BIOINFORMATICS: BIG DATA & MACHINE LEARNING IN BIOCHEMICAL PATHWAYS

This book is meant to be used for academic purposes and for reference purposes. It has many chapters on applied bioinformatics where the author discusses his thoughts of how the problem was handled, right until a deliverable solution was found. The book covers various aspects of bioinformatics and talks about some of the best bioinformatics tools available in the industry for novice as well as experts. It not only discusses some of the prominent web-based bioinformatics tools but also those which can be installed on a stand-alone system. It also shows by example how machine learning and statistical modeling can be used for solving various kinds of problems be it in pattern search or extracting more insights from data. The chapters can also be looked upon as case-studies where the work leads to an eventual publication – and so by going through each of the chapters the reader will have a firm grasp of the problem and solution which can make the reader broaden his horizon. It is also ensured that one of the chapters is ‘wet-lab’ based with support from a bioinformatics perspective wherever needed, and so by doing so the student can well understand how wet-lab bench work is integrated with data science, analysis and bioinformatics. Wherever possible, the chapters have been written in ‘Big Data’ perspective to make a coherent flow of understanding what the book is about. It is advised to use this book for a research based course in bioinformatics at Universities for final year bachelor students, master’s and doctoral studies. Although the scope of bioinformatics is huge, the book tries to touch upon major areas of bioinformatics, applied machine learning and data modeling, medical informatics, parallel computing,  and genetic engineering in general to improve upon the understanding of best practices in the industry. Students with a background in computer science, mathematics, molecular biology, genetics and biochemistry with interests in ‘biomedical and medical informatics’, high performance computing and genetic engineering would be the ideal target audience for those who want to deepen their understanding in best current practices in these domains.