KNowledge Acquisition and Representation Methodology (KNARM)

174 Pages·2017·10.14 MB·English

University of Miami
Scholarly Repository
Open Access Dissertations Electronic Teses and Dissertations
2018-05-06
KNowledge Acquisition and Representation
Methodology (KNARM) and Its Applications
Hande Küçük McGinty
University of Miami,
UNIVERSITY OF MIAMI
KNOWLEDGE ACQUISITION AND REPRESENTATION METHODOLOGY
(KNARM) AND ITS APPLICATIONS
By
Hande Küçük McGinty
A DISSERTATION
Submitted to the Faculty
of the University of Miami
in partial fulfillment of the requirements for
the degree of Doctor of Philosophy
Coral Gables, Florida
May 2018
⃝c 2018
Hande Küçük McGinty
All Rights Reserved
UNIVERSITY OF MIAMI
A dissertation submitted in partial fulfillment of
the requirements for the degree of
Doctor of Philosophy
KNOWLEDGE ACQUISITION AND REPRESENTATION METHODOLOGY
(KNARM) AND ITS APPLICATIONS
Hande Küçük McGinty
Approved:
Ubbo Visser, Ph.D. Hüseyin Koçak, Ph.D.
Associate Professor of Computer Professor of Computer Science
Science
Geoff Sutcliffe, Ph.D. Stefan Wuchty, Ph.D.
Professor of Computer Science Associate Professor of Computer
Science
Stephan Schürer, Ph.D. Guillermo Prado, Ph.D.
Associate Professor of Dean of the Graduate School
Molecular and Cellular Pharmacol-
ogy
KÜÇÜK MCGINTY, HANDE (Ph.D., Electrical and Computer Engineering)
KNowledge Acquisition and Representation Methodology (May 2018)
(KNARM) and Its Applications
Abstract of a dissertation at the University of Miami.
Dissertation supervised by Professor Ubbo Visser.
No. of pages in text. (156)
Technological advancements in many fields have led to huge increases in data
production, including data volume, diversity, and the speed at which new data is
becoming available. In accordance with this, there is a lack of conformity in the
ways data is interpreted. In-depth analyses making use of various data types and
data sources, and extracting knowledge has become one of the many challenges with
this big data. This is especially the case in life-sciences where simplification and
flattening of diverse data types often leads to incorrect predictions.
Effective applications of big data approaches in the life sciences require better,
knowledge-based, semantic models that are suitable as a framework for big data
integration, while avoiding overly extreme simplification, such as reducing various
biological data types to the gene level. A major challenge in developing such se-
mantic knowledge models, or ontologies, is the knowledge acquisition bottleneck.
Automated methods are still very limited and significant human expertise is re-
quired.
In this research, we describe a methodology to systematize this knowledge acqui-
sition and representation challenge, termed KNowledge Acquisition and Represen-
tation Methodology (KNARM). We also present how KNARM was applied on three
ontologies: BioAssay Ontology (BAO), LINCS FramEwork Ontology (LIFE) ,and
Drug Target Ontology (DTO) built for three different projects: BioAssay Ontology,
Library of Integrated Network-Based Cellular Signatures (LINCS), and Illuminating
the Druggable Genome (IDG), and how they work together in complex queries.
To My Family.
iii
Acknowledgements
First and foremost, I am most grateful to my advisor, Dr.Ubbo Visser who has
supported me throughout this research with his extensive knowledge, encourage-
ment, and patience, giving me confidence to develop different aspects of my research.
Without him, this thesis could not have been completed or written. In addition to
my advisor, I also would like to offer my sincere thanks to Dr. Hüseyin Koçak, Dr.
Geoff Sutcliffe, Dr. Stefan Wuchty, and Dr. Stephan Schürer for their encouragement
and insightful comments. I would like to acknowledge the Computer Science Depart-
ment, Center for Computational Science (CCS) and National Institute for Health
(NIH grants U54CA189205 (Illuminating the Druggable Genome Knowledge Man-
agement Center, IDG-KMC), U24TR002278 (Illuminating the Druggable Genome
Resource Dissemination and Outreach Center, IDG-RDOC), U54HL127624 (BD2K
LINCS Data Coordination and Integration Center, DCIC), and U01LM012630-02
(BD2K, Enhancing the efficiency and effectiveness of digital curation for biomedical
big data)) for their financial support during my studies and toward the conferences
I attended.
I am blessed to have such wonderful friends in and outside of the Computer
Science Department, who supported, helped, and encouraged me even during the
most difficult times of this period. I thank them all for everything, especially to
Hema Raju, Basar Koç, Prajwal Devkota, and Seminda Abetruwan for listening to
me in the office and their help in various parts of my research. I would like to thank
the UM Writing Center for their help with the editing of this dissertation.
My studies would not have been completed without the everlasting love, con-
stant support, and guidance of my parents Mahide and Yalçin Küçük, my brother
Yigit Küçük, my parents-in-law, my husband Jacob McGinty, and my wonderful son
William Sarp McGinty, who is the reason why I continued to finish this journey.
Hande Küçük McGinty
University of Miami
May 2018
iv
Table of Contents
LIST OF FIGURES ix
LIST OF TABLES xv
1 INTRODUCTION 1
2 RELATED RESEARCH 8
2.1 Reviews . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
2.2 Tools . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
2.3 Review of Related Life-Sciences Studies . . . . . . . . . . . . . . . . . 27
3 APPROACH 36
3.1 KNowledge Acqusition Methodology
(KNARM) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36
3.1.1 Sub-language Analysis . . . . . . . . . . . . . . . . . . . . . . 38
3.1.2 In-House Unstructured Interview . . . . . . . . . . . . . . . . 39
3.1.3 Sub-language Recycling . . . . . . . . . . . . . . . . . . . . . 39
3.1.4 Meta-Data Creation and Knowledge Modeling . . . . . . . . . 39
3.1.5 Structured Interview . . . . . . . . . . . . . . . . . . . . . . . 40
v
3.1.6 Knowledge Acquisition Validation . . . . . . . . . . . . . . . . 41
3.1.7 Database Formation . . . . . . . . . . . . . . . . . . . . . . . 41
3.1.8 Semi-Automated Ontology Building . . . . . . . . . . . . . . . 42
3.1.9 Ontology Validation and Evoluation . . . . . . . . . . . . . . . 44
4 METHODS AND APPLICATIONS OF KNARM 47
4.1 LINCS Information FramEwork (LIFE) and The BioAssay Ontology
(BAO) 2.0: . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47
4.1.1 Sub-language Analysis and Unstructured Interview for BAO
and LIFE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47
4.1.2 Sub-language Recycling for BAO and LIFE . . . . . . . . . . 51
4.1.3 Meta-Data Creation snd Knowledge Modeling for BAO and
LIFE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54
4.1.4 Structured Interview for BAO and LIFE . . . . . . . . . . . . 57
4.1.5 Knowledge Acquisition Validation (KA Validation) for BAO
and LIFE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58
4.1.6 Database Formation for BAO and LIFE . . . . . . . . . . . . 59
4.1.7 Semi-Automated Ontology Building for BAO and LIFE . . . . 59
4.1.8 Ontology Validation for BAO and LIFE . . . . . . . . . . . . 60
4.2 Drug Target Ontology (DTO): . . . . . . . . . . . . . . . . . . . . . 62
4.2.1 Sub-language Analysis and In-House Unstructured Interview
for DTO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63
4.2.2 Sub-language Recycling for DTO . . . . . . . . . . . . . . . . 67
4.2.3 Meta-Data Creation and Knowledge Modeling for DTO . . . . 69
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4.2.4 Structured Interview for DTO . . . . . . . . . . . . . . . . . . 72
4.2.5 Knowledge Acquisition Validation (KA Validation) for DTO . 73
4.2.6 Database Formation for DTO . . . . . . . . . . . . . . . . . . 73
4.2.7 Semi-Automated Ontology Building for DTO . . . . . . . . . 74
4.2.7.1 Knowledge Modeling of the Drug Target Ontology: . 74
4.2.7.2 A New Modular Architecture for the Drug Target
Ontology: . . . . . . . . . . . . . . . . . . . . . . . . 75
4.2.8 Ontology Validation for DTO . . . . . . . . . . . . . . . . . . 78
5 RESULTS 80
5.1 Use Case Examples . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81
5.1.1 Example Query 1 . . . . . . . . . . . . . . . . . . . . . . . . . 81
5.1.2 Example Query 2 . . . . . . . . . . . . . . . . . . . . . . . . . 85
5.1.3 Example Query 3 . . . . . . . . . . . . . . . . . . . . . . . . . 86
5.1.4 Example Query 4 . . . . . . . . . . . . . . . . . . . . . . . . . 91
5.1.5 Example Query 5 . . . . . . . . . . . . . . . . . . . . . . . . . 93
6 CONCLUSION 99
6.1 Discussion and Conclusions . . . . . . . . . . . . . . . . . . . . . . . 99
6.2 Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 111
APPENDICES 114
.1 In-House Structured Interview and Meta-Data Creation Documents
for LIFE and BAO . . . . . . . . . . . . . . . . . . . . . . . . . . . . 114
.1.1 LINCS Assay data . . . . . . . . . . . . . . . . . . . . . . . . 116
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