NDLI: Big data analytics in bioinformatics: architectures, techniques, tools and issues

Content Provider	Springer Nature Link
Author	Bhattacharyya, Dhruba Kumar Ahmed, Hasin Afzal Kashyap, Hirak Hoque, Nazrul Roy, Swarup
Copyright Year	2016
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, standard big data architectures are still lacking. Also appropriate tools are not available 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.
Starting Page	1
Ending Page	28
Page Count	28
File Format	PDF
ISSN	21926662
Journal	Network Modeling Analysis in Health Informatics and Bioinformatics
Volume Number	5
Issue Number	1
e-ISSN	21926670
Language	English
Publisher	Springer Vienna
Publisher Date	2016-09-01
Publisher Place	Vienna
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Computational Biology/Bioinformatics Machine learning Gene regulatory network Health Informatics Big data Clustering Bioinformatics MapReduce Applications of Graph Theory and Complex Networks
Content Type	Text
Resource Type	Article
Subject	Urology

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