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系統識別號 U0026-1702202007550200
論文名稱(中文) 使用異質卷積神經網路預測抗微生物肽
論文名稱(英文) Using Heterogeneous Convolutional Neural Network to Predict Antimicrobial Peptide
校院名稱 成功大學
系所名稱(中) 電機工程學系
系所名稱(英) Department of Electrical Engineering
學年度 108
學期 1
出版年 109
研究生(中文) 沈柏妤
研究生(英文) Po-Yu Shen
學號 N26070130
學位類別 碩士
語文別 中文
論文頁數 39頁
口試委員 指導教授-張天豪
口試委員-吳謂勝
口試委員-解巺評
口試委員-陳健生
口試委員-高宏宇
中文關鍵字 卷積神經網路  深度學習  抗微生物肽 
英文關鍵字 Convolutional Neural Network  Deep Learning  Antimicrobial Peptide 
學科別分類
中文摘要 抗微生物肽 (Antimicrobial Peptide),又稱為宿主防禦胜肽 (Host Defense Peptide),廣泛存在於各種生物體內,在免疫反應中扮演著重要的角色。抗微生物肽是強效的廣譜 (broad spectrum) 抗生素,生物實驗已證明抗微生物肽可殺死革蘭氏陰性菌、革蘭氏陽性菌、病毒、真菌,甚至是癌細胞,抗微生物肽可以對抗已對現有抗生素產生抗藥性的病原體,因此有望發展為新型治療劑。
近年來抗微生物肽研究有大幅的進展,其中有許多利用機器學習預測抗微生物肽的論文。這些論文大部分是依據胜肽的物理化學特性 (physicochemical properties) 來設計輸入機器學習模型的特徵值,然而這樣的方式需要大量的生醫專業知識,且容易侷限於目前人類對於抗微生物肽機制的理解。隨著深度學習的發展,開始有不少研究將深度學習應用在蛋白質序列上,讓神經網路自行學習蛋白質序列的規律。
本研究提出一個異質卷積神經網路模型以預測抗微生物肽,此模型結合了三個進階卷積神經網路架構,分別是 Deep Residue Network (ResNet)、Densely Connected Network (DenseNet) 以及Squeeze-and-Excitation Network (SENet),在與其他相關研究比較後,此模型在基準資料集 (Xiao 資料集) 上達到了最好的準確度 (97.6%)、曲線下面積 (99.6%) 以及馬修斯相關係數 (95.2%)。
英文摘要 Antimicrobial peptides (AMPs), also called host defense peptides (HDPs) are part of the innate immune response found among all classes of life. These peptides have a broad spectrum of targets including bacteria, viruses, and fungi. AMPs can kill pathogens that have developed resistance to existing antibiotics. Therefore, antimicrobial peptides demonstrate the potential as novel therapeutic agents.
In recent years, researches concerning AMP prediction have come a long way. Most of the researches take physicochemical properties as input. However, this way highly depends on domain knowledge and may be limited to human understanding of antimicrobial peptides. With the development of deep learning in recent years, many studies applied deep learning to researches on protein sequences. Deep learning models are able to extract important features from raw protein sequences automatically and learn the law of protein sequences.
In this work, we combined three advanced Convolutional Neural Networks (CNNs), including Deep Residue Network (ResNet), Densely Connected Convolutional Network (DenseNet), and Squeeze-and-Excitation Network (SENet), and proposed a heterogeneous CNNs for AMP prediction. When compared to other related works on benchmark dataset (Xiao dataset), the proposed model reached the best accuracy (97.6%), area under ROC curve (99.6%), and Matthews correlation coefficient (95.2%).
論文目次 第一章 緒論 (1)
第二章 相關研究 (3)
2.1 抗微生物肽 (ANTIMICROBIAL PEPTIDE) (3)
2.2 抗微生物肽預測研究 (4)
2.2.1 iAMP-2L (4)
2.2.2 iAMPpred (5)
2.2.3 AmPEP (6)
2.2.4 AMP Scanner (8)
2.3 卷積神經網路 (CONVOLUTIONAL NEURAL NETWORK , CNN) (8)
2.3.1 卷積層 (Convolutional Layer) (9)
2.3.2 平均池化層 (Average Pooling Layer) (10)
2.3.3 全域平均池化層 (Global Average Pooling Layer) (11)
2.3.4 全連接層 (Fully Connected Layer, FC) (11)
2.3.5 Deep Residue Network (ResNet) (12)
2.3.6 Densely Connected Convolutional Network (DenseNet) (13)
2.3.7 Squeeze-and-Excitation Network (SENet) (14)
第三章 研究方法 (16)
3.1 資料集 (16)
3.2 資料前處理 (18)
3.3 資料編碼 (18)
3.4 模型訓練與驗證流程 (19)
3.5 神經網路模型 (20)
第四章 研究結果 (24)
4.1 評估標準 (24)
4.2 與其他方法之比較 (24)
4.3 模型各部分重要性 (26)
第五章 討論 (29)
5.1 探討循環神經網路與卷積神經網路穩定性 (29)
5.2 探討 SE BLOCK 數量對模型效能的影響 (30)
5.3 模型視覺化 (34)
5.3.1 結合位點 (35)
5.3.2 真陽性樣本上氨基酸帶電性影響 (36)
5.3.3 真陰性樣本上著重部分探討 (36)
第六章 結論 (38)
6.1 結論 (38)
6.2 未來展望 (38)
參考文獻 (39)
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3. Thomas, S., et al., CAMP: a useful resource for research on antimicrobial peptides. Nucleic acids research, 2009. 38(suppl_1): p. D774-D780.
4. Xiao, X., et al., iAMP-2L: a two-level multi-label classifier for identifying antimicrobial peptides and their functional types. Analytical biochemistry, 2013. 436(2): p. 168-177.
5. Meher, P.K., et al., Predicting antimicrobial peptides with improved accuracy by incorporating the compositional, physico-chemical and structural features into Chou’s general PseAAC. Scientific reports, 2017. 7: p. 42362.
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7. Veltri, D., U. Kamath, and A. Shehu, Deep learning improves antimicrobial peptide recognition. Bioinformatics, 2018. 34(16): p. 2740-2747.
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17. de Paula, V. and A. Valente, A Dynamic Overview of Antimicrobial Peptides and Their Complexes. Molecules, 2018. 23(8): p. 2040.
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