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Penalized feature selection and classification in bioinformatics.

Shuangge Ma¹, Jian Huang

¹Department of Epidemiology and Public Health, Yale University, USA. shuangge.ma@yale.edu

Briefings in Bioinformatics

|June 20, 2008

Summary

This review covers penalized feature selection and classification methods for high-dimensional bioinformatics data. These embedded techniques improve classifier reliability and offer insights into genomic, epigenetic, and proteomic studies.

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Area of Science:

Bioinformatics
Computational Biology
Genomics
Epigenomics
Proteomics

Background:

Supervised classification with high-dimensional data is common in bioinformatics.
Genomic, epigenetic, and proteomic studies frequently involve complex datasets.
Over-fitting is a challenge in high-dimensional classification tasks.

Purpose of the Study:

To review recently developed penalized feature selection and classification techniques.
To highlight embedded methods applicable to high-dimensional bioinformatics data.
To inform researchers about advanced analytical tools for biological data.

Main Methods:

Discussion of classification objective functions.
Analysis of various penalty functions.
Overview of computational algorithms for feature selection and classification.

Main Results:

Penalized methods offer a way to perform feature selection concurrently with classifier construction.
These embedded techniques can enhance classifier performance and interpretability.
The review categorizes methods based on their objective and penalty functions.

Conclusions:

Penalized feature selection and classification are valuable for high-dimensional bioinformatics.
Awareness of these methods can aid researchers in analyzing complex biological datasets.
The discussed techniques contribute to more reliable and insightful biological data analysis.