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Predicting Sequence Specificities of Transcription Factors with Self-Attention Sequence Modeling : 자기참조 모델링을 통한 전사인자의 서열 특이성 예측
DC Field | Value | Language |
---|---|---|
dc.contributor.advisor | 김선 | - |
dc.contributor.author | 안용주 | - |
dc.date.accessioned | 2019-05-07T03:19:22Z | - |
dc.date.available | 2019-05-07T03:19:22Z | - |
dc.date.issued | 2019-02 | - |
dc.identifier.other | 000000153984 | - |
dc.identifier.uri | https://hdl.handle.net/10371/150803 | - |
dc.description | 학위논문 (석사)-- 서울대학교 대학원 : 공과대학 컴퓨터공학부, 2019. 2. 김선. | - |
dc.description.abstract | Transcription factor plays crucial role in gene expression via regulating transcription process. To predict the sequence specificity of each transcription factor I propose a deep learning model, the AttendBind which employs k-mer embedding and self-attention sequence modeling approaches. The experimental results on real biophysical data show that the proposed method outperforms other deep learning methods, indicating that the self-attention sequence modeling is highly effective on this task. In addition to the given prediction task, the visualization of self-attention maps and top-3 frequency based analyses can provide useful information for interpreting the deep learning model and discovering scientific knowledge. | - |
dc.description.tableofcontents | Abstract i
Contents iii List of Figures v List of Tables viii Chapter 1 Introduction 1 Chapter 2 Methods 4 2.1 Data 4 2.2 Attention Mechanism 6 2.3 Transformer 8 2.4 AttendBind 11 Chapter 3 Results 18 3.1 Regression Results 18 3.2 Binary Classification Results 21 3.3 Attention Visualization and Motif Analysis 23 Chapter 4 Conclusion 27 References 31 요약 34 감사의 글 35 | - |
dc.language.iso | eng | - |
dc.publisher | 서울대학교 대학원 | - |
dc.subject.ddc | 621.39 | - |
dc.title | Predicting Sequence Specificities of Transcription Factors with Self-Attention Sequence Modeling | - |
dc.title.alternative | 자기참조 모델링을 통한 전사인자의 서열 특이성 예측 | - |
dc.type | Thesis | - |
dc.type | Dissertation | - |
dc.description.degree | Master | - |
dc.contributor.affiliation | 공과대학 컴퓨터공학부 | - |
dc.date.awarded | 2019-02 | - |
dc.identifier.uci | I804:11032-000000153984 | - |
dc.identifier.holdings | 000000000026▲000000000039▲000000153984▲ | - |
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