Simple decision rules for classifying human cancers from gene expression profiles.
about
Patient-derived tumour xenografts as models for oncology drug developmentHighly accurate two-gene classifier for differentiating gastrointestinal stromal tumors and leiomyosarcomasUsing rule-based machine learning for candidate disease gene prioritization and sample classification of cancer gene expression dataClassification 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 cancerThe 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 pairsSystems 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 patientsDevelopment 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 prognosisIn silico models of cancer
P2860
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P2860
Simple decision rules for classifying human cancers from gene expression profiles.
description
2005 nî lūn-bûn
@nan
2005年の論文
@ja
2005年論文
@yue
2005年論文
@zh-hant
2005年論文
@zh-hk
2005年論文
@zh-mo
2005年論文
@zh-tw
2005年论文
@wuu
2005年论文
@zh
2005年论文
@zh-cn
name
Simple decision rules for classifying human cancers from gene expression profiles.
@en
type
label
Simple decision rules for classifying human cancers from gene expression profiles.
@en
prefLabel
Simple decision rules for classifying human cancers from gene expression profiles.
@en
P2093
P2860
P356
P1433
P1476
Simple decision rules for classifying human cancers from gene expression profiles.
@en
P2093
Aik Choon Tan
Daniel Q Naiman
Donald Geman
Raimond L Winslow
P2860
P304
P356
10.1093/BIOINFORMATICS/BTI631
P407
P577
2005-08-16T00:00:00Z