The Biology and Laboratory Paradigm Shift: A Review of Machine Learning and Artificial Intelligence in Biomolecular Data Interpretation and Predictive Modeling - Alasmari, Saeed Ali; Baz, Abdulmajeed Saad Bin; Alshehri, Mohammed Awadh; Aldawsari, Saad Salem; Alkaabi, Awadh Jarallah; Alamri, Saad Mahdi Saleh; Alwadaie, Turki Saeed; Moredh, ALhanouf Mohammed; Alharthi, Turkia Mohammed; Alamer, Abdullah Ahmed; et al. (Saudi Journal of Medicine and Public Health, 2024)
<span style="font-size: 12pt; font-family: Aptos; color: #0f1115; background-color: transparent; font-weight: bold; font-variant-numeric: normal; font-variant-east-asian: normal; font-variant-alternates: normal; font-variant-position: normal; vertical-align: baseline; white-space-collapse: preserve;">Background: <span style="font-size: 12pt; font-family: Aptos; color: #0f1115; background-color: transparent; font-variant-numeric: normal; font-variant-east-asian: normal; font-variant-alternates: normal; font-variant-position: normal; vertical-align: baseline; white-space-collapse: preserve;">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). <span style="font-size: 12pt; font-family: Aptos; color: #0f1115; background-color: transparent; font-weight: bold; font-variant-numeric: normal; font-variant-east-asian: normal; font-variant-alternates: normal; font-variant-position: normal; vertical-align: baseline; white-space-collapse: preserve;">Aim:<span style="font-size: 12pt; font-family: Aptos; color: #0f1115; background-color: transparent; font-variant-numeric: normal; font-variant-east-asian: normal; font-variant-alternates: normal; font-variant-position: normal; vertical-align: baseline; white-space-collapse: preserve;"> 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. <span style="font-size: 12pt; font-family: Aptos; color: #0f1115; background-color: transparent; font-weight: bold; font-variant-numeric: normal; font-variant-east-asian: normal; font-variant-alternates: normal; font-variant-position: normal; vertical-align: baseline; white-space-collapse: preserve;">Methods:<span style="font-size: 12pt; font-family: Aptos; color: #0f1115; background-color: transparent; font-variant-numeric: normal; font-variant-east-asian: normal; font-variant-alternates: normal; font-variant-position: normal; vertical-align: baseline; white-space-collapse: preserve;"> 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. <span style="font-size: 12pt; font-family: Aptos; color: #0f1115; background-color: transparent; font-weight: bold; font-variant-numeric: normal; font-variant-east-asian: normal; font-variant-alternates: normal; font-variant-position: normal; vertical-align: baseline; white-space-collapse: preserve;">Results:<span style="font-size: 12pt; font-family: Aptos; color: #0f1115; background-color: transparent; font-variant-numeric: normal; font-variant-east-asian: normal; font-variant-alternates: normal; font-variant-position: normal; vertical-align: baseline; white-space-collapse: preserve;"> 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. <span style="font-size: 12pt; font-family: Aptos; color: #0f1115; background-color: transparent; font-weight: bold; font-variant-numeric: normal; font-variant-east-asian: normal; font-variant-alternates: normal; font-variant-position: normal; vertical-align: baseline; white-space-collapse: preserve;">Conclusion:<span style="font-size: 12pt; font-family: Aptos; color: #0f1115; background-color: transparent; font-variant-numeric: normal; font-variant-east-asian: normal; font-variant-alternates: normal; font-variant-position: normal; vertical-align: baseline; white-space-collapse: preserve;"> 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.