Simple decision rules for classifying human cancers from gene expression profiles. - Wikidata - awgldk - TriplyDB

Simple decision rules for classifying human cancers from gene expression profiles.

Simple decision rules for classifying human cancers from gene expression profiles.

http://www.wikidata.org/entity/Q41735339

about

Patient-derived tumour xenografts as models for oncology drug development Highly accurate two-gene classifier for differentiating gastrointestinal stromal tumors and leiomyosarcomas Using rule-based machine learning for candidate disease gene prioritization and sample classification of cancer gene expression data Classification of tumor samples from expression data using decision trunks.A comprehensive sensitivity analysis of microarray breast cancer classification under feature variability.Two-transcript gene expression classifiers in the diagnosis and prognosis of human diseases.iCOSSY: An Online Tool for Context-Specific Subnetwork Discovery from Gene Expression Data.Outlier Analysis and Top Scoring Pair for Integrated Data Analysis and Biomarker Discovery.CAMUR: Knowledge extraction from RNA-seq cancer data through equivalent classification rules.Integrating multiple immunogenetic data sources for feature extraction and mining somatic hypermutation patterns: the case of "towards analysis" in chronic lymphocytic leukaemia.Large-scale integration of cancer microarray data identifies a robust common cancer signature.Merging microarray data from separate breast cancer studies provides a robust prognostic test.Automated Detection of Cancer Associated Genes Using a Combined Fuzzy-Rough-Set-Based F-Information and Water Swirl Algorithm of Human Gene Expression Data.On sparse Fisher discriminant method for microarray data analysis.A Robust and Efficient Feature Selection Algorithm for Microarray Data.Gene expression profile of empirically delineated classes of unexplained chronic fatigue.Iterative Bayesian Model Averaging: a method for the application of survival analysis to high-dimensional microarray data.An integrated method for cancer classification and rule extraction from microarray data.Integrative analysis of gene expression data including an assessment of pathway enrichment for predicting prostate cancer The ordering of expression among a few genes can provide simple cancer biomarkers and signal BRCA1 mutations.Classification across gene expression microarray studies.Feature weight estimation for gene selection: a local hyperlinear learning approach.A blood RNA signature for tuberculosis disease risk: a prospective cohort study.Identifying tightly regulated and variably expressed networks by Differential Rank Conservation (DIRAC).Identifying disease-associated pathways in one-phenotype data based on reversal gene expression orderings.Improving cancer classification accuracy using gene pairs Systems biology of interstitial lung diseases: integration of mRNA and microRNA expression changes.Confident predictability: identifying reliable gene expression patterns for individualized tumor classification using a local minimax kernel algorithm.Reconstructing cell cycle pseudo time-series via single-cell transcriptome data.Early response index: a statistic to discover potential early stage disease biomarkers.Rank-based miRNA signatures for early cancer detection.Use of Attribute Driven Incremental Discretization and Logic Learning Machine to build a prognostic classifier for neuroblastoma patients Development of an integrated genomic classifier for a novel agent in colorectal cancer: approach to individualized therapy in early development.Top scoring pairs for feature selection in machine learning and applications to cancer outcome prediction.Informative gene selection and direct classification of tumor based on Chi-square test of pairwise gene interactions.Gene-pair expression signatures reveal lineage control.Usefulness of the top-scoring pairs of genes for prediction of prostate cancer progression.Stepwise classification of cancer samples using clinical and molecular data.Relative expression analysis for molecular cancer diagnosis and prognosis In silico models of cancer

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Simple decision rules for classifying human cancers from gene expression profiles.

http://www.wikidata.org/entity/Q41735339

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2005年论文

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Simple decision rules for classifying human cancers from gene expression profiles.

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Simple decision rules for classifying human cancers from gene expression profiles.

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Simple decision rules for classifying human cancers from gene expression profiles.

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Simple decision rules for classifying human cancers from gene expression profiles.

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Aik Choon Tan

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Daniel Q Naiman

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Donald Geman

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Lei Xu

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Raimond L Winslow

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P2860

p29, a novel GCIP-interacting protein, localizes in the nucleus

Classifying gene expression profiles from pairwise mRNA comparisons

New feature subset selection procedures for classification of expression profiles

Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling

Molecular classification of cancer: class discovery and class prediction by gene expression monitoring

Molecular portraits of human breast tumours

Diffuse large B-cell lymphoma outcome prediction by gene-expression profiling and supervised machine learning

Gene-expression profiles predict survival of patients with lung adenocarcinoma

Diagnosis of multiple cancer types by shrunken centroids of gene expression

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10.1093/BIOINFORMATICS/BTI631

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