Multi-variable AUC for sifting complementary features and its biomedical application.

Abstract

Although sifting functional genes has been discussed for years, traditional selection methods tend to be ineffective in capturing potential specific genes. First, typical methods focus on finding features (genes) relevant to class while irrelevant to each other. However, the features that can offer rich discriminative information are more likely to be the complementary ones. Next, almost all existing methods assess feature relations in pairs, yielding an inaccurate local estimation and lacking a global exploration. In this paper, we introduce multi-variable Area Under the receiver operating characteristic Curve (AUC) to globally evaluate the complementarity among features by employing Area Above the receiver operating characteristic Curve (AAC). Due to AAC, the class-relevant information newly provided by a candidate feature and that preserved by the selected features can be achieved beyond pairwise computation. Furthermore, we propose an AAC-based feature selection algorithm, named Multi-variable AUC-based Combined Features Complementarity, to screen discriminative complementary feature combinations. Extensive experiments on public datasets demonstrate the effectiveness of the proposed approach. Besides, we provide a gene set about prostate cancer and discuss its potential biological significance from the machine learning aspect and based on the existing biomedical findings of some individual genes.

• Authors: Yue Su, Keyu Du, Jun Wang, Jin-Mao Wei, Jian Liu
• Journal: Briefings in bioinformatics (Brief Bioinform) Vol. 23 Issue 2 (Mar 10 2022) ISSN: 1477-4054 [Electronic] England
• PMID: 35212712 (Publication Type: Journal Article, Research Support, Non-U.S. Gov't)
• Copyright: © The Author(s) 2022. Published by Oxford University Press. All rights reserved. For Permissions, please email: [email protected].

Topics

• Algorithms
• Area Under Curve
• Machine Learning
• ROC Curve