Light Weight Machine Learning Model for Comparative Proteomics Within and Across Organisms

by Parimal Pal Chaudhuri, Adip Kumar Dutta, Raju Hazari

"Light Weight Machine Learning Model for Comparative Proteomics Within and Across Organisms" discusses the current and future possibilities of machine learning and artificial intelligence related to proteomic applications. Rather than working on quantitative/qualitative value of an individual protein expression, this title concentrates on the comparative study of expression of a pair of proteins, under different physiological conditions that may disrupt function of a normal cell. In 8 chapters the book provides a detailed survey on Machine Learning (ML – a subfield of AI) followed by outlines on Artificial Intelligence (AI) covering its historical perspective and philosophical issues associated with the current and future development of AI for comparative proteomics. It elaborates the coverage employing the CAML (Cellular Automata enhanced by Machine Learning) model. A representative set of proteins of wide varieties of organisms is covered in pointing out the similarities and differences using relevant data that is collected from different Databases available in public domain. "Light Weight Machine Learning Model for Comparative Proteomics Within and Across Organisms" includes a current hot area of interest for the scientific community and the ability to connect two reasonably different scientific disciplines together for increased impact.• provides a novelty in cellular automation described as an algorithm capable of computing solutions to various problems integrated with machine learning• evaluates and explores machine learning applications for realistic modeling of data from proteomics analysis of any sample or organism• discusses trending topics in machine learning generally, and the proteomes prediction in particular

Kindle Edition

Expected publication October 1, 2026