Venkatesan Amouda – författare

Computational Methods in Drug Discovery and Repurposing for Cancer Therapy provides knowledge about ongoing research as well as computational approaches for drug discovery and repurposing for cancer therapy. The book also provides detailed descriptions about target molecules, pathways, and their inhibitors for easy understanding and applicability.

The book discusses tools and techniques such as integrated bioinformatics approaches, systems biology tools, molecular docking, computational chemistry, artificial intelligence, machine learning, structure-based virtual screening, biomarkers, and transcriptome; those are discussed in the context of different cancer types, such as colon, pancreatic, glioblastoma, endometrial, and retinoblastoma, among others.

This book is a valuable resource for researchers, students, and members of the biomedical and medical fields who want to learn more about the use of computational modeling to better tailor the treatment for cancer patients.

Discusses in silico remodeling of effective phytochemical compounds for discovering improved anticancer agents for substantial/significant cancer therapy Covers potential tools of bioinformatics that are applied toward discovering new targets by drug repurposing and strategies to cure different types of cancers Demonstrates the significance of computational and artificial intelligence approaches in anticancer drug discovery Explores how these various advances can be integrated into a precision and personalized medicine approach that can eventually enhance patient care

Computational Methods in Drug Discovery and Repurposing for Cancer Therapy provides knowledge about ongoing research as well as computational approaches for drug discovery and repurposing for cancer therapy. The book also provides detailed descriptions about target molecules, pathways, and their inhibitors for easy understanding and applicability.The book discusses tools and techniques such as integrated bioinformatics approaches, systems biology tools, molecular docking, computational chemistry, artificial intelligence, machine learning, structure-based virtual screening, biomarkers, and transcriptome; those are discussed in the context of different cancer types, such as colon, pancreatic, glioblastoma, endometrial, and retinoblastoma, among others.This book is a valuable resource for researchers, students, and members of the biomedical and medical fields who want to learn more about the use of computational modeling to better tailor the treatment for cancer patients.- Discusses in silico remodeling of effective phytochemical compounds for discovering improved anticancer agents for substantial/significant cancer therapy- Covers potential tools of bioinformatics that are applied toward discovering new targets by drug repurposing and strategies to cure different types of cancers- Demonstrates the significance of computational and artificial intelligence approaches in anticancer drug discovery- Explores how these various advances can be integrated into a precision and personalized medicine approach that can eventually enhance patient care

Artificial Intelligence in Gastrointestinal Cancers examines the transformative impact of AI technologies on diagnosing and treating gastrointestinal malignancies. This book explores how advanced algorithms, particularly deep learning models, enhance the analysis of medical imaging, including endoscopic, MRI, and CT scans, facilitating earlier and more accurate identification of cancers such as esophageal, stomach, colorectal, and pancreatic cancers. The content covers AI's multifaceted applications, from improving diagnostic accuracy to personalizing treatment strategies based on genomic and clinical data. It discusses how machine learning predicts patient outcomes and optimizes treatment plans while also addressing AI's role in accelerating drug discovery and clinical trials. For an academic audience, this book provides crucial insights into AI's current and future applications in GI oncology. Readers gain a comprehensive understanding of AI diagnostic tools and personalized treatment strategies, equipping researchers and clinicians with the knowledge to leverage these advancements for improved patient care and outcomes in gastrointestinal cancers.Provides a comprehensive overview of how AI algorithms are applied in the early detection and diagnosis of gastrointestinal cancersHighlights the role of machine learning in analyzing large datasets to predict treatment responses and patient outcomesOutlines how AI accelerates the process of drug discovery and optimizes clinical trial design