The Biology and Laboratory Paradigm Shift: A Review of Machine Learning and Artificial Intelligence in Biomolecular Data Interpretation and Predictive Modeling
Abstract
Background: The life sciences are experiencing an explosion of data from high-throughput genomics, proteomics, and metabolomics. It is a challenging problem to interpret the complex data sets in parallel with developments in artificial intelligence (AI) and machine learning (ML).
Aim: This review categorizes the groundbreaking contribution of AI/ML to biomolecular data science during the period 2015-2024, elucidating its use in multi-omics analysis, protein structure prediction, and experimental automation.
Methods: We performed a systematic literature review highlighting the application of sophisticated computational models such as deep neural networks, graph neural networks, and transformer architectures in diverse biomolecular data.
Results: Our results establish that AI/ML has changed the discipline at its core. These technologies facilitate the discovery of new biomarkers and drug targets from multi-omics data and have made breakthrough achievements in protein structure prediction using AlphaFold2. In addition, AI is now automating experimental design, making closed-loop systems that accelerate discovery.
Conclusion: AI and ML are no longer ancillary tools but intrinsic drivers of a new paradigm in molecular biology. Although data quality and interpretability challenges persist, the incorporation of AI is imperative for decoding the patterns of complex biological systems and developing personalized medicine.
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Authors
Copyright (c) 2024 Saeed Ali Alasmari, Abdulmajeed Saad Bin Baz, Mohammed Awadh Alshehri, Saad Salem Aldawsari, Awadh Jarallah Alkaabi, Saad Mahdi Saleh Alamri, Turki Saeed Alwadaie, ALhanouf Mohammed Moredh, Turkia Mohammed Alharthi, Abdullah Ahmed Alamer, Abdullah Salman Al Salman
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