Abstract

The exponential growth of genomic data, driven by advancements in high-throughput sequencing technologies, has created a critical demand for robust computational frameworks capable of extracting biologically meaningful insights from high-dimensional, complex datasets. Feature selection plays a pivotal role in this context by identifying the most informative genomic markers while mitigating noise, redundancy, and overfitting. This book chapter presents a comprehensive study on the integration of Deep Learning (DL) architectures with Particle Swarm Optimization (PSO) techniques for effective feature selection in genomic data analysis. Deep neural networks, particularly Convolutional Neural Networks (CNNs), Autoencoders, and Long Short-Term Memory (LSTM) models, are examined for their ability to learn hierarchical and nonlinear representations of genetic information. Concurrently, PSO is explored as a population-based metaheuristic optimization strategy capable of navigating high-dimensional search spaces to select optimal feature subsets that enhance model performance.The synergy between deep learning and PSO enables a dynamic and adaptive approach to genomic feature selection, improving classification accuracy, interpretability, and computational efficiency. Several hybrid models and architectural variations are evaluated across benchmark datasets, highlighting their effectiveness in phenotype prediction, gene expression profiling, and regulatory element identification. The chapter addresses critical aspects such as latent space evaluation, architectural tuning, model interpretability, and the role of biologically informed fitness functions. The findings underscore the potential of hybrid DL-PSO frameworks to accelerate discoveries in computational genomics, enabling more accurate diagnostics, refined disease subtyping, and targeted therapeutic strategies. This contribution establishes a foundational perspective for future research and applications in precision medicine and integrative bioinformatics. 

Introduction

The exponential advancement of high-throughput sequencing technologies, such as next generation sequencing (NGS) and microarrays, has revolutionized the study of genomics by generating vast volumes of high-dimensional biological data [1]. These datasets, characterized by thousands to millions of features with relatively few samples, are commonly used in understanding gene functions, identifying disease-associated biomarkers, and discovering therapeutic targets [2]. The presence of irrelevant, redundant, and noisy features introduces significant challenges for computational models [3]. In this context, feature selection plays a crucial role in eliminating noninformative variables and preserving biologically relevant features to improve the performance of predictive models [4]. Traditional statistical methods and machine learning-based feature selectors have made considerable progress, but many still struggle with scalability, robustness, and generalization when applied to complex genomic datasets with non-linear relationships among features [5].

To address the inherent limitations of conventional models, deep learning has emerged as a transformative approach capable of handling the intricacies of genomic data [6]. Deep learning algorithms, including Convolutional Neural Networks (CNNs), Autoencoders, and Long ShortTerm Memory (LSTM) networks, offer hierarchical learning capabilities that extract high-level abstractions from raw data [7]. These models are particularly well-suited for learning complex feature interactions and discovering hidden structures embedded in biological sequences or expression matrices [8]. Their ability to automatically learn representations without relying on handcrafted features allows them to outperform traditional classifiers in tasks such as gene expression-based phenotype prediction, regulatory element recognition, and sequence classification [9]. Their power, deep learning models are sensitive to high-dimensional noise, require large-scale data for training, and are prone to overfitting, necessitating the integration of robust feature selection mechanisms to enhance model performance [10].