Document Type

Conference Paper

Publication Date

2024

Publication Title

American Society for Engineering Management 2024 International Annual Conference

Pages

10 pp.

Conference Name

American Society for Engineering Management 2024 International Annual Conference, November 6-9, 2024, Virginia Beach, Virginia

Abstract

The advent of Next-Generation Sequencing (NGS) techniques has revolutionized genomic research by enabling the rapid sequencing of DNA and RNA. This data can be used for various applications, including genome sequencing, transcriptome profiling, metagenomics, and epigenetics studies. For this study, DNA classifier dataset was extracted from UCI repository of machine learning databases. This vast amount of genomic data necessitates the development of sophisticated machine learning (ML) models for effective classification and analysis. This study presents a comprehensive comparison of various ML models, including Support Vector Machines (SVM), Random Forests (RF), and Neural Networks (NNs), approaches, in classifying genomic data. We evaluate these models based on accuracy, computational efficiency, and their ability to handle high-dimensional data. The comparison reveals distinct strengths and limitations of each model, with NNs and DL approaches showing superior performance in handling complex patterns in genomic sequences but requiring significant computational resources. Conversely, SVM and RF offer a balance between accuracy and computational demand, making them suitable for applications with limited computational resources. À notable research gap identified is the challenge of integrating diverse genomic data types (e.g., single nucleotide polymorphisms, copy number variations, and gene expression data) in a unified classification model. This gap underscores the need for developing advanced ML models that can effectively leverage the heterogeneous nature of genomic data to improve classification outcomes. This study paves the way for future research aimed at bridging this gap, which is critical for advancing personalized medicine and understanding complex genetic disorders.

Rights

Copyright© 2024. Reprinted with permission of the American Society for Engineering Management. International Annual Conference. All rights reserved.

Included with the kind written permission of the copyright holders.

ORCID

0000-0003-2824-4528 (Alla), 0009-0009-1889-6427 (Bheesetty), 0000-0003-1078-0314 (Mohanty)

Original Publication Citation

Alla, S., Bheesetty, N., Komaragiri, S. G., Chidipudi, P., Mohanty, J., Chintala, S. K., Thomas, J., Vummadi, J., Volikatla, H., & Kamuni, N. (2024). Integrative machine learning approaches for enhanced classification of genomic sequences: A next-generation sequencing perspective [Paper presentation]. American Society for Engineering Management 2024 International Annual Conference, Virginia Beach, Virginia.

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