Published Paper Details:

Mathematics underpins the theoretical and computational infrastructures of artificial intelligence (AI) and machine learning (ML), enabling advanced analysis and modeling of biological data. Domains such as linear algebra, calculus, probability theory, and optimization are instrumental in driving breakthroughs across genomics, proteomics, bioinformatics, and systems biology. For example, linear algebra supports the representation and transformation of high-dimensional biological datasets and underpins methods like principal component analysis (PCA) and singular value decomposition (SVD) for feature extraction in gene expression and image data. Meanwhile, calculus is essential to gradient-based neural network training, which is vital for applications such as protein structure prediction and biomedical image segmentation. Probability theory allows handling of uncertainty in biological predictions through Bayesian networks, Markov models, and probabilistic graphical models. Additionally, optimization techniques are crucial for parameter estimation and model calibration in computational biology, such as in metabolic-network optimization and modeling of drug-target interactions. Collectively, these mathematical tools support AI and ML systems in decoding complex biological processes, thereby accelerating progress in precision medicine and biotechnology.