瀰漫性大B細胞淋巴瘤（DLBCL）是一種侵略性異質性疾病。最常見的亞型包括生髮中心b細胞（GCB）型，即DLBCL GCB和激活中心b細胞（ABC）型，即DLBCL ABC，它們代表由淋巴分化的不同階段引起的淋巴瘤。為了更深入地了解DLBCL亞型的發病機制，我們構建了遺傳和表觀遺傳網絡，通過系統生物學方法和通過患者微陣列數據和藥物-靶標相互作用數據庫進行的深度學習方法，研究DLBCL ABC和DLBCL GCB中的系統致癌機制，以確定其用於DLBCL多分子藥物發現的重要生物標記。通過系統生物學方法，通過大數據挖掘技術構建候選全基因組遺傳和表觀遺傳網絡（GWGEN），並通過系統識別和模型順序檢測方法，通過相應的微陣列數據鑑定DLBCL ABC和DLBCL GCB的真實GWGEN。從真正的GWGENs中提取核心信號通路，以便更輕鬆地研究導致DLBCL ABC和DLBCL GCB系統性發病的細胞功能障礙。接著我們比較了兩種核心信號通路，以確定共同的和特定的致癌生物標誌物作為治療DLBCL ABC和DLBCL GCB的藥物標靶。最後，通過藥物和標靶相互作用數據庫訓練用於藥物-標靶相互作用（DTI）模型的深度神經網絡，以選擇候選藥物來對接這些藥物標靶。此外，可調節性，藥物毒性和副作用被認為是用於選擇候選藥物以設計DLBCL ABC和DLBCL GCB的多分子藥物的藥物設計規格。

Diffuse large B cell lymphoma (DLBCL) is an aggressive heterogeneous disease. The most common subtypes include germinal center b-cell (GCB) type, i.e. DLBCL GCB and activation center b-cell (ABC) type i.e. DLBCL ABC, which represent lymphomas caused by different stages of lymphatic differentiation. To learn more about the pathogenesis of the DLBCL subtypes, we have constructed the genetic and epigenetic network to investigate systematic carcinogenic mechanisms in DLBCL ABC and DLBCL GCB to identify their significant biomarkers for multi-molecule drug discovery of DLBCL, by systems biology method and deep learning method via patient microarray data and drug-target interaction databases. By systems biology method, we construct candidate genome-wide genetic and epigenetic network (GWGEN) through big data mining technology, and identify real GWGENs of DLBCL ABC and DLBCL GCB by the corresponding microarray data via system identification and model order selection methods. The core signaling pathways are extracted from real GWGENs for easier investigation of cellular dysfunctions leading to the systematic pathogenesis of DLBCL ABC and DLBCL GCB. We then compared the two core signaling pathways to identify the common and specific carcinogenic biomarkers as drug targets to treat DLBCL ABC and DLBCL GCB. Finally, the deep neural network for drug-target interaction (DTI) model is trained through the drug and target interaction databases to select candidate drugs to dock these drug targets. Further, the regulability, drug toxicity and side effect are considered as drug design specifications for selecting candidate drugs to design multi-molecule drugs of DLBCL ABC and DLBCL GCB.

摘要................................................................................................................................ II
Abstract ........................................................................................................................ III
Content .........................................................................................................................IV
Chapter1 Introduction ................................................................................................. 1
Chapter 2. Results ........................................................................................................ 8
2.1 Investigation of core signaling pathways from identified GWGEN in
DLBCL ABC and DLBCL GCB ........................................................................ 8
Table 1. The total number of nodes and edges in candidate GWGENs and
identified GWGENs of DLBCL ABC and DLBCL GCB. ............................... 9
Table 2. The pathway enrichment analysis of proteins by applying the
DAVID in core GWGEN of DLBCL ABC ...................................................... 10
Table 3. The pathway enrichment analysis of proteins by applying the
DAVID in core GWGEN of DLBCL GCB ...................................................... 11
2.2 The Carcinogenic Molecular Mechanism in DLBCL ABC ..................... 12
Figure 1. The core signaling pathways are obtained by projecting core
GWGENs to KEGG pathways to investigate the carcinogenic progression
mechanism of DLCBL ABC type. .................................................................... 15
2.3 The Carcinogenic Molecular Mechanism in DLBCL GCB ..................... 16
Figure 2. The core signaling pathways are obtained by projecting core
GWGENs to KEGG pathways to investigate the carcinogenic progression
mechanism of DLBCL GCB type. .................................................................... 19
2.4 The Common Carcinogenic Molecular Mechanism between DLBCL
ABC and DLBCL GCB ..................................................................................... 20
Figure 3. The common and specific core signaling pathways of core
signaling pathways of DLBCL ABC and DLBCL GCB. ............................... 24
2.5 Select biomarkers as drug targets to design multi-molecule drugs for
DLBCL ABC and DLBCL GCB ...................................................................... 25
Table 4. The biomarkers (drug targets) identified for each of DLBCL ABC
and DLBCL GCB. .............................................................................................. 27
2.6 Overview of Systems Drug Discovery and Design for DLBCL ABC and
DLBCL GCB by Deep Learning and Data Mining Methods ........................ 27
Table 5. Potential multiple-molecule drug and the corresponding target
proteins for DLBCL ABC ................................................................................. 30
Table 6. Potential multiple-molecule drug and the corresponding target
proteins for DLBCL GCB ................................................................................. 31
Chapter 3. Materials and Methods ........................................................................... 31
V
3.1 Overview the procedure of construction for core genome-wide genetic
and epigenetic networks (GWGENs) and systems drug discovery and design
for DLBCL GCB and DLBCL ABC. ............................................................... 31
3.2 Constructing the candidate GWGENs through data mining and data
preprocessing. ..................................................................................................... 33
Chapter 4. Discussion ................................................................................................ 34
Chapter 5. Conclusion ............................................................................................... 37
Appendix. Materials and Methods ........................................................................... 38
A.1 Constructing the Stochastic System Models of the GWGEN to identify
real GWGEN of DLBCL GCB and DLBCL ABC. ......................................... 38
A.2 Using the system identification and system order analysis method by
microarray data to prune false positives of the candidate GWGEN. ........... 41
A.3 Extracting the core GWGENs from the real GWGENs by the principal
network projection (PNP) method. .................................................................. 47
A.4 Discovery and design of multi-molecule drug for DLBCL ABC and
DLBCL GCB ...................................................................................................... 51
Tables ........................................................................................................................... 59
Table 7. The Model performance of deep DTI model (10-fold cross
validation) ........................................................................................................... 59
Table 8. The candidate drugs identified for target FOXL1 and NFκB1 ...... 59
Table 9. The candidate drugs identified for target AKT1 and MYC ........... 60
Table 10. The candidate drugs identified for target STAT3 and EZH2 ....... 60
Table 11. The docking drug targets and not docking significant biomarkers
of core signaling pathways in Figure 3 by the candidate molecular drugs in
Table 8,9,10. The first column denotes the drug targets with docking by
corresponding candidate molecular drug. The second column denotes the
number of the proteins and genes in core signaling pathways in Figure 3,
which has no docking with the corresponding candidate molecular drugs to
be indicated as the side-effect of the corresponding candidate molecular
drug. .................................................................................................................... 61
FIGURES .................................................................................................................... 62
Figure A1. Flowchart of using systems biology method to construct
candidate GWGENs, real GWGENs, core GWGENs and core signaling
pathways of DLBCL ABC and DLBCL GCB and find significant genetic
and epigenetic biomarkers as drug targets for multiple-molecule drug
design. .................................................................................................................. 63
Figure A2. Flowchart of the proposed systems drug discovery and design
method ................................................................................................................. 65
VI
Figure A3. The real genome-wide genetic and epigenetic network (GWGEN)
of DLBCL ABC. ................................................................................................. 66
Figure A4. The real genome-wide genetic and epigenetic network (GWGEN)
of DLBCL GCB. ................................................................................................. 67
Figure A5. The core genome-wide genetic and epigenetic network
(GWGEN) of DLBCL ABC............................................................................... 68
Figure A6. The core genome-wide genetic and epigenetic network
(GWGEN) of DLBCL GCB .............................................................................. 69
Figure A7. The architecture of the DTI model based on deep neural network
.............................................................................................................................. 70
Figure A8. Training and Validation Learning curves (10-fold cross
validation). (A)Training and validation accuracy. (B) Training and
validation loss. .................................................................................................... 71
Figure A9. The ROC curve of different DTI models for predicting drugtarget
interaction. ............................................................................................... 72
Reference .................................................................................................................... 73

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