Hands-on Data Science for Biologists using Python has been conceptualized to address the massive data handling needs of modern-day biologists. With the advent of high throughput technologies and consequent availability of omics data, biological science has become a data-intensive field. This hands-on textbook has been written with the inception of easing data analysis by providing an interactive, problem-based instructional approach in Python programming language.
The book starts with an introduction to Python and steadily delves into scrupulous techniques of data handling, preprocessing, and visualization. The book concludes with machine learning algorithms and their applications in biological data science. Each topic has an intuitive explanation of concepts and is accompanied with biological examples.
Features of this book:
- The book contains standard templates for data analysis using Python, suitable for beginners as well as advanced learners.
- This book shows working implementations of data handling and machine learning algorithms using real-life biological datasets and problems, such as gene expression analysis; disease prediction; image recognition; SNP association with phenotypes and diseases.
- Considering the importance of visualization for data interpretation, especially in biological systems, there is a dedicated chapter for the ease of data visualization and plotting.
- Every chapter is designed to be interactive and is accompanied with Jupyter notebook to prompt readers to practice in their local systems.
Other avant-garde component of the book is the inclusion of a machine learning project, wherein various machine learning algorithms are applied for the identification of genes associated with age-related disorders. A systematic understanding of data analysis steps has always been an important element for biological research. This book is a readily accessible resource that can be used as a handbook for data analysis, as well as a platter of standard code templates for building models.
Table of Contents
Python: Introduction and Environment Set up. Basic Python Programming. Biopython. Python for Data Analysis. Python for Data Visualization. Principal Component Analysis. Hands-on Projects. Machine Learning and Linear Regression. Logistic Regression. K-Nearest Neighbors (K-NN). Decision Trees and Random Forests. Support Vector Machines. Neural Nets and Deep Learning. The Machine Learning Project. Natural Language Processing. K-Means Clustering.
Yasha Hasija (B.Tech, M.Tech, Ph.D.) is an Associate Professor at the Department of Biotechnology, and Associate Dean of Alumni Affairs, at Delhi Technological University. Her research interests include genome informatics, genome annotation, microbial informatics, integration of genome-scale data for systems biology, and personalized genomics. Several of her works have been published in international journals of high repute, and she has made noteworthy contributions in the area of biotechnology and bioinformatics as author and editor of notable books. Her expertise, through her book chapters and conference papers, is of significance to other academic scholarship and teaching. She is also on the Editorial Board of numerous international journals. Dr. Hasija’s work has brought her recognition and several prestigious awards, including Human Gene Nomenclature Award at the Human Genome Meeting (2010), held at Montpellier, France. She is the Project Investigator for several research projects sponsored by the Government of India, including DST-SERB, CSIR-OSDD, and DBT. As Dr. Hasjia continues conducting research, her passion for finding the translational implications of her findings grows.
Mr. Rajkumar Chakraborty (B.Tech, M.Tech) received his Bachelors of Technology degree in Biotechnology from Bengal College of Engineering and Technology, West Bengal, India and completed his Masters of Technology degree in Bioinformatics from Delhi Technological University, Delhi, India. He is currently pursuing his Ph.D in the field of bioinformatics. He was a part of 4 member team which won ‘Promising Innovative Implementable Idea Award’ at SAMHAR-COVID19 Hackathon 2020 for innovating solution towards drug repurposing against Covid-19. His research interests are in the applied machine learning and integration of big data in biological science.