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Minimum redundancy feature selection from microarray gene expression data.

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TLDR
How to selecting a small subset out of the thousands of genes in microarray data is important for accurate classification of phenotypes.
Abstract
How to selecting a small subset out of the thousands of genes in microarray data is important for accurate classification of phenotypes. Widely used methods typically rank genes according to their ...

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BATMAN-TCM: a Bioinformatics Analysis Tool for Molecular mechANism of Traditional Chinese Medicine

TL;DR: BATMAN-TCM will contribute to the understanding of the “multi-component, multi-target and multi-pathway” combinational therapeutic mechanism of TCM, and provide valuable clues for subsequent experimental validation, accelerating the elucidation of TCm’s molecular mechanism.
Journal ArticleDOI

A Survey on Filter Techniques for Feature Selection in Gene Expression Microarray Analysis

TL;DR: This survey focuses on filter feature selection methods for informative feature discovery in gene expression microarray (GEM) analysis, which is also known as differentially expressed genes (DEGs) discovery, gene prioritization, or biomarker discovery, and presents them in a unified framework.
Journal ArticleDOI

minet : A R/Bioconductor Package for Inferring Large Transcriptional Networks Using Mutual Information

TL;DR: The package minet provides a series of tools for inferring transcriptional networks from microarray data and integrates accuracy assessment tools, like F-scores, PR-curves and ROC-Curves in order to compare the inferred network with a reference one.
Journal ArticleDOI

MicroRNAs and complex diseases: from experimental results to computational models.

TL;DR: Twenty state-of-the-art computational models of predicting miRNA-disease associations from different perspectives are reviewed, including five feasible and important research schemas, and future directions for further development of computational models are summarized.
References
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Journal ArticleDOI

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

TL;DR: A generic approach to cancer classification based on gene expression monitoring by DNA microarrays is described and applied to human acute leukemias as a test case and suggests a general strategy for discovering and predicting cancer classes for other types of cancer, independent of previous biological knowledge.
Journal ArticleDOI

Wrappers for feature subset selection

TL;DR: The wrapper method searches for an optimal feature subset tailored to a particular algorithm and a domain and compares the wrapper approach to induction without feature subset selection and to Relief, a filter approach tofeature subset selection.
Journal ArticleDOI

A comparison of methods for multiclass support vector machines

TL;DR: Decomposition implementations for two "all-together" multiclass SVM methods are given and it is shown that for large problems methods by considering all data at once in general need fewer support vectors.
Journal ArticleDOI

Broad patterns of gene expression revealed by clustering analysis of tumor and normal colon tissues probed by oligonucleotide arrays.

TL;DR: In this paper, a two-way clustering algorithm was applied to both the genes and the tissues, revealing broad coherent patterns that suggest a high degree of organization underlying gene expression in these tissues.
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