A Comprehensive Review of Artificial Intelligence Approaches in Omics Data Processing: Evaluating Progress and Challenges
Keywords:Artificial intelligence, cancer diagnosis, Machine Learning, Deep Learning, Quantum Computing, Multi-Omics, Omics dataset
The primary objective of this study is to review and assess the best available research on omics-related artificial intelligence (AI) methods. Furthermore, it seeks to demonstrate the promise of AI approaches in omics data analysis and identify the critical problems that must be solved to achieve this potential fully. There are many moving parts when trying to make sense of a plethora of research through a literature review. Essential components include, for instance, clinical applications and literature collections. Other researchers have faced challenges, and the existing literature highlights them. Using a systematic strategy, we searched all relevant articles on omics and AI utilizing multiple keyword variations. We also seek additional research, such as guidelines, studies of comparison, and review studies. Challenges with AI, preprocessing, datasets, validation of models, and testbed applications arose when AI was used to analyze omics data. To solve these problems, several pertinent investigations were carried out. Our work offers unique insights into the intersection of omics and AI model fields, setting it apart from prior review articles. We anticipate that practitioners seeking an all-encompassing perspective on using AI in omics data processing would find this study's findings invaluable.
How to Cite
Copyright (c) 2023 Ali Mahmoud Ali, Mazin Abed Mohammed
This work is licensed under a Creative Commons Attribution 4.0 International License.
IJMSCS is published Open Access under a Creative Commons CC-BY 4.0 license. Authors retain full copyright, with the first publication right granted to the journal.