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REVIEW article

Front. Digit. Health
Sec. Health Informatics
Volume 6 - 2024 | doi: 10.3389/fdgth.2024.1471200
This article is part of the Research Topic Vector-Borne Diseases - The Digital One Health Approach View all 3 articles

Opportunities, Challenges and Future Perspectives of using Bioinformatics and Artificial Intelligence Techniques on Tropical Disease Identification using Omics Data

Provisionally accepted
Sugandima Mihirani Vidanagamachchi Sugandima Mihirani Vidanagamachchi 1*Thamara Rangana Waidyarathna Thamara Rangana Waidyarathna 2
  • 1 Faculty of Science, University of Ruhuna, Matara, Sri Lanka
  • 2 Sri Lanka Institute of Advanced Technological Education (SLIATE), Colombo, Sri Lanka

The final, formatted version of the article will be published soon.

    Tropical diseases can often be caused by viruses, bacteria, parasites, and fungi. They can be spread over vectors. Analysis of multiple omics data types can be utilized in providing comprehensive insights into biological system functions and disease progression. To this end, bioinformatics tools and diverse AI techniques are pivotal in identifying and understanding tropical diseases through the analysis of omics data. In this article, we provide a thorough review of opportunities, challenges, and future directions of utilizing Bioinformatics tools and AI-assisted models on tropical disease identification using various omics data types. We conducted the review from 2015-2024 considering reliable databases of peer-reviewed journals and conference articles. Several keywords were taken for the article searching and around 40 articles were reviewed. According to the review, we observed that utilization of omics data with Bioinformatics tools like BLAST, and Clustal Omega can make significant outcomes in tropical disease identification. Further, the integration of multiple omics data improves biomarker identification, and disease predictions including disease outbreak predictions. Moreover, AI-assisted models can improve the precision, cost-effectiveness, and efficiency of CRISPR-based gene editing, optimizing gRNA design, and supporting advanced genetic correction. Several AI-assisted models including XAI can be used to identify diseases and repurpose therapeutic targets and biomarkers efficiently. Furthermore, recent advancements including Transformer-based models such as BERT and GPT-4, have been mainly applied for sequence analysis and functional genomics. Finally, the most recent GeneViT model, utilizing Vision Transformers, and other AI techniques like Generative Adversarial Networks, Federated Learning, Transfer Learning, Reinforcement Learning, Automated ML, and Attention Mechanisms have shown significant performance in disease classification using omics data.

    Keywords: Genomics, Transcriptomics, Proteomics, tropical, bioinformatics, AI, vision transformers, Advanced-AI Bioinformatics tools and techniques for Tropical Disease Identification using omics data

    Received: 30 Jul 2024; Accepted: 06 Nov 2024.

    Copyright: © 2024 Vidanagamachchi and Waidyarathna. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Sugandima Mihirani Vidanagamachchi, Faculty of Science, University of Ruhuna, Matara, Sri Lanka

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.