Overview

Why are cutting-edge data science techniques such as bioinformatics, few-shot learning, and zero-shot learning underutilized in the world of biological sciences?. In a rapidly advancing field, the failure to harness the full potential of these disciplines limits scientists' ability to unlock critical insights into biological systems, personalized medicine, and biomarker identification. This untapped potential hinders progress and limits our capacity to tackle complex biological challenges. The solution to this issue lies within the pages of Applying Machine Learning Techniques to Bioinformatics. This book serves as a powerful resource, offering a comprehensive analysis of how these emerging disciplines can be effectively applied to the realm of biological research. By addressing these challenges and providing in-depth case studies and practical implementations, the book equips researchers, scientists, and curious minds with the knowledge and techniques needed to navigate the ever-changing landscape of bioinformatics and machine learning within the biological sciences. Designed for academic scholars and practitioners, as well as upper-level undergraduates and graduates seeking to expand their knowledge, this book is a must-read for anyone passionate about the intersection of data science and human biology. Healthcare professionals, biotechnologists, and academics alike will find this resource invaluable for advancing their understanding and capabilities in the dynamic field of bioinformatics.

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Umesh Kumar Lilhore Research Area: AI, ML, Bioinformatics, and Blockchain. Abhishek Kumar Research Area: Cloud Computing, AI, ML, Bioinformatics and Blockchain. Sarita Simaiya Research Area: Cloud Computing, AI, ML, Bioinformatics and Blockchain. Narayan Vyas Research Area: Cloud Computing, AI, ML, Bioinformatics and Blockchain. Vishal Dutt Research Area: Cloud Computing, AI, ML, Bioinformatics and Blockchain

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