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Dr. Kwoh Chee Keong, PBM PhD,
DIC, MSc(ISE), Beng(EE), PGDIG, Senior
Member, IEEE Senior
Member IES Life
Member ICAAS Member
AMBIS Deputy Executive Director PaCE@NTU Centre
for Professional and Continuing Education 60
Nanyang Drive, SBS-01s-50, Singapore 637551 T: +65
6904 2056 (PaCE@NTU) School
of Computer Science and Engineering Block
N4, Level 2, Section A, Room 29 Nanyang Avenue, Singapore 639798 T: +65
6790 6057 (SCSE-CoE) F: +65
6792 6559 W:
https://personal.ntu.edu.sg/asckkwoh/
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I think the
nicest, most sincere compliments that I have received are those from my
students and people I did not expect.
Notes from
Students and Friends
HONORS AND AWARDS
National Day Awards
Public
Service Medal (National Day Award)
National Day Awards 2008, The Public
Service Medal (PBM) http://www.pmo.gov.sg/NationalHonoursandAwards/Pingat+Bakti+Masyarakat+(The+Public+Service+Medal).htm ,
conferred by the President of Singapore, Mr S R Nathan
Ministry
of Education Long Service Medal (National Day Award) 2016
Other Awards (NTU)
Best
Faculty Mentor Award from Temasek Foundation (TF)
LEARN 2014
Best
Faculty Mentor Award from Temasek Foundation (TF)
LEARN 2013
OPPORTUNITY
I am looking for a versatile, highly motivated Research Fellow/Pos-doc
PhD candidates. The successful candidates will build on the ongoing research directed
and will help define and explore this exciting area of research.
Applicants must have a strong background in
Computer Science and/or closely related areas (e.g. Mathematics, Computer Science,
Bioinformatics, Statistics and Physics) and excellent skills in both written
and spoken English, as the working language of the Faculty is English.
For PhD application, please visit the Graduate
Studies by Research at NTU before writing. Please note that PhD program is a very intensive program and the applicant
must have a strong interest, strong analytical
mind, technically sound in the area of data mining, learning theory, algorithms
and computer programming. You must be highly independent with good initiatives
and aspire to publish in top-tier journals. If you are interested and suitable,
Enquiries about these vacancies can be sent to asckkwoh@ntu.edu.sg (the deadlines are
flexible) with your CV, your proposed
research area with at least 3 references (either your own publications or
papers that inspired you to do research).
RESEARCH EXPERTISE
My
main interests lie in our desire to making sense of big heterogeneous data for
real application in engineering, life science, and medical.
Data Science (Bioinformatics) Research
High throughput
biological measurements and experiments in life science and healthcare have
resulted in the explosion of data available from sequencing and micro-arrays,
ChIP-Microarrays (ChIP-chip). This has led to the interdisciplinary science
called Bioinformatics. Which use Data Science in solving biological and life
science problems.
Machine
Learning and Statistical Inference
Machine Learning and statistical modelling
techniques that can learn from data to enables the making of the decision and simply a classification, this has application in almost every area. There
are many approaches and each has its own merits. The following have been
heavily used in my group: support vector machines, decision tree learning,
artificial neural networks, Bayesian networks, genetic and mimetic algorithms. Example include application
in supertype-specific HLA Class I binding peptides, protein-ligand binding affinity.
Learning with Unlabeled
Data
In the context of machine learning, PU learning is a collection of
semi-supervised techniques for training binary classifiers on positive (P) and
unlabeled (U) examples. To improve performance, it is important to partition U
into four sets, namely, reliable negative set RN, likely positive set LP,
likely negative set LN, and weak negative set WN. Such an approach has been used to identifying disease
genes from the human genome is an
important but challenging task in biomedical research.
Meta and Ensemble learning
Meta-learning is where automatic
learning algorithms are applied to
meta-data about machine learning experiments. The main goal is to use meta-data
to understand how automatic learning can become flexible in solving different
kinds of learning problems and enrich the knowledge discovered. Coupled with
ensemble methods that that integrates results of multiple predictive methods
into one system, these approach has found to be instrumental in improving predictive performance. Application
of this approach has been widely used in big data such as bioinformatics and
medical informatics. An example includes multiple kernel learning for heterogeneous data fusion and sparse learning in genome-wide association study (GWAS), and drug-target interaction
prediction.
Ontology for Knowledge Representation
Ontology is a formal representation of a set of concepts within a domain
and the relationships between those concepts. There have been major initiatives
in medical and bioinformatics to standardize the representation of terminology
and relationship across diseases, species and databases. The controlled
vocabulary of terms and description of product characteristics are the main
outcome to improve annotation for representation of meta-concept that greatly enhances
analysis. Specifically, it has been
extensively applied in complex mining, inferring gene-disease-phenotype
association.
Computational Structural Biology
This is where Computational Science and Engineering finds its
applications in understanding the molecular
structure of biological macromolecules. Dealing with computational models and
simulations, the field makes use of high-performance computing to solve complex
and expensive problems in biology. The result
includes the acceleration of docking and
its application in the understanding of
influenza and other viruses.
SOFTWARE
CovalentDock
Cloud: a web server for automated covalent docking
Covalent binding is an important
mechanism for many drugs to gain its function. We developed a computational algorithm
to model this chemical event and extended it to a web server, the CovalentDock Cloud, to make it accessible directly online
without any local installation and configuration. It provides a simple yet
user-friendly web interface to perform covalent docking experiments and
analysis online.
Software
for Accelerating Autodock Vina
Quickvina: This project
aims at accelerating Autodock Vina, a program for
protein-ligand docking. The main idea is to skip some of the local searches
which are not promising in finding a
better solution.
Quick Vina 2 is a
fast and accurate molecular docking tool, attained at accurately accelerating AutoDock Vina. It was tested against 195 protein-ligand complexes that compose the core
set of the 2014 release of the PDBbind using default
exhaustiveness level of 8, QVina 2
successfully attained up to 20.49-fold acceleration over Vina.
Software for
Learning with Unlabeled Data
1.
PUDI (2013) - a Positive-Unlabeled (PU) learning based method aiming to address the
problem of disease gene identification
Software –
for complexes
1.
CACHET- Discovery of
Protein Complexes with Core-Attachment Structures from TAP Data
2.
COACH- COre-AttaCHment based Complex Mining
Software –
for Computational Structural Biology
1.
CovalentDock
Cloud (2013) - This web server allows the researchers and
scientists to perform protein-ligand covalent docking.
2.
CovalentDock:
Automated covalent docking with parameterized covalent linkage energy
estimation and molecular geometry constrains
3.
QuickVina: Accelerating AutoDock Vina Using
Gradient-based Heuristics for Global Optimization
MY GRANTS
List of Research Grants
·
Computational Virulence Model
with Functional Information for Influenza Viruses
·
Methodological Investigation
for Automatic Detection of Primary Angle Closure Condition (PAC) and PAC
induced Glaucoma
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Bioinformatics Algorithms
for Detecting Genetic and Epigenetic Determinants of Meiotic Recombination
Hotspots from Genomic Data
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Whole genome sequencing,
single nucleotide polymorphisms, electron microscopy, Acinetobacter
baumannii, lipopolysaccharide
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Collaborative Research
Programme On Bioinformatics Algorithms And Tools
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Structural Analysis and Characterisation of Protein Complexes
·
Genomic analysis and
development of a new multilocus variable-number
tandem-repeat analysis - a scheme for
molecular epidemiological typing of Acinetobacter baumannii
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Core-Attachment Based Mining
For Protein Complexes & Small molecule
·
Interactions
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Characterization of novel
extracellular proteins produced by a newly-isolated
strain of Bacillus subtillis.
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Improved Design via
Evolutionary Algorithms
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Protein binding hotspots are
water-free?
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Analysis of Past DRG data
for the study of LOS for better utilization of Hospital Resources
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Data Warehousing and Data
Mining Analysis of Staphylococcus Aureus
·
A novel approach for inter-
to intra- network analysis of genetic
diseases using high-throughput data
·
Neural Systems modelling with functional MRI
·
SCE incubator proposal for
“Evolutionary and Complex Systems Lab”
·
The Application of ultrasound-based augmented reality with the
directional vacuum-assisted breast biopsy device in the treatment of breast
cancer
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Distributed Diagnosis and
Home Healthcare (D2H2)
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Development of a robotic
semi-automated remote handling system for radioiodine dispensing
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Functional MR Time-Series
Analysis
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Augmented Reality for
Prosthesis Cup Placement
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Robotic Skull Based Surgery
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Cardiovascular and
Respiratory Systems' Signal Simulation, Processing and Analysis for ICU, OR and
Telemedicine Applications.
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Strategic research: Interventive augmented reality for medical applications.
·
Surgeon Assistant Robot for
a Selected urological disorder.
MY GRADUATE STUDENTS
·
Pan
Hong – DNA methylation biomarkers of personal disease risk (PhD, 2012)
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Luay
Aswad - A molecular basis of the 5-gene breast tumour
aggressiveness grading signature (AGS) and its network – PhD, (2012 -)
·
Han
Xu - Constructing the Semantic Web for Biomedical Literature (PhD, 2011 - )
·
Ouyang
Xuchang - Automated and Accelerated Covalent Docking and Covalent Virtual
Screening (PhD, 2010–)
·
Thidathip
Wongsurawat - Computational Analysis and Prediction of Specific Genomic Regions
Forming R-loop Structure and Chromosomal Variations Associated with Cancer - (PhD, 2015)
·
Zhang
Zhou - Knowledge Discovery In Post Genome-Wide Association Study For Glaucoma
(PhD, 2015)
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Su
Tran To Chinh - Improving the Discrimination of Near-Native Complexes for
Protein Rigid Docking by Implementing Interfacial Water into Protein Interfaces
(PhD, 2015)
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Yang
Peng - Computational Approaches for Disease Gene Identification (PhD, 2014)
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Wu
Min - Mining Protein Complexes From Protein Interaction Data (PhD, 2012)
·
Zhang
Tianyou - Contact Network Based Framework For Infectious Disease Interventions
(PhD, 2015)
·
Stephanus
Daniel Handoko - Constrained-Oriented Refinement-Efficacious Memetic Algorithms
for Efficient Optimization of Computationally-Expensive Problems (PhD, 2014)
·
Adrianto
Wirawan - Whole-Genome Discovery Of Transcriptional Regulator Binding Sites
(PhD, 2011)
·
Zhang
Guanglan- Computational Epitope-Driven Vaccine Design (PhD, 2008)
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Zheng
Yun- Design Of Gene Expression Networks From Microarray Data (PhD, 2006)
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Zhao
Ying- Efficient Model And Feature Selection For SVM In Biomedical Data Analysis
(M Eng, -2004)
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Zhao
Jianhui- Human Animation from Motion
Recognition, Analysis and Optimisation ( PhD, 2003)
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Chen
Yintao - Image Processing For Ultrasound Guidance System In Breast Lump
Operation (M Eng, 2002)
·
Wang
Yan - Image-Based Indexing And Retrieval Of Trademark Logos, (M Eng, 2001)
·
Veena
Mohan Bhajammanavar - Image Processing Of The Digital
Mammogram For Segmentation And Characterization Of Microcalcifications,
(M Eng, 2000)
·
Misra
Sabita - Time Series Analysis Of ECG For Detection Of Premature Ventricular
Contraction (M Eng, 2000)
·
Zou
Qingsong - Object-Based Volume
Visualisation For Medical Imaging (PhD, 2001)
Planned and lectured subjects in
- CZ4032 Data Analytics and Mining (2014,15): Data Mining is an analytic process designed to explore
big data in search of consistent patterns and/or systematic relationships
between variables, and then to validate the findings by applying the algorithms
to new data.
- CE7411 Bioinformatics (2015): This course covers basic
bioinformatics concepts, databases, tools and applications. Introduction:
cell biology's central dogma, biological technologies for collecting and
storing genomic sequence data; databases that store these data and
strategies to extract information from them; Pairwise sequence alignment
for assessment of similarity to infer homology; Fundamental of Scoring
matrices to understand the assigned scores when performing alignment; The
popular heuristic search tool - Basic Local Alignment Search Tool (BLAST)
and advanced database searching; Multiple sequence alignment and
phylogenetic trees to complete the coverage from genomic sequences.
Functional genomics with the introduction to gene expression. Processes
for microarray data analysis; Feature selection and classification for
microarray data analysis. Protein families & proteomics; Protein
structure and structural genomics; and Molecular evolution and phylogeny.
- BI6123 Methods and Tools of Proteomics (2007): Proteomics study and identify
protein structure, interactions of protein/protein and protein/DNA and
biology of organisms. We will further introduce the newly developed
technology for the quantitative analysis of protein expression and
function on a genome-wide scale.
- BI1602 and SC448 Introductory Bioinformatics
(2005,06): Basic
bioinformatics concepts. Databases, tools and applications.
- BI1603 Computational Biology
(2006) Introduce the applications of the techniques of computer
science, applied mathematics, and statistics to address problems inspired
by biology. Major computational techniques used in biology include Bayes, HMM, MI etc.
- BG3011 Biocomputing (2005, 06): Introduction the new course
of biocomputing for students in SCBE, the
subject is first offered in July 2005; It covers Concepts; Bioinformatics
databases; Sequence alignment; Phylogeny and protein structure prediction.
- BI6104 Biostatistics: First offered in July 2003,
this course equipped the students in MSc with Knowledge of statistics,
experimental design and statistical learning.
- Curriculum for MSc in Bioinformatics: From August 2001 to June
2002, I worked with Vice-Dean (Academic) SCE, Head, Natural Science of
NIE, Vice-Dean (Academic) of SBS and Professor from MPE and EEE to
structure the new MSc in Bioinformatics.
- SC104 Mathematics I Fundamental of mathematics
for Engineering include statistics and calculus
- CE307 Computer Peripherals: In 1996-2000, re-design the course to
include start-of-the-art techniques such as PRML, USB and Bluetooth.
- M495 & M6524 Medical Assist Surgery (2000-2002): Co-planed and lectured the final year and
MSc elective for Biomedical Engineering.
- Digital Signal Processing (1992): Planed and lectured
the final year elective for the computer engineering.
MY PHD THESIS
GRADUATE ADVISORS: Prof
Duncan Fyfe Gillies - Professor of Biomedical Data Analysis, Department of
Computing, Imperial College London
My PhD thesis Probabilistic
Reasoning From Correlated Objective Data, University of London, Imperial
College
PUBLICATIONS
From Google Scholar
https://scholar.google.com.sg/citations?hl=en&user=jVn0wDMAAAAJ&view_op=list_works&sortby=pubdate
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Computational
approaches for detecting protein complexes from protein interaction networks:
a survey X
Li, M Wu, CK Kwoh, SK Ng BMC
genomics 11 (1), S3 |
2010 |
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A core-attachment based
method to detect protein complexes in PPI networks M
Wu, X Li, CK Kwoh, SK Ng BMC
bioinformatics 10 (1), 169 |
2009 |
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AL
Teh, H Pan, L Chen, ML Ong, S Dogra, J
Wong, JL MacIsaac, SM Mah, ... Genome
research, gr. 171439.113 |
2014 |
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Drug–target interaction
prediction by learning from local information and neighbors JP
Mei, CK Kwoh, P Yang, XL Li, J Zheng Bioinformatics
29 (2), 238-245 |
2012 |
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Augmented reality
systems for medical applications SL
Tang, CK Kwoh, MY Teo, NW Sing, KV Ling IEEE
engineering in medicine and biology magazine 17 (3), 49-58 |
1998 |
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Positive-unlabeled
learning for disease gene identification P
Yang, XL Li, JP Mei, CK Kwoh, SK Ng Bioinformatics
28 (20), 2640-2647 |
2012 |
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The safety issues of
medical robotics B
Fei, WS Ng, S Chauhan, CK Kwoh Reliability
Engineering & System Safety 73 (2), 183-192 |
2001 |
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Inferring
gene-phenotype associations via global protein complex network propagation P
Yang, X Li, M Wu, CK Kwoh, SK Ng PloS one 6 (7), e21502 |
2011 |
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Feasibility structure modeling: an effective chaperone for
constrained memetic algorithms SD
Handoko, CK Kwoh, YS Ong IEEE
Transactions on Evolutionary Computation 14 (5), 740-758 |
2010 |
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Applying the clonal selection
principle to find flexible job-shop schedules ZX
Ong, JC Tay, CK Kwoh International
Conference on Artificial Immune Systems, 442-455 |
2005 |
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X
Ouyang, S Zhou, CTT Su, Z Ge, R Li, CK Kwoh Journal
of computational chemistry 34 (4), 326-336 |
2013 |
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CBESW: sequence
alignment on the playstation 3 A
Wirawan, CK Kwoh, NT Hieu, B Schmidt BMC
bioinformatics 9 (1), 377 |
2008 |
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GL
Zhang, N Petrovsky, CK Kwoh, JT August, V Brusic Immunome research 2 (1), 3 |
2006 |
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Using hidden nodes in
Bayesian networks CK
Kwoh, DF Gillies Artificial
intelligence 88 (1-2), 1-38 |
1996 |
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Review of tandem repeat
search tools: a systematic approach to evaluating algorithmic performance KG
Lim, CK Kwoh, LY Hsu, A Wirawan Briefings
in bioinformatics 14 (1), 67-81 |
2012 |
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T
Wongsurawat, P Jenjaroenpun, CK Kwoh, V Kuznetsov Nucleic
acids research 40 (2), e16-e16 |
2011 |
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Fast leave-one-out
evaluation and improvement on inference for LS-SVMs Z
Ying, KC Keong Pattern
Recognition, 2004. ICPR 2004. Proceedings of the 17th International … |
2004 |
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On the two-level hybrid
clustering algorithm EY
Cheu, C Keongg,
Z Zhou International
conference on artificial intelligence in science and … |
2004 |
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Outlining the prostate
boundary using the harmonics method CK
Kwoh, MY Teo, WS Ng, SN Tan, LM Jones Medical
and Biological Engineering and Computing 36 (6), 768-771 |
1998 |
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Ensemble positive unlabeled
learning for disease gene identification P
Yang, X Li, HN Chua, CK Kwoh, SK Ng PloS one 9 (5), e97079 |
2014 |