“Artificial Intelligence in Bioinformatics” refers to the use of computational methods—such as machine learning, deep learning, data mining, and neural networks—to analyse, interpret, and understand complex biological data. This includes data from genomics, proteomics, metabolomics, transcriptomics, and other “-omics” fields, as well as DNA sequencing, system biology, and related areas.
Key uses involve:
Predictive modelling of biological processes and disease states
Identification of biomarkers for diagnostics or therapeutic targets
Integrating AI tools with bioinformatics platforms and services to support research and clinical applications
Enhancing decision-support systems in biological sciences
Important considerations include ensuring data quality and integrity, dealing with large and heterogeneous datasets, integrating AI models with existing bioinformatics infrastructure, addressing privacy/security of sensitive biological data, and ensuring interpretability of AI outcomes for scientific or clinical use.