Manish Kumar Gupta – författare

Integrative Omics: Concept, Methodology and Application provides a holistic and integrated view of defining and applying network approaches, integrative tools, and methods to solve problems for the rationalization of genotype to phenotype relationships. The reference provides systemic 'step-by-step' coverage that begins with basic concepts from Omic to Multi Integrative Omics approaches followed by applications and emerging and future trends. All areas of Omics are covered, including biological databases, sequence alignment, pharmacogenomics, nutrigenomics and microbial omics, integrated omics for Food Science and Identification of genes associated with disease, clinical data integration and data warehousing, translational omics, technology policy, and society research. This book covers recent concepts, methodologies, advancements in technologies and is also well-suited for researchers from both academic and industry background, undergraduate and graduate students who are mainly working in the area of computational systems biology, integrative omics and translational science.

Provides a holistic, integrated view of a defining and applying network approach, integrative tools, and methods to solve problems for rationalization of genotype to phenotype relationshipsOffers an interdisciplinary approach to Databases, data analytics techniques, biological tools, network construction, analysis, modeling, prediction and simulation of biological systems leading to 'translational research', i.e., drug discovery, drug target prediction, and precision medicineCovers worldwide methods, concepts, databases, and tools used in the construction of integrated pathways

The AI Frontier in Molecular Modelling and Drug Designing aims to provide a comprehensive guide on the application of AI concepts in drug designing, discovery, and molecular modeling. The book delves into machine and deep learning techniques that predict structural properties of molecules, identify druggable pockets, and assess physicochemical properties of targets. Sections explore current and potential computational resources to tackle complex biological challenges. Each chapter is enriched with relevant examples, text boxes, and case studies that illustrate the practical application of AI techniques, their outcomes, and the challenges faced during implementation.This resource is tailored for researchers, students, and professionals in both academia and industry, providing them with the latest methodologies, advancements in technology, and practical insights into AI-driven drug research, structural biology, computational biology, and translational science.Provides the latest concepts of Artificial Intelligence, methodologies, and technological advancements in drug researchCovers a wide range of topics, including protein structure prediction and molecular docking methods, bridging Artificial Intelligence technology with pharmaceutical sciences for translational researchIncludes detailed practical applications of AI, with case studies demonstrating how AI-driven approaches expedite drug discovery and molecular modeling processes