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Computational and bioinformatics tools for understanding disease mechanisms

MOHD ATHAR1,*, ANU MANHAS2, NISARG RANA2, AHMAD IRFAN3

1 Physics Department, University of Cagliari, Cittadella Universitaria, Monserrato (CA), 09042, Italy
2 Department of Chemistry, School of Energy Technology, Pandit Deendayal Energy University, Gandhinagar, 382426, India
3 Department of Chemistry, College of Science, King Khalid University, Abha, 61413, Saudi Arabia

* Corresponding Author: MOHD ATHAR. Email: email

(This article belongs to the Special Issue: Computational Approaches in Molecular and Cellular Biology: Advancements in Disease Research and Therapeutic Design)

BIOCELL 2024, 48(6), 935-944. https://doi.org/10.32604/biocell.2024.049891

Abstract

Computational methods have significantly transformed biomedical research, offering a comprehensive exploration of disease mechanisms and molecular protein functions. This article reviews a spectrum of computational tools and network analysis databases that play a crucial role in identifying potential interactions and signaling networks contributing to the onset of disease states. The utilization of protein/gene interaction and genetic variation databases, coupled with pathway analysis can facilitate the identification of potential drug targets. By bridging the gap between molecular-level information and disease understanding, this review contributes insights into the impactful utilization of computational methods, paving the way for targeted interventions and therapeutic advancements in biomedical research.

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APA Style
ATHAR, M., MANHAS, A., RANA, N., IRFAN, A. (2024). Computational and bioinformatics tools for understanding disease mechanisms. BIOCELL, 48(6), 935-944. https://doi.org/10.32604/biocell.2024.049891
Vancouver Style
ATHAR M, MANHAS A, RANA N, IRFAN A. Computational and bioinformatics tools for understanding disease mechanisms. BIOCELL . 2024;48(6):935-944 https://doi.org/10.32604/biocell.2024.049891
IEEE Style
M. ATHAR, A. MANHAS, N. RANA, and A. IRFAN "Computational and bioinformatics tools for understanding disease mechanisms," BIOCELL , vol. 48, no. 6, pp. 935-944. 2024. https://doi.org/10.32604/biocell.2024.049891



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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