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From the third issue of 2017, Microarrays has changed its name to High-Throughput.

Open AccessReview
Microarrays 2015, 4(4), 596-617; doi:10.3390/microarrays4040596

An Overview of NCA-Based Algorithms for Transcriptional Regulatory Network Inference

1
Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX 77843, USA
2
Institute of Genomic Medicine, University of California San Diego, La Jolla, CA 92093, USA
*
Author to whom correspondence should be addressed.
Academic Editor: Heather J. Ruskin
Received: 1 September 2015 / Revised: 7 October 2015 / Accepted: 11 November 2015 / Published: 16 November 2015
(This article belongs to the Special Issue Computational Modeling and Analysis of Microarray Data: New Horizons)
View Full-Text   |   Download PDF [634 KB, uploaded 16 November 2015]   |  

Abstract

In systems biology, the regulation of gene expressions involves a complex network of regulators. Transcription factors (TFs) represent an important component of this network: they are proteins that control which genes are turned on or off in the genome by binding to specific DNA sequences. Transcription regulatory networks (TRNs) describe gene expressions as a function of regulatory inputs specified by interactions between proteins and DNA. A complete understanding of TRNs helps to predict a variety of biological processes and to diagnose, characterize and eventually develop more efficient therapies. Recent advances in biological high-throughput technologies, such as DNA microarray data and next-generation sequence (NGS) data, have made the inference of transcription factor activities (TFAs) and TF-gene regulations possible. Network component analysis (NCA) represents an efficient computational framework for TRN inference from the information provided by microarrays, ChIP-on-chip and the prior information about TF-gene regulation. However, NCA suffers from several shortcomings. Recently, several algorithms based on the NCA framework have been proposed to overcome these shortcomings. This paper first overviews the computational principles behind NCA, and then, it surveys the state-of-the-art NCA-based algorithms proposed in the literature for TRN reconstruction. View Full-Text
Keywords: gene; transcription factor; transcriptional regulatory network; network component analysis gene; transcription factor; transcriptional regulatory network; network component analysis
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Wang, X.; Alshawaqfeh, M.; Dang, X.; Wajid, B.; Noor, A.; Qaraqe, M.; Serpedin, E. An Overview of NCA-Based Algorithms for Transcriptional Regulatory Network Inference. Microarrays 2015, 4, 596-617.

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