selecting-multiple-biomarker-subsets-with-similarly-effective-binary

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Biomarker detection is one of the more important biomedical questions for high-throughput &#39;omics&#39; researchers, and almost all existing biomarker ...

Jilin University

Binary classification is a widely employed problem to facilitate the decisions on various biomedical big data questions, such as clinical drug trials between treated participants and controls, and genome-wide association studies (GWASs) between participants with or without a phenotype. A machine learning model is trained for this purpose by optimizing the power of discriminating samples from two groups. However, most of the classification algorithms tend to generate one locally optimal solution according to the input dataset and the mathematical presumptions of the dataset. Here we demonstrated from the aspects of both disease classification and feature selection that multiple different solutions may have similar classification performances. So the existing machine learning algorithms may have ignored a horde of fishes by catching only a good one. Since most of the existing machine learning algorithms generate a solution by optimizing a mathematical goal, it may be essential for understanding the biological mechanisms for the investigated classification question, by considering both the generated solution and the ignored ones.

Multiple similarly-well solutions exist for biomedical feature selection and classification problems.

A correction to this article has been published and is linked from the HTML version of this paper. The error has been fixed in the paper.

Author Correction: Multiple similarly effective solutions exist for biomedical feature selection and classification problems.

Transcriptomic and methylomic patterns represent two major OMIC data sources impacted by both inheritable genetic information and environmental factors, and have been widely used as disease diagnosis and prognosis biomarkers.

An OMIC biomarker detection algorithm TriVote and its application in methylomic biomarker detection.

A correction to this article has been published and is linked from the HTML and PDF versions of this paper. The error has been fixed in the paper.

Weifeng Yang

College of Software

Jilin University

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Weifeng Yang

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Jilin University

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

College of Software