SHERP

Long Noncoding RNA Identification using Recurrent Neural Network
순환신경망을 이용한 lncRNA 판별

Cited 0 time in webofscience Cited 0 time in scopus
Authors
백정환
Advisor
윤성로
Major
자연과학대학 협동과정 생물정보학전공
Issue Date
2017
Publisher
서울대학교 대학원
Keywords
Long noncoding RNA (lncRNA)Recurrent Neural Network (RNN)Deep Learning
Description
학위논문 (석사)-- 서울대학교 대학원 자연과학대학 협동과정 생물정보학전공, 2017. 8. 윤성로.
Abstract
Long noncoding RNAs (lncRNAs) are important regulatory elements in biological processes. LncRNAs share similar sequence characteristics with messenger RNAs (mRNAs), but they play completely different roles, thus providing novel insights for biological studies. The development of next-generation sequencing (NGS) has helped in the discovery of lncRNA transcripts. However, the experimental verification of numerous transcriptomes is time consuming and costly. To alleviate these issues, a computational approach is needed to distinguish lncRNAs from the transcriptomes.
We present a deep learning-based approach, lncRNAnet, to identify lncRNAs that incorporates recurrent neural networks (RNNs) for RNA sequence modeling and convolutional neural networks (CNNs) for detecting stop codons to obtain an open reading frame (ORF) indicator. LncRNAnet performed clearly better than the other tools for sequences of short lengths, on which most lncRNAs are distributed. In addition, lncRNAnet successfully learned features and showed 7.83%, 5.76%, 5.30%, and 3.78% improvements over the alternatives on a human test set (HT) in terms of specificity, accuracy, F1-score, and area under the curve (AUC), respectively.
Language
English
URI
http://hdl.handle.net/10371/138102
Files in This Item:
Appears in Collections:
College of Natural Sciences (자연과학대학)Program in Bioinformatics (협동과정-생물정보학전공)Theses (Master's Degree_협동과정-생물정보학전공)
  • mendeley

Items in S-Space are protected by copyright, with all rights reserved, unless otherwise indicated.

Browse