Journal
FRONTIERS IN GENETICS
Volume 8, Issue -, Pages -Publisher
FRONTIERS MEDIA SA
DOI: 10.3389/fgene.2017.00096
Keywords
gene regulatory network; gene coexpression; association measure; high-throughput data; bioinformatics
Categories
Funding
- National Natural Science Foundation of China (NSFC) [61572287, 61533011]
- Natural Science Foundation of Shandong Province, China [ZR2015FQ001]
- Fundamental Research Funds of Shandong University [2015QY001, 2016JC007]
- Scientific Research Foundation for the Returned Overseas Chinese Scholars, Ministry of Education of China
- Pilot Research Grant from School of Control Science and Engineering at Shandong University
Ask authors/readers for more resources
In this work, we provide a comparative study of the main available association measures for characterizing gene regulatory strengths. Detecting the association between genes (as well as RNAs, proteins, and other molecules) is very important to decipher their functional relationship from genomic data in bioinformatics. With the availability of more and more high-throughput datasets, the quantification of meaningful relationships by employing association measures will make great sense of the data. There are various quantitative measures have been proposed for identifying molecular associations. They are depended on different statistical assumptions, for different intentions, as well as with different computational costs in calculating the associations in thousands of genes. Here, we comprehensively summarize these association measures employed and developed for describing gene regulatory relationships. We compare these measures in their consistency and specificity of detecting gene regulations from both simulation and real gene expression profiling data. Obviously, these measures used in genes can be easily extended in other biological molecules or across them.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available