Journal Article

ExhauFS: exhaustive search-based feature selection for classification and survival regression

Bokov, Grigoriy, Nersisyan, Stepan, Alekseev, Dmitry, Galatenko, Alexei, Novosad, Victor, Tonevitsky, Alexander, Konovalov, Alexander, Sokolov, Andrey

2022

Overview

Feature selection is one of the main techniques used to prevent overfitting in machine learning applications. The most straightforward approach for feature selection is an exhaustive search: one can go over all possible feature combinations and pick up the model with the highest accuracy. This method together with its optimizations were actively used in biomedical research, however, publicly available implementation is missing. We present ExhauFS—the user-friendly command-line implementation of the exhaustive search approach for classification and survival regression. Aside from tool description, we included three application examples in the manuscript to comprehensively review the implemented functionality. First, we executed ExhauFS on a toy cervical cancer dataset to illustrate basic concepts. Then, multi-cohort microarray breast cancer datasets were used to construct gene signatures for 5-year recurrence classification. The vast majority of signatures constructed by ExhauFS passed 0.65 threshold of sensitivity and specificity on all datasets, including the validation one. Moreover, a number of gene signatures demonstrated reliable performance on independent RNA-seq dataset without any coefficient re-tuning, i.e. , turned out to be cross-platform. Finally, Cox survival regression models were used to fit isomiR signatures for overall survival prediction for patients with colorectal cancer. Similarly to the previous example, the major part of models passed the pre-defined concordance index threshold 0.65 on all datasets. In both real-world scenarios (breast and colorectal cancer datasets), ExhauFS was benchmarked against state-of-the-art feature selection models, including L 1 -regularized sparse models. In case of breast cancer, we were unable to construct reliable cross-platform classifiers using alternative feature selection approaches. In case of colorectal cancer not a single model passed the same 0.65 threshold. Source codes and documentation of ExhauFS are available on GitHub: https://github.com/s-a-nersisyan/ExhauFS .

• Publisher: PeerJ. Ltd,PeerJ, Inc,PeerJ Inc
• Subject: Algorithms / Analysis / Bioinformatics / Breast cancer / Breast Neoplasms - genetics / Cervical cancer / Cervix / Classification / Colorectal cancer / Colorectal Neoplasms - genetics / Computational Biology / Data Mining and Machine Learning / Datasets / DNA microarrays / ExhauFS / Exhaustive search / Feature selection / Female / Gene expression / Genomes / Humans / Machine Learning / Medical research / Methods / Microarray Analysis / Molecular Biology / Patients / Regression analysis / Software / Survival / Survival regression