Paper Info
Title
Big data analytics in bioinformatics: architectures, techniques, tools and issues.
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.
Year
DOI
Venue
2016
10.1007/s13721-016-0135-4
NetMAHIB
Keywords
Field
DocType
Big data, Bioinformatics, Machine learning, MapReduce, Clustering, Gene regulatory network
Data mining,Data dependency,Computer science,Complex data type,Batch processing,Artificial intelligence,Analytics,Cluster analysis,Computation,Bioinformatics,Big data,Machine learning,Salient
Journal
Volume
Issue
ISSN
5
1
2192-6670
Citations 
PageRank 
References 
6
0.39
99
Authors
5
Name
Order
Citations
PageRank
Hirak Kashyap1212.09
Hasin Afzal Ahmed2555.65
Nazrul Hoque3301.61
Swarup Roy45612.13
Dhruba Kumar Bhattacharyya5506.95