A Brief Overview of Applications of Machine Learning in Life Sciences

Abstract

Biology or the science of life is immensely diverse and immense in its complexity and processes and interaction with the environment.

Despite diverse applications of conventional methods, tools and resources to decipher the complexity in life forms, encompassing their development, evolution, function, biological processes, ecology, behavior, infection and disease biology their remains, intriguing questions in Biology that remain hitherto unanswered. Thus along with the conventional methods, high throughput technology of sequencing and omics based approaches encompassing genomics, proteomics, transcriptomics, metabolomics, metagenomics, microarray technology are being applied to biological sources to understand the complex processes. This is leading to the generation of high dimensional data. The disciplines of Biostatistics and Computational Biology have become relevant and effective in understanding the processes and discover new interactions. Computational Biology uses Machine learning algorithms to analyze the biological problems. Biological data captured through different measurements form the training database to the machine learning algorithm system to learn and create the knowledge. In this chapter we briefly introduce the learners to the domain of machine learning with its application in Biological Sciences.