Publication Type : Conference Paper
Publisher : 2018 International Conference on Bioinformatics and Systems Biology (BSB)
Source : 2018 International Conference on Bioinformatics and Systems Biology (BSB) (2018)
Url : https://ieeexplore.ieee.org/document/8770699
Keywords : Encoding, Feature extraction, Long noncoding RNA (IncRNA), Non-coding RNA, Proteins, Radio frequency, Random forest, RNA, RNA secondary structure, RNAfold, Support Vector Machine, Support vector machines, Terms-Coding RNA, Tools
Campus : Coimbatore
School : School of Engineering
Department : Computer Science
Year : 2018
Abstract : Long non-coding RNAs are a distinctive class of non-coding RNAs of length greater than 200 nucleotides and no protein coding potential. LncRNA plays an important role in genetic and epigenetic regulation. Major studies reveal that IncRNAs are less conserved in their primary sequences and shows more functional characteristics at secondary structure level. The objective of this work is to identify an optimal sequencestructure combination for computational analysis of IncRNAs. We also propose a novel secondary structure quantization which consider the existence of various structure elements. The feature combinations when used as input to classification of IncRNAs from coding RNAs, significant improvement in the results were obtained.
Cite this Research Publication : C. M. Sreeshma, Manu Madhavan, and Gopakumar G., “Identification of Long Non-coding RNA from inherent features using Machine Learning Techniques”, 2018 International Conference on Bioinformatics and Systems Biology (BSB)2018 International Conference on Bioinformatics and Systems Biology (BSB). 2018.