Mô hình xử lý hiệu quả dữ liệu biểu hiện gen

BỘ GIÁO DỤC VÀ ĐÀO TẠO TRƯỜNG ĐẠI HỌC CẦN THƠ HUỲNH PHƯỚC HẢI MƠ HÌNH XỬ LÝ HIỆU QUẢ DỮ LIỆU BIỂU HIỆN GEN LUẬN ÁN TIẾN SĨ CHUYÊN NGÀNH: HỆ THỐNG THÔNG TIN Mà NGÀNH 62480104 CẦN THƠ, 2019 BỘ GIÁO DỤC VÀ ĐÀO TẠO TRƯỜNG ĐẠI HỌC CẦN THƠ HUỲNH PHƯỚC HẢI MƠ HÌNH XỬ LÝ HIỆU QUẢ DỮ LIỆU BIỂU HIỆN GEN LUẬN ÁN TIẾN SĨ CHUYÊN NGÀNH: HỆ THỐNG THÔNG TIN Mà NGÀNH 62480104 NGƯỜI HƯỚNG DẪN KHOA HỌC PGS TS ĐỖ THANH NGHỊ TS NGUYỄN VĂN HỊA CẦN THƠ, 2019 L˝IC MÌN ” ho n th nh lu“n ¡n n y tỉi ¢ nh“n ữổc sỹ hữợng dÔn, quan tƠm, giúp ù nhiằt tnh t quỵ Thy Cổ, bn b v ngữới thƠn Tổi xin gòi lới cÊm ỡn chƠn th nh n: Thy PGS.TS ỉ Thanh Ngh v Thy TS Nguyn Vôn Hặa  tn tnh ch bÊo, hữợng dÔn, ng viản v to mồi iãu kiằn tt nhĐt cho tổi quĂ tr…nh håc t“p v nghi¶n cøu Thƒy, Cỉ v c¡c anh, chà cıa khoa Cỉng ngh» thỉng tin v Truy•n thổng, trữớng i hồc Cn Thỡ  cung cĐp thảm ki‚n thøc, t⁄o måi i•u ki»n cho tỉi v quan t¥m, hØ trỉ tỉi qu¡ tr…nh håc t“p Ban gi¡m hi»u tr÷íng ⁄i håc An Giang, Ban chı nhi»m Khoa Cổng nghằ thổng tin  to iãu kiằn tổi ữổc tham gia hồc nƠng cao trnh chuyản mổn, cĂc bn ỗng nghiằp  khổng ngng ng vi¶n v gióp ï tỉi suŁt thíi gian håc t“p Sau cịng tỉi xin ch¥n th nh c£m ìn sƠu sc n gia nh v ngữới thƠn  giúp ï, ºng vi¶n tỉi suŁt qu¡ tr…nh håc t“p v to iãu kiằn tt nhĐt tổi ho n th nh lun Ăn NCS Huýnh Phữợc HÊi ii TMT T Trong nhng nôm gn Ơy, ung thữ l mt nhng nguyản nhƠn h ng u gƠy tò vong trản to n th giợi Do õ, ng y c ng cõ nhiãu nghiản cứu ữổc thỹc hiằn tm gi£i ph¡p hi»u qu£ ch'n o¡n v i•u trà ung thữ Tuy nhiản, cho n vÔn cặn nhiãu thĂch thức nguyản nhƠn gƠy ung thữ liản quan n rŁi lo⁄n di truy•n ho°c sü thay Œi qu¡ tr…nh phĂt trin tỹ nhiản t b o PhƠn t ‰ch bi”u hi»n gen b‹ng c¡c mæ h…nh håc m¡y l cỉng cư m⁄nh m‡ ” x¡c ành c¡c thay Œi cıa c¡c t‚ b o c¡c i•u ki»n mỉi tr÷íng kh¡c C¡c mỉ h… nh håc m¡y cung c§p thỉng tin hœu ‰ch ” ch'n o¡n v iãu tr ung thữ Tuy nhiản, mổ hnh hồc mĂy phƠn lợp d liằu biu hiằn gen l d bà qu¡ khỵp dœ li»u bi”u hi»n gen câ s chiãu rĐt lợn v s lữổng mÔu nhọ PhƠn lợp d liằu cõ s chiãu lợn l mt 10 th¡ch thøc cıa håc m¡y hi»n ⁄i Trong lu“n Ăn, chúng tổi giÊi quyt cĂc vĐn ã n y bng cĂc õng gõp sau Ơy Thứ nhĐt, chúng tổi ã xuĐt mổ hnh rút trch c trững mợi hồc cĂc t nh nông tiãm 'n ca d liằu bi”u hi»n gen b‹ng m⁄ng nì-ron t‰ch ch“p s¥u (DCNN) CĂc c trững mợi ữổc rút trch bng DCNN cÊi thiằn ch nh xĂc phƠn lợp d liằu biu hi»n gen cıa c¡c cæng ngh» DNA Microarray v RNA-Seq K‚t qu£ thüc nghi»m cho th§y º ch‰nh x¡c cıa cĂc b phƠn loi ữổc cÊi thiằn dũng DCNN rót tr‰ch °c tr÷ng tł dœ li»u bi”u hi»n gen Bản cnh õ, chúng tổi cặn ã xuĐt phữỡng phĂp giÊi quyt cÊ hai thĂch thức ca phƠn lợp d liằu biu hiằn gen bng giÊi thut tông cữớng d liằu SMOTE t c trững mợi ữổc rút trch bng m⁄ng DCNN Gi£i thu“t SMOTE ÷ỉc dịng ” sinh dœ liằu tng hổp t cĂc c trững mợi ữổc rút tr ‰ch b‹ng m⁄ng DCNN Dœ li»u tŒng hỉp ÷ỉc sinh ữổc tông cữớng cho d liằu hồc v sò dửng cĂc giÊi thut phƠn lợp hiằn i phƠn loi Thứ hai, chúng tổi ã xuĐt mổ hnh tông cữớng d liằu cho phƠn lợp biu hiằn gen bng mng i khĂng sinh mÔu (GAN) Mng GAN ữổc xƠy dỹng phũ hổp vợi d liằu biu hiằn gen ” sinh dœ li»u tŒng hæp tł dœ li»u gŁc Mổ hnh ữổc kt hổp vợi cĂc giÊi thut phƠn lợp phƠn loi d liằu biu hiằn gen Kt quÊ thỹc nghiằm cho thĐy mổ hnh ã xuĐt cÊi thi»n ÷ỉc º ch‰nh iii x¡c cıa c¡c gi£i thu“t gỗm k lĂng giãng, cƠy quyt nh, mĂy hồc vc-tỡ hỉ trổ v rng ngÔu nhiản Thứ ba, chúng tổi ã xuĐt mổ hnh hổp cĂc cƠy xiản phƠn ngÔu nhiản ỡn giÊn (RODS) dỹa trản mĂy hồc vc-tỡ hỉ trổ (SVM) phƠn lợp hiằu quÊ d liằu biu hiằn gen ị tững chnh l kt hổp nhiãu cƠy xiản phƠn ngÔu nhiản ỡn giÊn theo hữợng tip c“n Bagging v Boosting Chóng tỉi x¥y düng t“p hỉp cĂc cƠy xiản phƠn ngÔu nhiản ỡn giÊn dỹa trản siảu phflng ti ữu thu ữổc t huĐn luyằn SVM Kt quÊ thỹc nghiằm trản cho thĐy mổ hnh ã xuĐt hiằu quÊ hỡn cĂc giÊi thut khĂc gỗm k lĂng giãng, cƠy quyt nh, mĂy hồc vc-tỡ hỉ trổ, rng ngÔu nhiản, bagging v adaboost phƠn lợp trỹc tip trản s chiãu gc Ngo i ra, mổ hnh ã xuĐt cụng cÊi thiằn ữổc chnh xĂc ca mổ hnh phƠn lợp kt hổp vợi cĂc k thut tông cữớng d liằu bng mng GAN v rút tr‰ch °c tr÷ng b‹ng m⁄ng DCNN Tł khâa: dœ li»u biu hiằn gen, mổ hnh phƠn lợp, mng nỡ-ron tch chp sƠu, mng i khĂng sinh mÔu, mổ hnh hổp cĂc cƠy xiản phƠn ngÔu nhiản ỡn giÊn, mĂy håc v†c-tì hØ trỉ iv ABSTRACT In recent years, cancer is leading cause of death worldwide Therefore, more and more studies have been conducted which aim to improve the ability to discover cancers earlier and to diagnose them more accurately than was the case only a few years ago However, there are still many challenges in cancer treatment because the most common causes of cancer are genetic disorders and epigenetic alterations in the cells Gene expression is an exceptionally powerful tool for identifying changes in cells between different environmental conditions or developmental stages It is able to provide benefit information that is used to explore and diagnose disease Gene expression data classi-fication models play a key role to address the fundamental problems relat-ing to cancer Nevertheless, these models can easy overfiting because of the very-highdimensional and small-sample-size issues Classifying gene expres-sion data is a challenge in the field of machine learning In this dissertation we are interested in tackling these issues with the following contributions Firstly, we propose a new feature extraction model to learn latent fea-tures from gene expression data using deep convolutional neural network (DCNN) This model improves the classification accuracy of gene expression on both RNA-Seq and DNA-Microarray platforms Experiment results show that DCNN is effective to extract features from gene expression data On the other hand, we also propose a combined enhancing and extraction method to address both challenges of classification models using gene expression data In this approach, SMOTE algorithm generates new data from features extracted by DCNN These models are used in conjunction with various classifiers that efficiently classify gene expression data Secondly, we propose a new enhancing gene expression data model with generative adversarial network (GAN).GAN is implemented to generate synthetic data from original training datasets, which is used in conjunction with various classifiers to predict gene expression data Numerical test results show that our proposed model improve the classification accuracy of algorithms including support vector machines, k nearest neighbors and random forests v Finally, we investigate random ensemble oblique decision stumps (RODS) based on linear support vector machine (SVM) that is suitable for classify-ing very-high-dimensional microarray gene expression data Our classification algorithms (called Bag-RODS and Boost-RODS) learn multiple oblique decision stumps in the way of bagging and boosting to form an ensemble of classifiers more accurate than single model Numerical test results show that our proposed algorithms are more accurate than the-state-of-the-art classifica-tion models, including k nearest neighbors, support vector machines, decision trees and ensembles of decision trees like random forests, bagging and ad-aboost In addition, these models also improve the classification accuracy by combined with enhancing data model using the GAN and feature extraction model using DCNN Key words: gene expression data, classification, deep convolutional neural network, generative adversarial network, random ensemble oblique decision stumps, support vector machines vi MÖC LÖC L˝IC MÌN T´MT T ii iii ABSTRACT v MÖCLÖC vii DANHMÖCC CHNHV xii DANHMƯCC CB NGBI U xiv CH×ÌNG GI˛I THI U 1.1 T‰nh c§p thi‚t cıa lu“n ¡n 1.2 Mửc tiảu, i tữổng, phm vi v phữỡng phĂp nghiản cứu 1.3 Nhiằm vử v hữợng tip cn ca lun Ăn 1.3.1 Nghiản cứu xƠy dỹng mổ h…nh rót tr‰ch °c tr÷ng cho dœ li»u bi”u hi»n gen 1.3.2 Nghiản cứu xƠy dỹng mổ hnh tông c÷íng dœ li»u cho dœ li»u bi”u hi»n gen 1.3.3 Nghiản cứu xƠy dỹng mổ hnh phƠn lợp hiằu qu£ dœ li»u bi”u hi»n gen 1.4 C¡c âng gâp cıa lu“n ¡n 1.5 BŁ cöc cıa lu“n ¡n CHìèNG Cè S Lị THUY T V C C C˘NG TR NH LI N QUAN 11 2.1 Dœ li»u bi”u hi»n gen 11 2.2 Mæ hnh phƠn lợp d liằu biu hiằn gen 15 2.2.1 Ph¡t bi”u b i to¡n 15 2.2.2 ¡nh gi¡ mæ h…nh 16 2.2.3 Dœ li»u thüc nghi»m 18 2.3 C¡c nghi¶n cøu li¶n quan 24 2.3.1 Mỉ h…nh k l¡ng gi•ng 24 2.3.2 Mỉ h…nh c¥y quy‚t ành 25 2.3.3 M¡y håc v†c-tì hØ træ 26 vii 2.3.4 Ph÷ìng ph¡p t“p hỉp mỉ h…nh 2.3.5 Mỉ h…nh m⁄ng nì-ron nh¥n t⁄o 30 32 2.3.6 C¡c mæ h…nh håc s¥u 33 2.4 Th£o lu“n c¡c nghi¶n cøu li¶n quan 36 2.5 K‚t ch÷ìng 38 CH×ÌNG M˘ H NH RĨT TR CH C TRìNG CHO DLI UBI UHI NGEN 39 3.1 Giợi thiằu 39 3.2 Mỉ h…nh m⁄ng nì-ron t‰ch ch“p s¥u rót tr‰ch °c tr÷ng dœ li»u bi”u hi»n gen 41 3.2.1 Ki‚n tróc mỉ h…nh m⁄ng nì-ron t‰ch ch“p s¥u rót tr‰ch °c tr÷ng cho dœ li»u bi”u hi»n gen 41 3.2.2 Qu¡ tr…nh rót tr‰ch °c tr÷ng 44 3.2.3 CĂc giÊi thut phƠn lợp c tr÷ng ÷ỉc rót tr‰ch 49 3.3 K‚t qu£ thüc nghi»m 50 3.3.1 Kt quÊ phƠn lợp d liằu biu hiằn gen DNA Microarray 51 3.3.2 Kt quÊ phƠn lợp d liằu biu hi»n gen RNA-Seq 62 3.3.3 K‚t qu£ phƠn lợp d liằu biu hiằn gen RNA-Seq lợn 68 3.4 K‚t ch÷ìng CH×ÌNG 70 M˘ H NH T NG C×˝NG M U C TR×NG RĨT TR CH B NG SMOTE 4.1 Giỵi thi»u 71 71 4.2 Tông cữớng mÔu bng SMOTE dỹa v o c trững rót tr‰ch cıa dœ li»u bi”u hi»n gen 4.3 K‚t qu£ thüc nghi»m 73 76 4.3.1 Dœ li»u thüc nghi»m 76 4.3.2 Thi‚t l“p tham sŁ c¡c mæ h…nh 76 4.3.3 Kt quÊ phƠn lợp 78 4.4 K‚t ch÷ìng CH×ÌNG 89 M˘ H NH T NG C×˝NG DÚ LI U CHO DÚ LI UBI UHI NGEN 90 5.1 Giỵi thi»u 90 5.2 Mæ h…nh tông cữớng mÔu cho d liằu biu hiằn gen 92 viii C C CNG TR NH CNG Bă [CT1] Phuoc-Hai Huynh, Van Hoa Nguyen, Thanh-Nghi Do, "Novel hybrid DCNN-SVM model for classifying RNA-Sequencing gene expression data", Journal of Information and Telecommunication (JIT), Taylor & Francis, 3:4, pp 533-547, 2019 [CT2] Phuoc-Hai Huynh, Van Hoa Nguyen, Thanh-Nghi Do, "Enhancing gene expression classification of support vector machines with generative ad-versarial networks", Journal of Information and Communication Convergence Engineering (JICCE), Vol 17, pp 14-20, 2019 (SCOPUS) [CT3] Phuoc-Hai Huynh, Van Hoa Nguyen, Thanh-Nghi Do, "So s¡nh c¡c mỉ h…nh håc s¥u v c¡c kÿ thu“t håc tỹ ng khĂc phƠn lợp d liằu biu hiằn gene microarray", in proc of the 10th National Conference on Fundamental and Applied Information Technology Research (FAIR’10), pp 841- 850, 2017 [CT4] Phuoc-Hai Huynh, Van Hoa Nguyen, Thanh-Nghi Do, "A Coupling Support Vector Machines with the Feature Learning of Deep Convolutional Neural Networks for Classifying Microarray Gene Expression Data", in proc of the 10th Asian Conference on Intelligent Information and Database Systems (ACIIDS), Springer, pp 233-243, 2018 [CT5] Phuoc-Hai Huynh, Van Hoa Nguyen, Thanh-Nghi Do, "Random ensemble oblique decision stumps for classifying gene expression data",in proc of International Symposium on Information and Communication Technology 2018, Association for Computing Machinery (SoICT), pp 137-144, 2018 [CT6] Phuoc-Hai Huynh, Van Hoa Nguyen, Thanh-Nghi Do,"A 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