2019 年冠狀病毒病 (COVID-19) 流行病目前正在全球範圍內迅速蔓延。在 COVID-19 患者中，SARS-CoV-2 相關的急性呼吸窘迫綜合徵 (ARDS) 是導致高發病率和死亡率的主要因素。然而，SARS-CoV-2 相關的 ARDS 和非 SARS-CoV-2 相關的 ARDS 之間的臨床表現相當類似，由於其錯綜複雜的病理生理學尚未完全了解，因此其治療方法有限。在本研究中，為了研究 SARS-CoV-2 相關 ARDS 和非 SARS-CoV-2 相關 ARDS 的致病機制，我們首先通過數據庫挖掘構建了候選宿主-病原體種間全基因組遺傳和表觀遺傳網絡（HPI- GWGEN）。借助宿主-病原體 RNA 測序 (RNA-Seq) 數據、系統建模、系統識別和 Akaike 信息準則 (AIC)系統複雜度檢測法來刪除候選HPI-GWGEN中的誤報獲得 COVID-19 相關 ARDS 和非病毒性 ARDS 的真實 HPI-GWGEN。為方便分析，利用主網絡投影（PNP）方法提取核心HPI-GWGEN，COVID-19相關ARDS和非病毒性ARDS的核心訊號通路可以透過核心HPI-GWGEN的KEGG途徑註解建立。為了設計 COVID-19 相關 ARDS 和非病毒性 ARDS 的多分子藥物，我們通過比較 COVID-19 相關 ARDS 和非病毒性 ARDS 之間的核心信號通路，確定了重要生物標誌物以作為發病機制的藥物標靶。通過藥物-靶標相互作用數據庫提前進行訓練，基於深度神經網絡架構的藥物-靶標相互作用 (DNN-DTI) 模型可以預測已識別藥物標靶的候選藥物。我們通過藥物設計規範的過濾器，包括調節能力、敏感性、排泄、毒性和藥物相似性，進一步縮小了這些預測的候選藥物的範圍，老藥新用潛在的多分子藥物。總之，我們不僅啟發了 COVID-19 相關 ARDS 和非病毒性 ARDS 的病因機制，而且還為 COVID-19 相關性 ARDS 和非病毒性 ARDS 提供了新的治療選擇。

The coronavirus disease 2019 (COVID-19) epidemic is currently raging around the world at a rapid speed. Among COVID-19 patients, SARS-CoV-2-associated acute respiratory distress syn-drome (ARDS) is the main contribution to the high ratio of morbidity and mortality. However, clinical manifestations between SARS-CoV-2-associated ARDS and non-SARS-CoV-2-associated ARDS are quite common, and their therapeutic treatments are limited because the intricated pathophysiology having been not fully understood. In this study, to investigate the pathogenic mechanism of SARS-CoV-2-associated ARDS and non-SARS-CoV-2-associated ARDS, first, we constructed a candidate host-pathogen interspecies genome-wide genetic and epigenetic network (HPI-GWGEN) via database mining. With the help of host-pathogen RNA sequencing (RNA-Seq) data, real HPI-GWGEN of COVID-19-associated ARDS and non-viral ARDS were obtained by system modeling, system identification, and Akaike information criterion (AIC) model order selection method to delete the false positives in candidate HPI-GWGEN. For the convenience of mitigation, the principal network projection (PNP) approach is utilized to extract core HPI-GWGEN, and then the corresponding core signaling pathways of COVID-19-associated ARDS and non-viral ARDS are annotated via their core HPI-GWGEN by KEGG pathways. In order to design multiple-molecule drugs of COVID-19-associated ARDS and non-viral ARDS, we identified essential biomarkers as drug targets of pathogenesis by comparing the core signal pathways between COVID-19-associated ARDS and non-viral ARDS. The deep neural network of the drug–target interaction (DNN-DTI) model could be trained by drug–target interaction databases in advance to predict candidate drugs for the identified drug targets. We further narrowed down these predicted drug candidates to repurpose potential multiple-molecule drugs by the filters of drug design specifications, including regulation ability, sensitivity, excretion, toxicity, and drug-likeness. Taken together, we not only enlighten the etiologic mechanisms under COVID-19-associated ARDS and non-viral ARDS but also provide novel therapeutic options for COVID-19-associated ARDS and non-viral ARDS.

致謝---- I
摘要---- II
Abstract---- IV
Content---- VI
Chapter 1 . Introduction---- 1
Chapter 2 . Material and methods---- 5
2.1 Preprocessing of Host-pathogen RNA-Seq Datasets and Construction of Candidate HPI-GWGEN by RNA-seq Pipeline and Big-data Mining.---- 5
2.2 Systematic Model Construction for the Candidate HPI-GWGEN of COVID-19-associated-ARDS and Non-Viral-ARDS Patients.---- 7
2.3 Parameter Estimation of Real HPI-GWGENs of COVID-19-associated-ARDS and Non-Viral-ARDS by System Identification, System Order Detection Methods, and RNA-Seq data.---- 11
2.4 Extracting Core HPI-GWGEN From Real HPI-GWGEN By Using the PNP Approach.---- 16
2.5 Data Preprocess for Deep Neuron-Network-Based Drug-Target Interaction Model in Multiple-Molecule Drug Design.---- 19
2.6 Parameters Tuning Process and Prediction Quality Measurement of DNN-Based Drug Target Interaction Model.---- 20
Chapter 3 . Results---- 24
3.1 Overview of Core HPI-GWGEN Construction and Drug Discovery Design for COVID-19-associated-ARDS And Non-Viral-ARDS By System Biology Approaches.---- 24
3.2 The Construction of Deep Neural Network as Drug–Target Interaction Model and Drug Specification Filters to Select Potential Small Compounds for Multiple-Molecule Therapies.---- 39
3.3 Discovery of Multiple-Molecule Drug Therapy of COVID-19-associated-ARDS and Non-Viral-ARDS.---- 43
Chapter 4 . Discussions---- 45
4.1 The Common Pathogenic Molecular Mechanism and Drug Target Selection between COVID-19-associated-ARDS and Non-Viral-ARDS.---- 45
4.2 The Specific Pathogenic Molecular Mechanism and Drug Target Selection of COVID-19-associated-ARDS.---- 47
4.3 The Specific Pathogenic Molecular Mechanism and Drug Target Selection of Non-Viral-ARDS.---- 53
4.4 Systems Biology Approaches and Potential Drug Combination for COVID-19-associated-ARDS and Non-Viral-ARDS.---- 54
Chapter 5 . Conclusion---- 58
Appendix---- 60
Appendix: Tables---- 60
Appendix: Figures---- 62
Reference---- 67

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