Big Data Analytics in Bioinformatics: A Machine Learning Perspective

TL;DR

This paper reviews how machine learning and big data technologies are applied to bioinformatics, highlighting current challenges, recent advances, and future research directions for handling large, complex biological datasets.

Contribution

It provides a comprehensive overview of big data analytics methods and architectures tailored for bioinformatics, identifying gaps and proposing future research opportunities.

Findings

Parallel and incremental machine learning algorithms are increasingly used in bioinformatics.

Graph-based architectures and in-memory tools help optimize iterative processing.

There is a lack of standard big data tools for key bioinformatics problems.

Abstract

Bioinformatics research is characterized by voluminous and incremental datasets and complex data analytics methods. The machine learning methods used in bioinformatics are iterative and parallel. These methods can be scaled to handle big data using the distributed and parallel computing technologies. Usually big data tools perform computation in batch-mode and are not optimized for iterative processing and high data dependency among operations. In the recent years, parallel, incremental, and multi-view machine learning algorithms have been proposed. Similarly, graph-based architectures and in-memory big data tools have been developed to minimize I/O cost and optimize iterative processing. However, there lack standard big data architectures and tools for many important bioinformatics problems, such as fast construction of co-expression and regulatory networks and salient module identification, detection of complexes over growing protein-protein interaction data, fast analysis of massive DNA, RNA, and protein sequence data, and fast querying on incremental and heterogeneous disease networks. This paper addresses the issues and challenges posed by several big data problems in bioinformatics, and gives an overview of the state of the art and the future research opportunities.