Irigoien et al., 2018 - Google Patents
Identification of differentially expressed genes by means of outlier detectionIrigoien et al., 2018
View HTML- Document ID
- 9982301534132263928
- Author
- Irigoien I
- Arenas C
- Publication year
- Publication venue
- BMC bioinformatics
External Links
Snippet
Background An important issue in microarray data is to select, from thousands of genes, a small number of informative differentially expressed (DE) genes which may be key elements for a disease. If each gene is analyzed individually, there is a big number of hypotheses to …
- 238000001514 detection method 0 title description 4
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
- G06F19/10—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
- G06F19/18—Bioinformatics, i.e. methods or systems for genetic or protein-related data processing in computational molecular biology for functional genomics or proteomics, e.g. genotype-phenotype associations, linkage disequilibrium, population genetics, binding site identification, mutagenesis, genotyping or genome annotation, protein-protein interactions or protein-nucleic acid interactions
-
- 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
- G06F19/10—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
- G06F19/12—Bioinformatics, i.e. methods or systems for genetic or protein-related data processing in computational molecular biology for modelling or simulation in systems biology, e.g. probabilistic or dynamic models, gene-regulatory networks, protein interaction networks or metabolic networks
-
- 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
- G01N33/574—Immunoassay; Biospecific binding assay for cancer
- G01N33/57407—Specifically defined cancers
-
- 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
- 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
- G01N33/5005—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving human or animal cells
-
- 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
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
-
- C—CHEMISTRY; METALLURGY
- C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
- C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES OR MICRO-ORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
- C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or micro-organisms; Compositions therefor; Processes of preparing such compositions
- C12Q1/68—Measuring or testing processes involving enzymes, nucleic acids or micro-organisms; Compositions therefor; Processes of preparing such compositions involving nucleic acids
- C12Q1/6876—Hybridisation probes
- C12Q1/6883—Hybridisation probes for diseases caused by alterations of genetic material
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Chen et al. | Deep-learning approach to identifying cancer subtypes using high-dimensional genomic data | |
| Lu et al. | Predicting features of breast cancer with gene expression patterns | |
| Hanczar et al. | Small-sample precision of ROC-related estimates | |
| Xiao et al. | A novel significance score for gene selection and ranking | |
| Boulesteix et al. | Stability and aggregation of ranked gene lists | |
| Student et al. | Stable feature selection and classification algorithms for multiclass microarray data | |
| Dessì et al. | A comparative analysis of biomarker selection techniques | |
| Liu et al. | Data integration by multi-tuning parameter elastic net regression | |
| WO2021006279A1 (en) | Data processing and classification for determining a likelihood score for breast disease | |
| Ubels et al. | Predicting treatment benefit in multiple myeloma through simulation of alternative treatment effects | |
| Ahmad et al. | Towards clinically more relevant dissection of patient heterogeneity via survival-based Bayesian clustering | |
| Hewett et al. | Tumor classification ranking from microarray data | |
| Papiez et al. | BatchI: Batch effect Identification in high-throughput screening data using a dynamic programming algorithm | |
| Ni et al. | Performance evaluation of transcriptomics data normalization for survival risk prediction | |
| Wang et al. | Interaction screening by Kendall’s partial correlation for ultrahigh-dimensional data with survival trait | |
| Qi et al. | A new feature selection method based on feature distinguishing ability and network influence | |
| Banerjee et al. | Adaptive and powerful microbiome multivariate association analysis via feature selection | |
| Vijayan et al. | Blood-based transcriptomic signature panel identification for cancer diagnosis: benchmarking of feature extraction methods | |
| Mohammed et al. | Colorectal cancer classification and survival analysis based on an integrated RNA and DNA molecular signature | |
| Madjar et al. | Weighted cox regression for the prediction of heterogeneous patient subgroups | |
| Ruan et al. | An empirical Bayes’ approach to joint analysis of multiple microarray gene expression studies | |
| Irigoien et al. | Identification of differentially expressed genes by means of outlier detection | |
| De Smet et al. | Balancing false positives and false negatives for the detection of differential expression in malignancies | |
| Polewko-Klim et al. | Integration of multiple types of genetic markers for neuroblastoma may contribute to improved prediction of the overall survival | |
| Fan et al. | Probability calibration-based prediction of recurrence rate in patients with diffuse large B-cell lymphoma |