 Название: Statistical Modelling and Machine Learning Principles for Bioinformatics Techniques, Tools, and Applications
Автор: K.G. Srinivasa, G.M. Siddesh
Издательство: Springer
Год: 2020
Страниц: 318
Язык: английский
Формат: pdf (true)
Размер: 10.3 MB
This book discusses topics related to bioinformatics, statistics, and Machine Learning, presenting the latest research in various areas of bioinformatics. It also highlights the role of computing and Machine Learning in knowledge extraction from biological data, and how this knowledge can be applied in fields such as drug design, health supplements, gene therapy, proteomics and agriculture.
Bioinformatics represents an interdisciplinary branch for developing improved methods for retrieving, analyzing, storing, and organizing the biological data. It focuses on the development of algorithms and software for the transfer, storage, analysis, and development of genomics databases.
Machine Learning (ML) belongs to the branch of computer science that provides self-learning capability to the machines without explicit programming. The ML algorithms are being extensively used for the tasks of prediction, classification, and feature selection in bioinformatics. The ML approaches are very good for solving problems such as distinguishing between DNA sequences and classification of DNA sequences. Currently, the ML in bioinformatics has become significant due to the advent of Deep Learning.
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Statistical Modelling and Machine Learning Principles for Bioinformatics Techniques, Tools, and Applications 
