GEMS: a system for automated cancer diagnosis and biomarker discovery from microarray gene expression data.
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A p53 drug response signature identifies prognostic genes in high-risk neuroblastomaIdentifying unproven cancer treatments on the health web: addressing accuracy, generalizability and scalabilityA comprehensive comparison of random forests and support vector machines for microarray-based cancer classification.Causal graph-based analysis of genome-wide association data in rheumatoid arthritisA comprehensive evaluation of multicategory classification methods for microbiomic data.Information content and analysis methods for multi-modal high-throughput biomedical data.Formative evaluation of a prototype system for automated analysis of mass spectrometry data.A robust data scaling algorithm to improve classification accuracies in biomedical dataA comparison of univariate and multivariate gene selection techniques for classification of cancer datasets.Genes related to apoptosis predict necrosis of the liver as a phenotype observed in rats exposed to a compendium of hepatotoxicants.Outcome prediction based on microarray analysis: a critical perspective on methodsFactors influencing the statistical power of complex data analysis protocols for molecular signature development from microarray dataChallenges in the analysis of mass-throughput data: a technical commentary from the statistical machine learning perspective.A white-box approach to microarray probe response characterization: the BaFL pipeline.Early prediction of reading disability using machine learning.Co-expression network analysis identifies Spleen Tyrosine Kinase (SYK) as a candidate oncogenic driver in a subset of small-cell lung cancerProtein-protein interaction reveals synergistic discrimination of cancer phenotypeA computational pipeline for the development of multi-marker bio-signature panels and ensemble classifiers.Microbiomic signatures of psoriasis: feasibility and methodology comparison.Benefits of biomarker selection and clinico-pathological covariate inclusion in breast cancer prognostic modelsIdentification of biomarkers that distinguish chemical contaminants based on gene expression profiles.A Filter Feature Selection Method Based on MFA Score and Redundancy Excluding and It's Application to Tumor Gene Expression Data Analysis.An experimental study of the intrinsic stability of random forest variable importance measures.Structured feature selection using coordinate descent optimization.Low-grade inflammation in symptomatic knee osteoarthritis: prognostic value of inflammatory plasma lipids and peripheral blood leukocyte biomarkersIdentification of common tumor signatures based on gene set enrichment analysis.An 18 gene expression-based score classifier predicts the clinical outcome in stage 4 neuroblastoma.Classifying publications from the clinical and translational science award program along the translational research spectrum: a machine learning approachThe FAST-AIMS Clinical Mass Spectrometry Analysis System.Medical decision support using machine learning for early detection of late-onset neonatal sepsis.Machine Learning Approach to Automated Quality Identification of Human Induced Pluripotent Stem Cell Colony Images.CCL8 and the Immune Control of Cytomegalovirus in Organ Transplant Recipients.Are random forests better than support vector machines for microarray-based cancer classification?Support vector machine based diagnostic system for breast cancer using swarm intelligence.Selecting Feature Subsets Based on SVM-RFE and the Overlapping Ratio with Applications in Bioinformatics.Using the GEMS System for Supervised Analysis of Cancer Microarray Gene Expression Data.Federated learning of predictive models from federated Electronic Health Records.In-sample Model Selection for Trimmed Hinge Loss Support Vector Machine
P2860
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P2860
GEMS: a system for automated cancer diagnosis and biomarker discovery from microarray gene expression data.
description
2005 nî lūn-bûn
@nan
2005 թուականի Օգոստոսին հրատարակուած գիտական յօդուած
@hyw
2005 թվականի օգոստոսին հրատարակված գիտական հոդված
@hy
2005年の論文
@ja
2005年学术文章
@wuu
2005年学术文章
@zh-cn
2005年学术文章
@zh-hans
2005年学术文章
@zh-my
2005年学术文章
@zh-sg
2005年學術文章
@yue
name
GEMS: a system for automated c ...... croarray gene expression data.
@ast
GEMS: a system for automated c ...... croarray gene expression data.
@en
type
label
GEMS: a system for automated c ...... croarray gene expression data.
@ast
GEMS: a system for automated c ...... croarray gene expression data.
@en
prefLabel
GEMS: a system for automated c ...... croarray gene expression data.
@ast
GEMS: a system for automated c ...... croarray gene expression data.
@en
P2093
P1476
GEMS: a system for automated c ...... croarray gene expression data.
@en
P2093
Alexander Statnikov
Constantin F Aliferis
Ioannis Tsamardinos
Yerbolat Dosbayev
P304
P356
10.1016/J.IJMEDINF.2005.05.002
P577
2005-08-01T00:00:00Z