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Cost-Efficient Transcriptomic-Based Drug Screening

Cost-Efficient Transcriptomic-Based Drug Screening

Published on: February 23, 2024

A between-class overlapping filter-based method for transcriptome data analysis.

Alok Sharma¹, Seiya Imoto, Satoru Miyano

¹Laboratory of DNA Information Analysis, Human Genome Center, Institute of Medical Science, University of Tokyo, 4-6-1 Shirokanedai, Minato-ku, Tokyo 108-8639, Japan. aloks@ims.u-tokyo.ac.jp

Journal of Bioinformatics and Computational Biology

|August 2, 2012

Summary

This summary is machine-generated.

This study introduces novel univariate filter-based methods for gene selection in cancer classification. These methods demonstrate improved accuracy and biological significance, offering efficient tools for discovery.

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High-Throughput Transcriptome Analysis for Investigating Host-Pathogen Interactions

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Cost-Efficient Transcriptomic-Based Drug Screening

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High-Throughput Transcriptome Analysis for Investigating Host-Pathogen Interactions

High-Throughput Transcriptome Analysis for Investigating Host-Pathogen Interactions

Published on: March 5, 2022

Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

Feature selection is vital for identifying key genes in cancer classification.
Filter-based methods offer computational efficiency but often lack classification accuracy.
Univariate filter methods are popular but require enhancement for better performance.

Purpose of the Study:

To propose and evaluate new univariate filter-based feature selection methods.
To enhance gene selection accuracy for cancer classification using a novel criterion.
To assess the biological significance and robustness of proposed methods.

Main Methods:

Development of univariate filter-based methods employing a between-class overlapping criterion.
Comparative analysis against existing univariate filter methods using an acute leukemia dataset.
Evaluation of classification accuracy, gene redundancy (ridge regression, LASSO), similarity, sensitivity, functional relevance, and stability.

Main Results:

The proposed methods achieved promising results in comprehensive experiments.
Selected genes and subsets demonstrated high classification accuracy.
Analyses confirmed biological significance, robustness, and computational efficiency.

Conclusions:

The proposed univariate filter-based methods using between-class overlapping are accurate and robust.
These methods offer biological significance and are computationally efficient for implementation.
They are well-suited for advancing biological and clinical discoveries in cancer research.