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<p><b>Rabinarayan Satpathy</b> graduated from the National Institute of Technology – Rourkela. He has received 2 PhDs, one in Computational Mathematics from Utkal University and other in Computer Science Engineering from Fakir Mohan University, as well as a DSc in Computational Fluid Dynamics. <p><b>Tanupriya Choudhury</b> earned his PhD in 2016. He has filed 14 patents and received 16 copyrights from MHRD for his own software. He has authored more than 85 research papers. He is also Technical Adviser of Deetya Soft Pvt. Ltd. Noida, IVRGURU Mydigital360, etc. <p><b>Suneeta Satpathy</b>, received her PhD from Utkal University, Bhubaneswar, Odisha, in 2015 with Directorate of Forensic Sciences, Her research interests include computer forensics, cyber security, data fusion, data mining, big data analysis, and decision mining. She has edited several books. <p><b>Sachi Nandan Mohanty</b>, received his PhD from IIT Kharagpur in 2015. His research areas include data mining, big data analysis, cognitive science, fuzzy decision making, brain-computer interface, and computational intelligence. He has authored 3 books as well as edited four, of which several are with the Wiley-Scrivener imprint. <p><b>Xiaobo Zhang</b> received his Master of Computer Science, Doctor of Engineering (Control Theory and Control Engineering) and works in the Department of Automation, Guangdong University of Technology, China. He has published more than 30 papers in academic journals as well as edited three books. He has applied for more than 40 invention patents and obtained 6 software copyrights.

<p><b>The chapters are based on progressive collaborative research work on a broad range of topics and implementations, and will be of interest to both researchers and students from computer science and biological domains.</b> <p>Machine learning techniques are increasingly being used to address problems in computational biology and bioinformatics. Novel machine learning computational techniques to analyze high throughput data in the form of sequences, gene and protein expressions, pathways, and images are becoming vital for understanding diseases and future drug discovery. Machine learning techniques such as Markov models, support vector machines, neural networks, and graphical models have been successful in analyzing life science data because of their capabilities in handling randomness and uncertainty of data noise and in generalization. <p><i>Data Analytics in Bioinformatics</i> compiles recent approaches in machine learning methods and their applications in addressing contemporary problems in bioinformatics approximating classification and prediction of disease, feature selection, dimensionality reduction, gene selection and classification of microarray data, and much more. <p><b>Audience</b> <p>The book is an indispensable resource for computer scientists, engineers, biologists, mathematicians, researchers, clinicians, physicians, and medical informaticists. It is also a valuable reference text for computer science, engineering, and biology courses at the upper undergraduate and graduate levels.

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