Feature Selection Optimization in Bioinformatics Using Machine Learning and Neural Network Techniques

Nizar M. Soufian; Weiguo Gee; Wang Zhang

Feature Selection Optimization in Bioinformatics Using Machine Learning and Neural Network Techniques

Nizar M. SoufianWeiguo GeeWang Zhang
Volume 4

Abstract

Feature selection is a critical step in bioinformatics, aiming to identify the most informative variables from high-dimensional biological datasets to enhance predictive modeling and interpretability. Machine learning techniques, including filter, wrapper, and embedded methods, have been widely employed to assess feature relevance and redundancy, thereby improving model performance and reducing computational complexity. Neural network-based approaches, such as deep learning models, offer advanced capabilities in capturing complex, non-linear relationships among features, facilitating more accurate classification and prediction tasks in genomics and proteomics. Recent advancements have introduced hybrid models that integrate machine learning algorithms with neural networks, leveraging the strengths of both to optimize feature selection processes. These integrative approaches have demonstrated improved accuracy and robustness in various bioinformatics applications, including disease diagnosis and biomarker discovery.

Keywords

Feature Selection, Bioinformatics, Machine Learning, Neural Networks, High Dimensional Data, Predictive Modeling

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