Kulcsár et al., 2021 - Google Patents
Development of machine learning based colorectal cancer subtype classificatorKulcsár et al., 2021
- Document ID
- 4879494535416570867
- Author
- Kulcsár F
- Békevári D
- Eigner G
- Drexler D
- Patai
- Micsik T
- Fleiner R
- Publication year
- Publication venue
- 2021 IEEE 21st International Symposium on Computational Intelligence and Informatics (CINTI)
External Links
Snippet
The 4 Consensus Molecular Subtypes (CMS1-4) determined by the Colorectal Cancer subtyping Consortium (CRCSC) could have been identified by high-priced methods so far. This study aimed at building a model which can reliably classify patients into the same …
- 201000011231 colorectal cancer 0 title abstract description 13
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F19/00—Digital computing or data processing equipment or methods, specially adapted for specific applications
- G06F19/30—Medical informatics, i.e. computer-based analysis or dissemination of patient or disease data
- G06F19/34—Computer-assisted medical diagnosis or treatment, e.g. computerised prescription or delivery of medication or diets, computerised local control of medical devices, medical expert systems or telemedicine
- G06F19/345—Medical expert systems, neural networks or other automated diagnosis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F19/00—Digital computing or data processing equipment or methods, specially adapted for specific applications
- G06F19/10—Bioinformatics, i.e. methods or systems for genetic or protein-related data processing in computational molecular biology
- G06F19/20—Bioinformatics, i.e. methods or systems for genetic or protein-related data processing in computational molecular biology for hybridisation or gene expression, e.g. microarrays, sequencing by hybridisation, normalisation, profiling, noise correction models, expression ratio estimation, probe design or probe optimisation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F19/00—Digital computing or data processing equipment or methods, specially adapted for specific applications
- G06F19/10—Bioinformatics, i.e. methods or systems for genetic or protein-related data processing in computational molecular biology
- G06F19/28—Bioinformatics, i.e. methods or systems for genetic or protein-related data processing in computational molecular biology for programming tools or database systems, e.g. ontologies, heterogeneous data integration, data warehousing or computing architectures
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F19/00—Digital computing or data processing equipment or methods, specially adapted for specific applications
- G06F19/10—Bioinformatics, i.e. methods or systems for genetic or protein-related data processing in computational molecular biology
- G06F19/24—Bioinformatics, i.e. methods or systems for genetic or protein-related data processing in computational molecular biology for machine learning, data mining or biostatistics, e.g. pattern finding, knowledge discovery, rule extraction, correlation, clustering or classification
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F19/00—Digital computing or data processing equipment or methods, specially adapted for specific applications
- G06F19/30—Medical informatics, i.e. computer-based analysis or dissemination of patient or disease data
- G06F19/32—Medical data management, e.g. systems or protocols for archival or communication of medical images, computerised patient records or computerised general medical references
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N33/00—Investigating or analysing materials by specific methods not covered by the preceding groups
- G01N33/48—Investigating or analysing materials by specific methods not covered by the preceding groups biological material, e.g. blood, urine; Haemocytometers
- G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
- G01N33/53—Immunoassay; Biospecific binding assay
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N99/00—Subject matter not provided for in other groups of this subclass
- G06N99/005—Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Systems or methods specially adapted for a specific business sector, e.g. utilities or tourism
- G06Q50/01—Social networking
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
- G06F17/30286—Information retrieval; Database structures therefor; File system structures therefor in structured data stores
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Singh et al. | Contrastive learning in protein language space predicts interactions between drugs and protein targets | |
Van den Berge et al. | Observation weights unlock bulk RNA-seq tools for zero inflation and single-cell applications | |
Schwabe et al. | The transcriptome dynamics of single cells during the cell cycle | |
Lee et al. | Review of statistical methods for survival analysis using genomic data | |
Piccolo et al. | Multiplatform single-sample estimates of transcriptional activation | |
Jayawardana et al. | Determination of prognosis in metastatic melanoma through integration of clinico‐pathologic, mutation, mRNA, microRNA, and protein information | |
AU779635B2 (en) | Methods and devices for identifying patterns in biological systems and methods for uses thereof | |
Cho et al. | Generalizable and scalable visualization of single-cell data using neural networks | |
US6789069B1 (en) | Method for enhancing knowledge discovered from biological data using a learning machine | |
US7324926B2 (en) | Methods for predicting chemosensitivity or chemoresistance | |
Malod-Dognin et al. | Unified alignment of protein-protein interaction networks | |
KR20220069943A (en) | Single-cell RNA-SEQ data processing | |
Yu et al. | RNA‐Seq‐Based Breast Cancer Subtypes Classification Using Machine Learning Approaches | |
US11574718B2 (en) | Outcome driven persona-typing for precision oncology | |
Alexe et al. | Logical analysis of data–the vision of Peter L. Hammer | |
Chen et al. | Predictive biomarkers for treatment selection: statistical considerations | |
Senthilkumar et al. | Incorporating artificial fish swarm in ensemble classification framework for recurrence prediction of cervical cancer | |
US20240249839A1 (en) | Systems, software, and methods for multiomic single cell classification and prediction and longitudinal trajectory analysis | |
US20200311384A1 (en) | Clustering methods using a grand canonical ensemble | |
Lamba et al. | Breast cancer prediction and categorization in the molecular era of histologic grade | |
JP2023549614A (en) | Methods and systems for quantifying cellular activity from high-throughput sequencing data | |
Li et al. | scDEA: differential expression analysis in single-cell RNA-sequencing data via ensemble learning | |
Sumanaweera et al. | Gene-level alignment of single-cell trajectories | |
Henao et al. | Multi-omics regulatory network inference in the presence of missing data | |
Pandey et al. | Improved downstream functional analysis of single-cell RNA-sequence data using DGAN |