Affiliation:
1. Department of Artificial Intelligence, Ajou University, Suwon 16499, Republic of Korea
2. Department of Software and Computer Engineering, Ajou University, Suwon 16499, Republic of Korea
Abstract
Feature selection methods are essential for accurate disease classification and identifying informative biomarkers. While information-theoretic methods have been widely used, they often exhibit limitations such as high computational costs. Our previously proposed method, ClearF, addresses these issues by using reconstruction error from low-dimensional embeddings as a proxy for the entropy term in the mutual information. However, ClearF still has limitations, including a nontransparent bottleneck layer selection process, which can result in unstable feature selection. To address these limitations, we propose ClearF++, which simplifies the bottleneck layer selection and incorporates feature-wise clustering to enhance biomarker detection. We compare its performance with other commonly used methods such as MultiSURF and IFS, as well as ClearF, across multiple benchmark datasets. Our results demonstrate that ClearF++ consistently outperforms these methods in terms of prediction accuracy and stability, even with limited samples. We also observe that employing the Deep Embedded Clustering (DEC) algorithm for feature-wise clustering improves performance, indicating its suitability for handling complex data structures with limited samples. ClearF++ offers an improved biomarker prioritization approach with enhanced prediction performance and faster execution. Its stability and effectiveness with limited samples make it particularly valuable for biomedical data analysis.
Funder
National Research Foundation of Korea(NRF) grant funded by the Korea governmen
Institute of Information & communications Technology Planning & Evaluation (IITP) under the Artificial Intelligence Convergence Innovation Human Resources Development
Korea government
Reference48 articles.
1. Biomarkers and surrogate endpoints: Preferred definitions and conceptual framework;Group;Clin. Pharmacol. Ther.,2001
2. A filter-based feature selection approach for identifying potential biomarkers for lung cancer;Lee;J. Clin. Bioinfor.,2011
3. Robust biomarker identification for cancer diagnosis with ensemble feature selection methods;Abeel;Bioinformatics,2010
4. Integrated machine learning pipeline for aberrant biomarker enrichment (i-mAB): Characterizing clusters of differentiation within a compendium of systemic lupus erythematosus patients;Le;AMIA Annu. Symp. Proc.,2018
5. Stable feature selection for biomarker discovery;He;Comput. Biol. Chem.,2010