(untitled) (ML in Pharmacognosy & Biotech Discovery, 2025)
Author Mrs. Sreedevi Kudaravalli, Associate Professor, Department of Pharmaceutical Biotechnology, Sultan-ul-Uloom College of Pharmacy, Banjara Hills, Hyderabad, Telangana, India Abstract The convergence of computer science and life sciences has birthed a new era where Artificial Intelligence serves as the analytical backbone for decoding the complexity of biological systems. This section explores the fundamental principles of machine learning ranging from supervised classification to deep neural networks and their application in navigating the petabytes of data generated by modern genomics, proteomics, and metabolomics. It frames the central dogma of molecular biology not merely as a biological process but as a flow of information, where DNA, RNA, and proteins function as distinct yet interconnected data layers. AI algorithms automate the labor-intensive task of genomic annotation, utilizing Hidden Markov Models and deep learning to identify gene structures and functional regulatory motifs within the vast non-coding landscape. Beyond static sequence analysis, machine learning models decipher dynamic high-throughput sequencing data, such as RNA-Seq and ChIP-Seq, to reveal the regulatory logic governing gene expression. Algorithms trained on these multidimensional datasets can now predict the function of unknown genes and deconvolute the complex signal transduction networks that dictate cellular behavior. This foundational guideline provides the essential literacy required to navigate the modern bio-computational landscape, transforming raw sequence data into mechanistic biological insight and laying the groundwork for advanced applications in precision medicine and synthetic biology. Keywords: Supervised Learning, Genomic Annotation, Central Dogma, RNA-Seq Analysis, ChIP-Seq, Multi-Omics Integration VIEW PDF