ellipsoidFN: a tool for identifying a heterogeneous set of cancer biomarkers based on gene expressions. - Wikidata - awgldk - TriplyDB

ellipsoidFN: a tool for identifying a heterogeneous set of cancer biomarkers based on gene expressions.

ellipsoidFN: a tool for identifying a heterogeneous set of cancer biomarkers based on gene expressions.

http://www.wikidata.org/entity/Q36619915

about

Network biomarkers reveal dysfunctional gene regulations during disease progression DISIS: prediction of drug response through an iterative sure independence screening MCentridFS: a tool for identifying module biomarkers for multi-phenotypes from high-throughput data.Identifying network biomarkers based on protein-protein interactions and expression data.An Efficient Approach to Screening Epigenome-Wide Data.Pathway and network analysis in proteomics Machine learning applications in cancer prognosis and prediction Promote connections of young computational biologists in China.Big biological data: challenges and opportunities iPcc: a novel feature extraction method for accurate disease class discovery and prediction.Edge biomarkers for classification and prediction of phenotypes.Screening candidate microRNA-mRNA regulatory pairs for predicting the response to chemoradiotherapy in rectal cancer by a bioinformatics approach Computer-aided biomarker discovery for precision medicine: data resources, models and applications.

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P2860

ellipsoidFN: a tool for identifying a heterogeneous set of cancer biomarkers based on gene expressions.

http://www.wikidata.org/entity/Q36619915

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Luonan Chen

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Qi Jin

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Xiang-Sun Zhang

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Xianwen Ren

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Yong Wang

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P2860

Significance analysis of microarrays applied to the ionizing radiation response

NCBI GEO: archive for functional genomics data sets--10 years on

ALDH1A1 is a marker for malignant prostate stem cells and predictor of prostate cancer patients' outcome

Gene selection and classification of microarray data using random forest.

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

A Bayesian framework for the analysis of microarray expression data: regularized t -test and statistical inferences of gene changes

A two-sample Bayesian t-test for microarray data.

Predicting the prognosis of breast cancer by integrating clinical and microarray data with Bayesian networks.

Integrative molecular concept modeling of prostate cancer progression.

Molecular signatures suggest a major role for stromal cells in development of invasive breast cancer.

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