NDLI: RIPGA: RNA-RNA interaction prediction using genetic algorithm

Content Provider	IEEE Xplore Digital Library
Author	Kwan-Yau Cheung Kwok-Kit Tong Kin-Hong Lee Kwong-Sak Leung
Copyright Year	2013
Description	Author affiliation: Dept. of Comput. Sci. & Eng., Chinese Univ. of Hong Kong, Shatin, China (Kwan-Yau Cheung; Kwok-Kit Tong; Kin-Hong Lee; Kwong-Sak Leung)
Abstract	Non-coding RNAs are RNA molecules that do not translate into proteins. These RNAs are functional important in many biological processes. Their biological functions are highly related to their interaction partners. RNA-RNA interactions are one of the possibilities. It is desired to use computational methods to study and predict the interaction partners of non-coding RNAs. Most recent programs for RNA-RNA interaction prediction programs are based on Turner Energy Model. Some papers show that the free energy of RNA-structures is lower than random sequences but it is not statistically significant. It shows that we may need to modify the energy model for different RNA structures applications. In this paper, we first study the RNA-RNA interaction pattern using experimental validated RNA-RNA interaction data, which are extracted from sRNATarBase. We study the sRNA-mRNA interaction data and extract some features of the RNA-RNA interaction patterns. Then we combine these features about interaction sites into the Turner Energy Model. We develop a genetic algorithm based program RIPGA to solve the RNA-RNA interaction prediction problem. We use genetic algorithm because the RNA-RNA interaction prediction is NP-hard and we are interested to find out good suboptimal solutions. We use an sRNA-mRNA interaction dataset to evaluate the performance of the modified energy model and compare the results with two state-of-the-art programs. The comparison of the original model and the modified model shows that the modified energy model has better performance in both sensitivity and positive predictive value (PPV). Comparing RIPGA with state-of-the-art programs, RIPGA have better sensitivity and comparable positive predictive value.
Sponsorship	IEEE Comput. Intelligence Soc.
Starting Page	148
Ending Page	153
File Size	370515
Page Count	6
File Format	PDF
ISBN	9781467358750
DOI	10.1109/CIBCB.2013.6595401
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-04-16
Publisher Place	Singapore
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	RNA Predictive models Sensitivity Genetic algorithms Bonding Computational modeling Biological system modeling sRNA-mRNA interaction RNA-RNA interaction prediction genetic algorithm
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Libarray of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

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