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Long Noncoding RNA Identification using Recurrent Neural Network : 순환신경망을 이용한 lncRNA 판별
DC Field | Value | Language |
---|---|---|
dc.contributor.advisor | 윤성로 | - |
dc.contributor.author | 백정환 | - |
dc.date.accessioned | 2017-10-31T08:34:30Z | - |
dc.date.available | 2017-10-31T08:34:30Z | - |
dc.date.issued | 2017-08 | - |
dc.identifier.other | 000000145500 | - |
dc.identifier.uri | https://hdl.handle.net/10371/138102 | - |
dc.description | 학위논문 (석사)-- 서울대학교 대학원 자연과학대학 협동과정 생물정보학전공, 2017. 8. 윤성로. | - |
dc.description.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. | - |
dc.description.tableofcontents | Chapter 1 Introduction 1
Chapter 2 Background 5 2.1 Long noncoding RNA (lncRNA) 5 2.2 Convolutional Neural Network (CNN) 7 2.3 Recurrent Neural Network (RNN) 7 Chapter 3 Proposed methodology 10 3.1 Bucketing 12 3.2 Detecting an ORF Indicator 12 3.3 Encoding Sequences 13 3.4 Learning lncRNAs 14 Chapter 4 Results 18 4.1 Datasets 18 4.2 Performance Comparison of Hyperparameter Variations 20 4.3 Performance Comparison between Tools 22 4.3.1 Performance Comparison in the Human Dataset 24 4.3.2 Performance Comparison in a Cross-species Dataset 24 Chapter 5 Discussion 25 Chapter 6 Conclusion 27 Bibliography 28 국문초록 35 | - |
dc.format | application/pdf | - |
dc.format.extent | 2101126 bytes | - |
dc.format.medium | application/pdf | - |
dc.language.iso | en | - |
dc.publisher | 서울대학교 대학원 | - |
dc.subject | Long noncoding RNA (lncRNA) | - |
dc.subject | Recurrent Neural Network (RNN) | - |
dc.subject | Deep Learning | - |
dc.subject.ddc | 574.8732 | - |
dc.title | Long Noncoding RNA Identification using Recurrent Neural Network | - |
dc.title.alternative | 순환신경망을 이용한 lncRNA 판별 | - |
dc.type | Thesis | - |
dc.contributor.AlternativeAuthor | Baek Junghwan | - |
dc.description.degree | Master | - |
dc.contributor.affiliation | 자연과학대학 협동과정 생물정보학전공 | - |
dc.date.awarded | 2017-08 | - |
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