Multi-view learning and deep learning for heterogeneous biological data to maintain oral health Imangaliyev, Sultan

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1 UvA-DARE (Digital Academic Repository) Multi-view learning and deep learning for heterogeneous biological data to maintain oral health Imangaliyev, Sultan Link to publication Citation for published version (APA): Imangaliyev, S. K. (2016). Multi-view learning and deep learning for heterogeneous biological data to maintain oral health General rights It is not permitted to download or to forward/distribute the text or part of it without the consent of the author(s) and/or copyright holder(s), other than for strictly personal, individual use, unless the work is under an open content license (like Creative Commons). Disclaimer/Complaints regulations If you believe that digital publication of certain material infringes any of your rights or (privacy) interests, please let the Library know, stating your reasons. In case of a legitimate complaint, the Library will make the material inaccessible and/or remove it from the website. Please Ask the Library: or a letter to: Library of the University of Amsterdam, Secretariat, Singel 425, 1012 WP Amsterdam, The Netherlands. You will be contacted as soon as possible. UvA-DARE is a service provided by the library of the University of Amsterdam ( Download date: 04 Mar 2017

Multi-View Learning and Deep Learning Sultan Imangaliyev

2 Sultan Imangaliyev Multi-View Learning and Deep Learning for Heterogeneous Biological Data to Maintain Oral Health Multi-View Learning and Deep Learning for Heterogeneous Biological Data to Maintain Oral Health Sultan Imangaliyev

3 Multi-view Learning and Deep Learning for Heterogeneous Biological Data to Maintain Oral Health Sultan K. Imangaliyev

Academisch Centrum Tandheelkunde Amsterdam Faculteit der Tandheelkunde van de Universiteit van Amsterdam en de Vrije

Free University of Amsterdam Department of Preventive Dentistry Netherlands Organisation for Applied Scientific Research

(TIFN) Theme Microbes and Function The study presented in this thesis was funded by Top Institute Food and Nutrition

Printing of this thesis was kindly supported by ACTA Onderzoeksinstituut. Copyright 2016 by Sultan K.

4 Academisch Centrum Tandheelkunde Amsterdam Faculteit der Tandheelkunde van de Universiteit van Amsterdam en de Vrije Universiteit Amsterdam Academic Centre for Dentistry Amsterdam (ACTA) Faculty of Dentistry of University of Amsterdam and Free University of Amsterdam Department of Preventive Dentistry Netherlands Organisation for Applied Scientific Research (TNO) TNO Earth, Life and Social Sciences Department of Microbiology and Systems Biology Top Institute Food and Nutrition (TIFN) Theme Microbes and Function The study presented in this thesis was funded by Top Institute Food and Nutrition (project number OH001), a public-private partnership in pre-competitive research on food and nutrition. Printing of this thesis was kindly supported by ACTA Onderzoeksinstituut. Copyright 2016 by Sultan K. Imangaliyev Cover image by: Chad Meserve ( Cover design by: GVO drukkers & vormgevers BV Printed and bound by: GVO drukkers & vormgevers BV ISBN/EAN:

5 Multi-view Learning and Deep Learning for Heterogeneous Biological Data to Maintain Oral Health ACADEMISCH PROEFSCHRIFT ter verkrijging van de graad van doctor aan de Universiteit van Amsterdam op gezag van de Rector Magnificus prof. dr. ir. K. I. J. Maex ten overstaan van een door het College voor Promoties ingestelde commissie, in het openbaar te verdedigen in de Aula der Universiteit op vrijdag 14 oktober 2016, te uur door Sultan Kairzhanovich Imangaliyev geboren te Gurev, Sovjet-Unie.

6 Promotiecommissie Promotores: Prof. dr. W. Crielaard Universiteit van Amsterdam Prof. dr. B. J. F. Keijser Universiteit van Amsterdam Co-promotor: Dr. E. Levin Universiteit van Amsterdam Overige leden: Prof. dr. B. G. Loos Universiteit van Amsterdam Prof. dr. A. H. Zwinderman Universiteit van Amsterdam Prof. dr. M. Nieuwdorp Universiteit van Amsterdam Prof. dr. T. M. Heskes Radboud Universiteit Nijmegen Prof. dr. D. Bogaert University of Sheffield Faculteit der Tandheelkunde

7 Table of contents 1 Prologue Data-driven research in life sciences A historical introduction to data-driven research in life sciences Data-driven research and hypothesis-driven research Data-driven research in oral health A novel approach in defining oral health Oral systems biology Computational challenges in analysis of -omics data Machine learning in oral health: a brief overview Supervised, unsupervised and semi-supervised machine learning Feature selection and regularization Multi-view learning and co-training Deep learning A guideline for the reader The datasets used in the thesis Outline of the thesis Online semi-supervised learning: algorithm and application in metagenomics Introduction and background Multi-output co-regularized algorithm Experiments Previous experimental results HMP dataset Conclusions Personalized microbial network inference via co-regularized spectral clustering Introduction and background Materials and methods Co-regularized spectral clustering algorithm Dataset description and preprocessing Personalized network inference and visualization Methods summary Results Clustering of individuals and defining groups with similar microbial composition I

8 3.3.2 Clustering of microbial species per cluster of similar individuals Personalized network visualization of bacterial species within each cluster Discussion Conclusions Unsupervised Multi-View Feature Selection via co-regularization: algorithm and application on oral metagenomics data Introduction and background The algorithm Empirical evaluation Conclusions Cancer type categorization and biomarker selection via unsupervised multiview algorithm Introduction and background Motivation Related work Materials and methods Mathematical formulation of the algorithm Datasets Experimental setup Results and discussion Synthetic datasets NCI-60 datasets Biological relevance and interpretation Critical analysis Conclusions On the ecosystemic network of saliva in healthy young adults Introduction and background Materials and methods S rrna gene amplicon sequencing and data processing Metabolome assessment Unsupervised machine learning Supervised feature selection Other statistical machine learning techniques Results The heterogeneity of the salivary microbiome The heterogeneity of the salivary metabolome The relation between the salivary microbiome and the biochemical salivary parameters The relation between the salivary metabolome and the biochemical salivary parameters The relation between the salivary metabolome and the salivary microbiome II

9 6.4 Discussion Conclusions Application of deep convolutional neural network to dental red fluorescent plaque images Introduction and background Materials and methods Convolutional Neural Network Dataset Ethics approval and consent to participate Plaque assessment and calculation of labels Experimental setup Results and discussion Model performance evaluation Advantages of the Deep Learning model Influence and visualization of convolutional filters Influence of multi-channel representation Influence of plaque assessment methods on classification performance Relevance of an automated classification system to a dental practice Limitations and possible solutions Conclusions Metagenomic biomarkers of dental plaque accumulation Introduction and background Materials and methods Dataset Ethics approval and consent to participate Stability selection Data pre-processing and setup for simulations Results and discussion Conclusions Epilogue Machine learning for oral health: a biological perspective Machine learning for oral health: a mathematical perspective Machine learning for life sciences: a future perspective A Summary 101 B Samenvatting 105 C Publications list 109 D Co-authorship list 111 E Financial justification of the research 115 III

10 F Word of thanks 117 G Curriculum vitae 121 Bibliography 123 IV

Style Characterization of Machine Printed Texts

Style Characterization of Machine Printed Texts Andrew D. Bagdanov This book is typeset by the author using L A TEX2 ε. The main body of the text is set using the Computer Modern family of fonts. The images