VIETNAM NATIONAL UNIVERSITY, HANOI UNIVERSITY OF ENGINEERING AND TECHNOLOGY LE HOANG QUYNH A HYBRID APPROACH TO FINDING PHENOTYPE CANDIDATES IN GENETIC TEXT MASTER THESIS Hanoi – 2012 LUAN VAN CHAT LUONG download : add luanvanchat@agmail.com VIETNAM NATIONAL UNIVERSITY, HANOI UNIVERSITY OF ENGINEERING AND TECHNOLOGY LE HOANG QUYNH A HYBRID APPROACH TO FINDING PHENOTYPE CANDIDATES IN GENETIC TEXT Major : Computer Science Code : 60 48 01 MASTER THESIS Supervisor: Assoc. Ha Quang Thuy Hanoi – 2012 LUAN VAN CHAT LUONG download : add luanvanchat@agmail.com A hybrid approach to finding phenotype candidates in genetic texts Le Hoang Quynh Faculty of Information Technology University of Engineering and Technology Vietnam National University, Hanoi Supervised by Associate Professor. Ha Quang Thuy A thesis submitted in fulfillment of the requirements for the degree of Master of Science in Computer Science November 2012 LUAN VAN CHAT LUONG download : add luanvanchat@agmail.com 2 LUAN VAN CHAT LUONG download : add luanvanchat@agmail.com ORIGINALITY STATEMENT ‘I hereby declare that this submission is my own work and to the best of my knowledge it contains no materials previously published or written by another person, or substan- tial proportions of material which have been accepted for the award of any other degree or diploma at University of Engineering and Technology (UET/Coltech) or any other educational institution, except where due acknowledgement is made in the thesis. Any contribution made to the research by others, with whom I have worked with at Univer- sity of Engineering and Technology and National Institute of Informatic (Tokyo, Japan) or elsewhere, is explicitly acknowledged in the thesis.
I also declare that the intellectual content of this thesis is the product of my own work, except to the extent that assistance from others in the project’s design and conception or in style, presentation and linguistic expression is acknowledged.’ Hanoi, November 10th , 2012 Signed. Le Hoang Quynh i LUAN VAN CHAT LUONG download : add luanvanchat@agmail.com ABSTRACT Named entity recognition (NER) has been extensively studied for the names of genes and gene products but there are few proposed solutions for phenotypes. Phe- notype terms are expected to play a key role in inferring gene function in complex heritable diseases but are intrinsically difficult to analyse due to their complex se- mantics and scale. In contrast to previous approaches we evaluate state-of-the-art techniques involving the fusion of machine learning on a rich feature set with evi- dence from extant domain knowledge-sources.
The techniques are validated on two gold standard collections including a novel annotated collection of 112 abstracts de- rived from a systematic search of the Online Mendelian Inheritance of Man database for auto-immune diseases. Encouragingly the hybrid model outperforms a HMM, a CRF and a pure knowledge-based method to achieve an F1 of 75.37 for BF and micro average F1 of 84.01 for the whole system. Publications: • Mai-Vu Tran, Tien-Tung Nguyen, Thanh-Son Nguyen, Hoang-Quynh Le. Automatic Named Entity Set Expansion Using Semantic Rules and Wrappers for Unary Relations.
In Inter- national Conference on Asian Language Processing 2010. Harbin, China; December 28-30, 2010, DOI: http://doi.73 • Hoang-Quynh Le, Mai-Vu Tran, Nhat-Nam Bui, Nguyen-Cuong Phan and Quang- Thuy Ha. An Integrated Approach Using Conditional Random Fields for Named En- tity Recognition and Person Property Extraction in Vietnamese Text. In Proceedings of International Conference on Asian Language Processing 2011.
DOI: http://doi.37 • Nigel Collier, Mai-Vu Tran, Hoang-Quynh Le, Anika Oellrich, Ai Kawazoe, Martin Hall- May and Dietrich Rebholz-Schuhmann. A hybrid approach to finding phenotype candidates in genetic text. In The 24th conference on Computational Linguistics (COLING 2012). Accepted as long paper.
ii LUAN VAN CHAT LUONG download : add luanvanchat@agmail.com ACKNOWLEDGEMENTS First and foremost, I would like to express my deep gratitude to my supervi- sor, Assoc. Ha Quang Thuy, for his patient guidance and continuous support throughout the years. He always appears when I need help, and responds to queries so helpfully and promptly. I would like to express my gratitude to the National Institute of Informatics (NII - Tokyo, Japan) for giving me a great chance working at NII in the NII International Internship program.
Then, I sincerely give my honest thanks and appreciation to Assoc. Collier, my internship supervisor at NII, for his great support. I would like to say thank you to all my teachers at university of Engineering and Technology (VNU), who bring me many knowledge and experiences. I also want to thank my colleagues at the Knowledge and Technology laboratory (UET, VNU) and my classmate for their enthusiasm and promptly help.
I sincerely acknowledge the Vietnam National University, NAFOSTED and the QG.38 project for some supporting finance to my master study. And thanks to all my friends who always be by my side and cheer me. Finally, this thesis would not have been possible without the support and love of my family. Thank you, mother and father.
Thanks brother and sister, thanks to my nephew. And thank you, my beloved husband. Again, thank you and love all of you so much ♥. iii LUAN VAN CHAT LUONG download : add luanvanchat@agmail.com Table of Contents 1 Introduction 1 1.1 Motivation and problem definition .3 The challenges of phenotype entity recognition .1 GENIA and JNLPBA corpora .2 The online mendelian inheritance in man .3 The human phenotype ontology .4 The mammalian phenotype ontology .5 The unified medical language system .1 Baseline method: Khordad et al.2 Annotated data sources .2 Machine learning labeler .3 Knowledge-based labeler.
25 4 Experimental results and evaluation 29 4.2 Experiments on the KMR corpus. 31 iv LUAN VAN CHAT LUONG download : add luanvanchat@agmail.com TABLE OF CONTENTS v 4.3 Experiments on the Phenominer corpus .1 Discussion on corpora .2 Discussion on results. 36 5 Conclusion 40 LUAN VAN CHAT LUONG download : add luanvanchat@agmail.com List of Figures 2.1 A visual example of HPO hierarchical structure .2 A visual example of MP hierarchical structure .3 Khordad et al. (2011)’s system block diagram .1 An informal overview of bodily feature entity .2 Phenotype tagging architecture .3 Brat rapid annotation tool example .1 Column chart shows the experimental results on KMR corpus .2 Column chart shows the experimental results of BF entities on Phe- nominer corpus .3 Column chart shows the experimental results of GGP entities on Phe- nominer corpus.
34 vi LUAN VAN CHAT LUONG download : add luanvanchat@agmail.com List of Tables 3.1 Referential semantics and scoping of mentions by entity type .2 List of auto-immune disease used to collect Phenominer corpus .3 Feature sets used in the machine learning labeler .4 Features exploited by the two learner models .1 Results for BF entity on the KMR corpus using models with partial matching .2 Results for each entity on the Phenominer corpus using models with partial matching .3 Sources of error by the Hybrid system on the KMR corpus.4 Sources of error by Khordad et al.’s system on the Phenominer corpus.5 Sources of error by the Hybrid system on the Phenominer corpus. 39 vii LUAN VAN CHAT LUONG download : add luanvanchat@agmail.com List of Abbreviations BF Bodily feature CRF Conditional Random Field GGP Gene and gene product HMM Hidden Markov Model HPO the Human Phenotype Ontology KB Knowledge-based ML Machine learning MP the Mammalian Phenotype Ontology NE Named entity NER Named entity recognition viii LUAN VAN CHAT LUONG download : add luanvanchat@agmail.com Chapter 1 Introduction 1.1 Motivation and problem definition During the last decade biomedicine has developed tremendously. Everyday a lot of biomedical papers are published and a great amount of information is produced. Due to the rapidly increasing amount of biomedical literature available on the Web, biomedical information extraction becomes more and more important.
Biomedical named entity recognition (NER) is a subtask of biomedical infor- mation extraction which is a fundamental step and can affect the results of others tasks. Biomedical NER is a computational technique used to identify and classify strings of text (mentions) that designate important concepts in biomedicine. As the first stage in the integrated semantic linking of knowledge between literature and structured databases it is critically important to maximize the effectiveness of this step. This thesis focuses on the analysis and identification of a new class of entity: phenotypes.
Follow Hoehndorf et al. (2010), phenotype is important for the analysis of the molecular mechanisms underlying disease; it is also expected to play a key role in inferring gene function in complex heritable diseases. Two thoughts motivate our work are: (1) The database curation community has expressed a wish for full text entity indexing and the inclusion of phenotypes (Dowell et al., 2009; Hirschman et al., 2012), and (2) Biomedicine is rapidly moving towards full-scale integration of data, opening up the possibility to understand complex heritable diseases caused by genes. Association studies involving phenotypes are considered important to making progress (Lage et al., 2007; Wu et al.
The ultimate goal of the work we present 1 LUAN VAN CHAT LUONG download : add luanvanchat@agmail. Phenotype definition 2 here is to allow relations mined from sentences such as the one we annotated below to feed into novel hypothesis generation procedures. From Ex 1, the reader can easily infer a relation between ‘IgG1 disorder’ and three genes/gene products marked as GGP. Among [patients]ORGAN ISM with [systemic lupus erythematosus]DISEASE ([SLE]DISEASE ), those with the [IgG1 disorder]P HEN OT Y P E have a higher prevalence of high titre [rheumatoid factor]GGP and [antinuclear antibody]GGP , but a lower prevalence of [anti-double-stranded DNA (anti-dsDNA) antibodies]GGP above 30 U/ml.2 Phenotype definition Unlike genes or anatomic structures, phenotypes and their traits are complex concepts and do not constitute a homogeneous class of objects (i.
Traits such as ‘eye colour’, ‘blood group’, ‘hemoglobin concentration’ or ‘facial gri- macing’ describe morphological structures, physiological processes and behaviours. When qualities or quantities of traits are used to describe a specific organism then we have phenotypic descriptions, e. ‘blue eyes’, ‘blood group AB’, ‘not having between 13 and 18 gm/dl hemoglobin concentration’. Until recently, there has been little effort to provide data integration standards for phenotypes.
This means that phenotypic descriptions tend to be author/study specific and biological results may go undiscovered if the terms used lie outside an author’s immediate research area (Bard and Rhee, 2004). In some researches, it is simply called as ‘phenotypic information’ and authors do not give any specific def- inition for it (Hoehndorf et al. In CSI-OMIM system (Cohen et al., 2011), phenotypes are considered as genetic terms including clinical signs and symptoms. Freimer and Sabatti (2003) describe phenotypes as referring to ‘any morphologic, biochemical, physiological or behavioral characteristic of an organism.
All phe- notypic characteristicsrepresent the expression of particular genotypes combined with the effects of specific environmental influences’. Khordad et al. (2011) defines phe- notypes as ‘genetically-determined observable characteristics of a cell or organism, including the result of any test that is not a direct test of the genotype.A pheno- type of an organism is determined by the interaction of its genetic constitution and the environment’. LUAN VAN CHAT LUONG download : add luanvanchat@agmail.
The challenges of phenotype entity recognition 3 Our definition of phenotype was taken from the formal analysis in Scheuermann et al. Definition: A phenotype entity is a (combination of) bodily features(s) of an organism determined by the interaction of its genetic make-up and environment. But Scheuermann et al. (2009) also define symptom as ‘a bodily feature of a patient that is observed by the patient or clinician and suspected of being caused by a disease’.
We can see an ambiguity made by the causality (or context) here: a term may be symptom in some contexts but refer to phenotype in others or many symptoms may be phenotypes. Thus, it is important to recognize that this phenotype definition requires us to know the underlying cause. Since causality is often difficult to establish using narrow contextual evidence of the sort used in NER it seems reasonable that we focus here on identifying bodily features themselves, i. phenotype candidates, and then determine causality in another stage of processing.