of glioma. According to the application of the reagent for detecting LanCL1 gene expression in preparation of products for glioma molecular typing and prognosis prediction, molecular typing of glioma patients can be achieved, LanCL1 high expression is highly related to low malignancy and good prognosis, and clinical risk stratification, treatment decision and treatment assistance are facilitated.

The application relates to a high-throughput identification method of lncRNA coding peptides and application thereof, and belongs to the technical field of biology. The method combines a translationomics technology and a protein mass spectrometry technology, takes ribosome footprint sequencing data as a starting point, determines an open reading frame located on an lncRNA gene according to a three-base periodicity rule of a sequencing fragment, further constructs a proteogenomics index after systematic filtration, uses a computational proteomics method to detect an lncRNA translation event, and systematically identifies lncRNA coding peptides on the basis. The method has the characteristics of modularity and strong expandability, can be widely used for various types of proteome data, and can high-throughput identify and quantitatively detect lncRNA coding peptides. The application provides a prediction of molecular typing and prognosis of cancer patients based on an lncRNA coding peptide network model, and provides an important reference basis for precise medication of clinical patients.

The application discloses a molecular typing method of Staphylococcus capitis, and first establishes an operation technology for molecular typing of Staphylococcus capitis by using a multi-locus sequence typing system (MLST). Seven conservative gene fragments are confirmed by screening, all known Staphylococcus capitis genomes are typed based on the combined sequences, ST types and CC types are defined, and a system evolution tree based on core genome SNP differences is compared, so that reliable typing results can be obtained. Seven pairs of PCR primers are designed and verified, and the Staphylococcus capitis can be typed by MLST. The seven conservative gene combinations and amplification primers used in the typing technology are used for the first time, and the PCR reaction system and conditions are optimized by the inventors. The technical process can be used for molecular classification and epidemiological monitoring of Staphylococcus capitis.

The invention provides a 19F streptococcus pneumoniae invasiveness risk prediction method, and relates to the technical field of molecular biology. Wherein the evaluation marker combination comprises a translation genome, a cell synthesis genome and a ligand transport metabolism genome; the translation genome comprises rluB and rsmE; the cell synthetic genome comprises mscL and murI; and the ligand transport metabolism genome comprises czcD, site B, mtsC and pstS. The evaluation marker combination can accurately evaluate the invasive risk of serotype 19F streptococcus pneumoniae, and realizes molecular typing and risk prediction. The kit has high sensitivity and specificity, is suitable for high-throughput detection, and is helpful for clinical diagnosis and pathogenic mechanism research.

The invention belongs to the technical field of breast cancer molecular typing, and more specifically relates to a mammary gland ultrasound video HER2 expression state identification method based on spatial-temporal feature interaction. The method comprises the following steps: taking an original breast ultrasonic video as input data, performing multi-stage data preprocessing operation, and then performing data balance on training data to obtain a standardized training set; sequentially constructing a UniFormerV2 network-based feature module, a multi-stage feature fusion module and a classification module, and training to obtain a trained model; and inputting the test set into the model to obtain a final prediction result of the HER2 expression state. The problems that an existing HER2 detection method is invasive in operation and cannot perform dynamic monitoring, and an existing deep learning method based on static images neglects space-time dynamic information in an ultrasonic video, so that prediction accuracy of the HER2 expression state, especially HER2-Low subtype is insufficient are solved.

The invention relates to a breast cancer tumor blood vessel near-infrared multiple imaging instrument and a preparation method, and the imaging instrument comprises an excitation imaging cooperation system which comprises a light source module, an optical imaging assembly and a detector, and the light source module and the detector are in state matching with a spectrum pair of an excitation wavelength and a receiving wavelength through a closed-loop feedback mechanism; the three-dimensional electric objective table is used for controlling the space positioning and visual field range of a detection area and is linked with the optical imaging assembly to dynamically adjust an imaging focal plane so as to be matched with the blood vessel depth; the map generation module is used for carrying out time-space domain decoupling processing on the time sequence fluorescence signals and respectively extracting blood vessel form distribution, hemodynamic parameters and molecular typing characteristics so as to generate a multi-imaging superposition map; and the surgical navigation interface is used for displaying the superposition map in real time and generating an intraoperative mark track and a boundary resection path based on the superposition map. The imaging instrument disclosed by the invention has high sensitivity, deep penetrability and multi-parameter synchronous analysis capability, and shows remarkable clinical advantages.

The invention relates to the field of clinical molecular typing, and discloses a novel PCOS subphenotype precise diagnosis and treatment platform, which comprises a case sample inclusion and queue construction module; a data acquisition and processing module; the key feature screening module is used for acquiring 12-dimensional feature data of the patient for sub-phenotype clustering; the unsupervised learning module is used for carrying out unsupervised clustering on the 12-dimensional feature data of the cases by adopting a K-Means clustering algorithm and dividing a PCOS patient group into five new subtypes; the XGBoost model construction module is used for constructing an XGBoost model by taking the five new subtypes as classification tags; and the typing prediction module is used for predicting the PCOS subphenotype by adopting the constructed XGBoost model. The invention provides a new set of fine-grained typing and prediction model and a subtype-based precise treatment scheme, and is suitable for subtype distinguishing and treatment guidance of PCOS patients.

The invention relates to the technical field of medicine, and discloses a transcriptomics-based prognosis prediction model for a super-aglaia standard hepatocellular carcinoma liver transplantation recipient. The prognosis prediction model comprises the steps of standardized sample collection and quality control, standardized RNA-seq library construction and sequencing, data preprocessing and quality control, pathway activity feature extraction, molecular typing model construction, prognosis verification and clinical application. The method can deeply understand the heterogeneity and prognosis of liver cancer liver transplantation recipients, the recipients are divided into different subgroups by analyzing gene expression modes in tumor tissues, an important basis is provided for prognosis evaluation of the recipients, key genes with different expression levels in different subgroups can be identified, a more accurate scheme is provided for individualized treatment, and the method is suitable for popularization and application. The RNA-seq-based molecular typing model provides a comprehensive, accurate and individualized method for prognosis evaluation of standard liver cancer liver transplantation recipients of the super-aglaia, and is helpful for improving clinical outcomes of patients.

The invention discloses a group of genes for immunohistochemical typing of BRAF V600E mutant colorectal cancer and application of the genes, relates to the field of cancer typing and genomics, and aims to solve the problems of high price, complex process and poor clinical accessibility of the conventional BRAF V600E mutant colorectal cancer molecular typing method. The genes of the immunohistochemical typing comprise a CD8 gene and a TEM4 gene. The method comprises the following steps: screening mutant colorectal cancer subtype differential genes by using three machine learning algorithms, and taking an intersection of results to obtain a key gene; on the basis of mRNA expression profile sequencing data, 15 candidate genes are determined through ROC analysis; performing immunohistochemical detection and immunohistochemical scoring on the paraffin embedded tissue of the colorectal cancer patient; a cut-off value is determined; and screening out a gene combination with the best distinguishing capability on BM typing by adopting a decision tree algorithm. The BRAF V600E mutant colorectal cancer immunohistochemical typing method is used for BRAF V600E mutant colorectal cancer immunohistochemical typing.

The present invention relates to a breast image classification method and device based on molecular typing auxiliary information, which is used to determine the histological grade of an image to be classified. The method comprises the following steps: obtaining an image to be classified and preprocessing the image to be classified; splitting the preprocessed image to be classified into multiple sequence data, each of which serves as input to different classification models, and fusing the classification results of each classification model to obtain a final classification label; wherein the classification model includes a main network and an auxiliary supervision branch based on molecular typing auxiliary information vectors, the main network includes a multi-scale feature extraction layer, the auxiliary supervision branch adjusts and processes the intermediate output features of the different scale feature extraction layers, and the output results of the main network and the auxiliary supervision branch are weighted and fused to form the classification result of the classification model. Compared with the existing technology, the present invention has the advantages of high accuracy and high efficiency.

The invention provides a bladder cancer subtype classification system and molecular typing method based on machine learning, and the molecular typing method comprises the steps: firstly obtaining transcriptome data and survival information of bladder cancer tissue of a patient, extracting data from a preset amino acid metabolism related gene set, and constructing a gene expression matrix; then, clustering the patients by adopting an unsupervised clustering algorithm, and determining at least two types of amino acid metabolism molecule subtypes in combination with a stability index; thirdly, carrying out survival difference analysis on different subtypes, screening out differential expression genes related to survival outcomes, constructing a survival prediction model based on the differential expression genes, and calculating amino acid metabolism scores of the patients; finally, the patients are grouped according to the scores, and molecular typing based on the amino acid metabolism characteristics is completed. According to the invention, stable and accurate typing of the bladder cancer patient is realized.

This invention provides a method, system, and device for molecular subtyping of retroperitoneal liposarcoma based on biomarkers. The method can classify retroperitoneal liposarcoma into three subtypes based on three genes: KLF6, ECM2, and LMNB2. This invention can lay the foundation for molecular subtyping, prognosis, and targeted drug design of retroperitoneal liposarcoma.

The invention provides a method and device for detecting tumor molecular typing based on low-depth whole genome sequencing and application. The method comprises the following steps: independently dividing low-depth whole genome sequencing data into an unfixed bin group and a fixed bin group; calculating the ratio of the average copy number to the mutation region of each group by using the copy numbers of the unfixed bin group and the fixed bin group; establishing a scoring model according to the ratio of the average copy number to the mutation region, and judging the copy number variation type of each structure level of the chromosome so as to obtain a corresponding molecular subtype; wherein the unfixed bin group comprises a first target area belonging to an exon and an anti-target area except the exon; the fixed bin group comprises a second target area with the length of 1M and a third target area with the length of 10K which are obtained by continuous division. The copy number variation types of the chromosomes at different hierarchical structure positions are obtained, so that relatively accurate molecular typing detection of each hierarchical size of the tumor chromosomes is realized.

The invention discloses an oral lichen planus molecular subtype based on a serum protein marker. The molecular typing is based on a multi-center clinical queue, a new molecular typing is obtained by using transcriptome sequencing data through a Seed-Kmeans semi-supervised clustering algorithm, and a serum marker is further screened out through multiple omics data including the transcriptome sequencing data, proteome sequencing data and serum enzyme-linked immunosorbent assay experimental data. The method can be used for further judging molecular typing. The molecular typing can obviously improve the curative effect of the oral lichen planus medicine, and is beneficial to the realization of OLP individualized precision medical treatment.

The invention relates to the technical field of medical detection, and particularly discloses a biomarker combination for identifying cervical cancer molecular subtypes, a kit and application. The biomarker combination comprises a protein CDH13, a protein TP53BP1, a protein NNMT and a protein HSPB1, and molecular subtype correlation analysis is carried out on a sample by detecting relative expression characteristics of the proteins in a cervical cancer in-vitro sample. Based on the relative expression characteristics of each protein, a cervical cancer sample can be divided into at least one of an epithelial-mesenchymal transition related subtype, a proliferation related subtype, an immune response related subtype and an epithelial differentiation related subtype. The invention also provides a detection kit and an analysis system for realizing cervical cancer molecular subtype identification. According to the technical scheme, the cervical cancer can be subjected to molecular typing from the protein function execution level, a reliable technical means is provided for molecular typing research, prognosis evaluation and accurate treatment related research of the cervical cancer, and the application prospect is good.

The invention provides a kit for molecular typing and prognosis evaluation of salivary gland adenoid cystic carcinoma and application, and relates to the technical field of molecular pathological diagnosis. The invention provides application of a reagent for detecting long arm deletion of a chromosome 6 and / or long arm deletion of a chromosome 14 in preparation of a kit for molecular typing, risk stratification and / or prognosis evaluation of salivary gland adenoid cystic carcinoma. In the application, the defect of traditional pathological typing in prognosis evaluation of adenoid cystic carcinoma can be remarkably overcome by detecting long-arm deletion of the chromosomes 6 and 14, latent patients with morphological seemingly low risk but actually high risk such as sieve subtype can be accurately identified, and the accuracy of risk stratification is greatly improved. As an objective and independent molecular index, the method is helpful for guiding personalized diagnosis and treatment decisions, can be easily converted into a rapid and economic clinical conventional detection means, and has extremely high practical value.