Ensemble machine learning on gene expression data for cancer classification.
about
A combinational feature selection and ensemble neural network method for classification of gene expression dataAn ensemble model of QSAR tools for regulatory risk assessmentConceptualizing cancer drugs as classifiersArrayMining: a modular web-application for microarray analysis combining ensemble and consensus methods with cross-study normalization.Combining multiple hypothesis testing and affinity propagation clustering leads to accurate, robust and sample size independent classification on gene expression data.Global gene expression profiling of dimethylnitrosamine-induced liver fibrosis: from pathological and biochemical data to microarray analysis.Accurate molecular classification of cancer using simple rulesNovel methods to identify biologically relevant genes for leukemia and prostate cancer from gene expression profiles.Confident predictability: identifying reliable gene expression patterns for individualized tumor classification using a local minimax kernel algorithm.Optimization based tumor classification from microarray gene expression data.Microarray-based cancer prediction using single genesSelecting a single model or combining multiple models for microarray-based classifier development?--a comparative analysis based on large and diverse datasets generated from the MAQC-II project.Ensemble Classification of Cancer Types and Biomarker IdentificationDesign of a multi-signature ensemble classifier predicting neuroblastoma patients' outcomeSPICE: discovery of phenotype-determining component interplaysETHNOPRED: a novel machine learning method for accurate continental and sub-continental ancestry identification and population stratification correctionMultimodal classification of Alzheimer's disease and mild cognitive impairment.The parameter sensitivity of random forests.A four gene signature predicts benefit from anthracyclines: evidence from the BR9601 and MA.5 clinical trialsComputational selection of antibody-drug conjugate targets for breast cancer.Investigating the efficacy of nonlinear dimensionality reduction schemes in classifying gene and protein expression studies.Microarray-based cancer prediction using soft computing approachA classification framework applied to cancer gene expression profiles.A Hybrid Computer-aided-diagnosis System for Prediction of Breast Cancer Recurrence (HPBCR) Using Optimized Ensemble Learning.Identification of consensus biomarkers for predicting non-genotoxic hepatocarcinogens.Identification of Marker Genes for Cancer Based on Microarrays Using a Computational Biology Approach.Microbial forensics: predicting phenotypic characteristics and environmental conditions from large-scale gene expression profiles.Random Subspace Aggregation for Cancer Prediction with Gene Expression ProfilesSimple decision rules for classifying human cancers from gene expression profiles.Tumor gene expression data classification via sample expansion-based deep learning.A PRObabilistic Pathway Score (PROPS) for Classification with Applications to Inflammatory Bowel Disease.Gene expression profiles for predicting metastasis in breast cancer: a cross-study comparison of classification methods.Anonymizing and Sharing Medical Text Records.On the overestimation of random forest's out-of-bag errorMachine Learning With K-Means Dimensional Reduction for Predicting Survival Outcomes in Patients With Breast Cancer
P2860
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P2860
Ensemble machine learning on gene expression data for cancer classification.
description
2003 nî lūn-bûn
@nan
2003 թուականի Յունուարին հրատարակուած գիտական յօդուած
@hyw
2003 թվականի հունվարին հրատարակված գիտական հոդված
@hy
2003年の論文
@ja
2003年論文
@yue
2003年論文
@zh-hant
2003年論文
@zh-hk
2003年論文
@zh-mo
2003年論文
@zh-tw
2003年论文
@wuu
name
Ensemble machine learning on gene expression data for cancer classification.
@ast
Ensemble machine learning on gene expression data for cancer classification.
@en
type
label
Ensemble machine learning on gene expression data for cancer classification.
@ast
Ensemble machine learning on gene expression data for cancer classification.
@en
prefLabel
Ensemble machine learning on gene expression data for cancer classification.
@ast
Ensemble machine learning on gene expression data for cancer classification.
@en
P1476
Ensemble machine learning on gene expression data for cancer classification
@en
P2093
David Gilbert
P304
P433
P577
2003-01-01T00:00:00Z