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.

The advent of big data era has imposed both running time and learning efficiency challenges for the machine learning researchers. Biomedical OMIC research is one of these big data areas and has changed the biomedical research drastically. But the high cost of data production and difficulty in participant recruitment introduce the paradigm of "large p small n" into the biomedical research. Feature selection is usually employed to reduce the high number of biomedical features, so that a stable data-independent classification or regression model may be achieved. This study randomly changes the first element of the widely-used incremental feature selection (IFS) strategy and selects the best feature subset that may be ranked low by the statistical association evaluation algorithms, e.g. t-test. The hypothesis is that two low-ranked features may be orchestrated to achieve a good classification performance. The proposed Randomly re-started Incremental Feature Selection (RIFS) algorithm demonstrates both higher classification accuracy and smaller feature number than the existing algorithms. RIFS also outperforms the existing methylomic diagnosis model for the prostate malignancy with a larger accuracy and a lower number of transcriptomic features.

RIFS: a randomly restarted incremental feature selection algorithm.

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.

Weiwei Zheng

College of Computer Science and Technology,
and Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education,
College of Computer Science and Technology, and Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education

Jilin University

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Weiwei Zheng

College of Computer Science and Technology,and Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education,College of Computer Science and Technology, and Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education

Jilin University

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

College of Computer Science and Technology,
and Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education,
College of Computer Science and Technology, and Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education