Composition, physicochemical property and base periodicity for discriminating lncRNA and mRNA.

ABSTRACT: Annotation of genome data with biological features is a challenging problem. One such problem deals with distinguishing lncRNA from mRNA. In this study, three groups of classification features, namely base periodicity, physicochemical property and nucleotide compositions were considered. We are attempting to propose a simple neural network model to obtain better results using judicious combination of the above said sequence features. Our approach uses balanced dataset, simple prediction model and use of limited features in distinguishing lncRNA and mRNA. Accordingly (a) two properties of base periodicity: peak power spectrum of the signal and noise-to-signal ratio (SNR) of this peak signal (b) three physicochemical properties: solvation, stacking and hydrogen-bonding energy and (c) all din

SUBMITTER: Rajesh P 

PROVIDER: S-EPMC10794758 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature