8 results about "Gene classification" patented technology

The application discloses a landscape gene identification method based on machine learning, relates to the technical field of machine learning and data science, and comprises the following steps: constructing a multi-level landscape gene classification architecture, pre-training a machine learning model based on the multi-level landscape gene classification architecture, and obtaining a landscape feature identification model; then, acquiring landscape element data corresponding to a to-be-identified landscape, calling the landscape feature identification model to analyze the landscape element data, and obtaining a landscape index identification result; then, taking the landscape index identification result as the basis, acquiring a first landscape gene identification result by using a feature deconstruction method, acquiring a second landscape gene identification result by using a prototype-variation theory, and acquiring a third landscape gene identification result by using a digital analysis strategy; and finally, taking the three results as a target landscape gene identification result, so that the efficiency and accuracy of landscape gene text identification and extraction are improved, and the application of the landscape gene is facilitated.

To improve estimation accuracy of functional abnormality.SOLUTION: A medical diagnosis support method includes acquiring a medical image of a subject, acquiring variant information including an estimation result of variants of a plurality of genes of the subject by analyzing the medical image, classifying the plurality of genes into one or more co-occurrence groups based on co-occurrence information indicating co-occurrence of variants between genes, determining variants of one or more genes belonging to each of the co-occurrence groups based on the variant information, and estimating a functional abnormality of the subject based on a determination result of the variants of the genes.SELECTED DRAWING: Figure 1

The embodiment of the invention discloses a gene classification method and device based on gene expression data and electronic equipment, and belongs to the technical field of bioinformatics and the field of data processing. The method comprises the following steps: determining expression values of genes in different cell clusters to obtain an expression value data set corresponding to the genes; determining a target expression value threshold value of the gene according to the expression value distribution condition in the expression value data set; and classifying the genes in the cell cluster based on the target expression value threshold. According to the embodiment of the invention, the distribution rule of the gene expression values can be utilized, the objectivity and accuracy of biological classification can be improved, meanwhile, fluctuation of the gene expression level caused by the batch effect can be effectively handled, and the consistency of gene classification is ensured.

This invention provides a species gene classification system, method, apparatus, electronic device, and storage medium, belonging to the field of integrated circuit technology. It includes: a preprocessing module for converting a first serial current signal corresponding to a gene of a species to be tested into a hash vector representing that gene; a content-addressable memory (MAP) for performing vector-matrix multiplication on the hash vectors to obtain multiple first cumulative currents; the MAP is an RRAM array structure, and its conductance is positively correlated with a reference hash vector, where each reference hash vector represents a gene from a species gene pool; and a first comparison module for comparing the various first cumulative currents to determine the species to which the gene belongs. This invention achieves species gene classification through in-memory computation, reducing data movement and eliminating the need for complex algorithms to correct noise, thereby accelerating system operation, reducing sequencing time, and lowering system energy consumption.

The present application relates to the technical field of biomedical detection, and particularly relates to an intelligent transcriptome analysis system based on a maternal-fetal interface biomarker. The system comprises a maternal-fetal RNA low-temperature storage module, a maternal-fetal genome sequencing calculation module, a transcriptome differential expression determination module and a transcription differential expression network construction module, can obtain a maternal-fetal interface biological blood sample set and perform RNA extraction and low-temperature storage to generate a maternal-fetal blood RNA low-temperature sample group; by constructing a maternal-fetal blood RNA sample library and performing single-end sequencing calculation and reference gene mapping screening, a maternal-fetal RNA reference genome is generated; based on the maternal-fetal RNA reference genome, differential expression genes are determined, target gene clustering and enrichment analysis are simultaneously performed, the gene pathway distribution corresponding to each gene classification is obtained, and gene expression network analysis is performed to generate a maternal-fetal target gene expression network. The present application can analyze the gene pathway network between maternal-fetal target genes.

The invention relates to the technical field of biology, and particularly discloses a typing and prognosis prediction model construction method based on HR < + > / HER2-LOW breast cancer gene classification. According to the application, clinical pathological characteristics and biological characteristics of the HR < + > / HER2-low breast cancer are comprehensively analyzed, an internal molecular typing system is established, specific molecular treatment targets of all subtypes are identified, a combined treatment scheme is optimized, and'classified treatment 'is developed to accurately treat the HR < + > / HER2-low breast cancer.

The invention discloses a tumor gene classification method based on a variable precision fuzzy rough set, and relates to the technical field of mining and bioinformatics crossing, and the method comprises the following steps: constructing a variable precision fuzzy rough set model based on a pseudo-overlap function, and defining a fuzzy positive domain and fuzzy dependency degree used for measuring the correlation degree between attributes and decisions; providing a feature selection algorithm based on the model, the rough fuzzy set and the fuzzy dependency degree; a feature selection algorithm is combined with an intelligent classifier to be applied to tumor gene classification; according to the method, the variable-precision fuzzy rough set model based on the pseudo-overlap function is provided, so that the processing capability of fuzzy information is enhanced, the uncertainty of a fuzzy relationship in a discourse domain can be flexibly dealt with, a solid theoretical basis is provided for accurately depicting the association between attributes and decisions, and the adaptability and expression capability of complex fuzzy data are improved.

This paper provides a method for predicting the risk of immune-related adverse events (irAEs) from immune checkpoint inhibitor (ICI) therapy. ICIs have emerged as promising treatments for many cancer types. However, these therapies can induce unpredictable and potentially serious autoimmune toxicities, termed irAEs. The method involves classifying T cell receptor β genes as productive or repair TCR β genes and calculating a tolerant fraction (TF) score. TCR β gene classification indicates the relative presence of non-tolerant T cells, which are more likely to recognize autoantigens after treatment with ICI therapy. The relative presence of tolerant T cells indicates the risk of irAEs.