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研究生:張 申
研究生(外文):Chang, Shen
論文名稱:通過系統生物學和深度學習方法研究乙型肝炎病毒發病機理的核心信號通路,以進行生物標誌物鑑定和 藥物發現
論文名稱(外文):Investigating Core Signaling Pathways of Hepatitis B Virus Pathogenesis for Biomarkers Identification and Drug Discovery via Systems Biology and Deep Learning Method
指導教授:陳博現
指導教授(外文):Chen, Bor-Sen
口試委員:莊永仁藍忠昱王慧菁李征衛
口試委員(外文):Chuang, Yung-JenLan, Chung-YuWang, Hui-ChingLi, Cheng-Wei
口試日期:2020-12-22
學位類別:碩士
校院名稱:國立清華大學
系所名稱:電機工程學系
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2020
畢業學年度:109
語文別:英文
論文頁數:49
中文關鍵詞:乙肝病毒感染發病機制宿主/病原體種間遺傳和表觀遺傳網絡(HPI-GEN)系統藥物發現藥物-靶標相互作用(DTI)模型深度學習
外文關鍵詞:hepatitis B virus infectionpathogenesishost/pathogen interspecies genetic and epigenetic network (HPI-GEN)systems medicine discoverydrug-target interaction (DTI) modeldeep learning
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乙型肝炎病毒(HBV)感染是全世界發病率和死亡率的主要原因。然而,對其發病機理的了解不足通常會導致免疫逃逸和預後的複發。因此,基於資料探勘和全基因組RNA-seq數據的強效系統方法對於進一步分析致病機理和鑑定用於藥物設計的生物標記至關重要。在這項研究中,系統生物學方法被應用於通過雙向RNA序列數據修剪在HBV感染下的宿主/病原體遺傳和表觀遺傳相互作用網絡(HPI-GEN)中的假陽性。然後,通過主要網絡投影(PNP)方法和KEGG途徑的註釋,從核心串擾信號途徑中識別出與細胞功能障礙相關的重要生物標誌物作為藥物靶標。此外,基於預先訓練的基於深度學習的藥物-靶標相互作用(DTI)模型和從數據庫中獲得經驗證的藥理特性,即藥物調節能力,毒性和敏感性,將有前途的多靶點藥物組合設計為一種多分子藥物,為治療HBV感染創造了更多可能性。因此,通過擬議的系統醫學發現和重新定位程序,我們不僅闡明了乙肝病毒感染期間的病因機制,而且還有效地提供了治療乙肝的潛在藥物組合。
Hepatitis B Virus (HBV) infection is a major cause of morbidity and mortality worldwide. However, poor understanding of its pathogenesis often gives rise to intractable immune escape and prognosis recurrence. Thus, a valid systematic approach based on big data mining and genome-wide RNA-seq data is imperative to further investigate the pathogenetic mechanism and identify biomarkers for drug design. In this study, systems biology method was applied to trim false positives from the host/pathogen genetic and epigenetic interaction network (HPI-GEN) under HBV infection by two-side RNA-seq data. Then, via the principal network projection (PNP) approach and the annotation of KEGG (Kyoto Encyclopedia of Genes and Genomes) pathways, significant biomarkers related to cellular dysfunctions were identified from the core cross-talk signaling pathways as drug targets. Further, based on the pre-trained deep learning-based drug-target interaction (DTI) model and the validated pharmacological properties from databases, i.e., drug regulation ability, toxicity, and sensitivity, a combination of promising multi-target drugs was designed as a multiple-molecule drug to create more possibility for the treatment of HBV infection. Therefore, with the proposed systems medicine discovery and repositioning procedure, we not only shed light on the etiologic mechanism during HBV infection but also efficiently provided a potential drug combination for therapeutic treatment of Hepatitis B.
Contents
致謝 i
摘要 ii
Abstract iii
Contents iv
1. Introduction 1
2. Materials and Methods 2
2.1. The Construction of the HPI-GEN in HepaRG Cell Line during HBV Infection and the Application of Deep Learning-Based DTI Model for New Drug Design: An Overview 2
2.2. Big Data Mining and Data Preprocessing of RNA-seq Data for Human and Pathogen 2
2.3. Text Mining of Human and Pathogen Protein Interactions 3
2.4. Dynamic Models of Candidate HPI-GEN for Human Cells and HBV During the Infection 3
2.5. Parameter Estimation of the Dynamic Models of Candidate HPI-GEN by System Identification Approach 6
2.6. Determination of Significant Interaction Pairs 12
2.7. Extracting Core Network Structure from the Real HPI-GEN by Using PNP Approach 14
2.8. Deep Learning-Based Drug-Target Interaction Prediction 16
2.8.1. Data Preparation 16
2.8.2. Parameters Tuning Process Based on Deep Learning Algorithm 18
2.8.3. Measurement of Prediction Quality 19
3. Results 20
3.1. Overview of Systems Medicine Discovery Procedure 20
3.2. Extracting Core Signaling Pathways from Identified HPI-GEN and Core HPI-GEN in HBV Infection 26
3.3. Analysis of Core Interspecies Cross-Talk Pathways to Investigate Host/Pathogen Offensive/Defensive Mechanism during HBV Infection 26
3.3.1. Inception of HBV Infection 27
3.3.2. Liver Microenvironment and Immune Pathogenesis under HBV Infection 27
3.3.3. Toll Pathway as the First Line of Defense against Infection 27
3.3.4. TNF-α-Stimulated Signaling Pathways 28
3.3.5. TAK-STAT Signaling Pathways 29
3.3.6. TNFs-Induced Apoptotic Pathways 29
3.3.7. Virus Proteins-Induced Pathogenic Mechanism 30
3.4. Drugs Discovery and Repositioning Based on Selected Biomarkers for HBV 31
3.4.1. Deep Learning-Based Drug-Target Interaction Prediction Model 35
3.4.2. Systems Discovery and Design of the Multiple-Molecule Drug for HBV Infection 36
4. Discussion 39
4.1. Apoptosis in HBV Infection 39
4.2. Autophagy in HBV Infection 40
4.3. Inflammation and Innate Immune Response in HBV Infection 40
4.4. Discovery of Potential Drug Combination 41
5. Conclusions 41
References 42
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