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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 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/ |
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
Current Active Grants (updated
2023)
·
Host-pathogen
protein-protein interaction approaches for predicting virulence
·
The
discovery of neutralizing antibodies for potential novel coronavirus through
machine learning approaches
·
Explainable
AI for Multimodal Predictive Maintenance of Jet Engines with Smart HCI
·
Hybrid
Finite Element Method And Mixedlevel Coarse GrainingMolecular Dynamics
Simulation
·
Computational
Virulence Model With Functional Information For Influenza Viruses
·
Structural
analysis and characterization of protein complexes
·
Towards
direct and rapid mapping of RNA modifications with nanopore sequencing
·
Untangling
cancer re-wiring: Pan-Cancer mapping of transcription factor driven
dysregulatory hotspots using AlphaFold2 and integrative machine learning
·
Investigating
the regulation of 3D genome organization using machine learning
·
Predict
the solubility of proteins using machine learning
·
Hodge
Laplacian based deep learning models for drug design
·
Challenge-Learn:
Developing and Assessing an Andragogical Programme and System based on
Co-Skilling to Enhance Employability and Learning
·
Artificial
intelligence for the prediction of alternative splicing from epigenomics and
transcriptomics data in cancer
·
Computational
Systems Biology of Synthetic Lethality Towards New Cancer Medicine
List of Research Grants
·
Untangling cancer re-wiring: Pan-Cancer mapping of transcription factor
driven dysregulatory hotspots using AlphaFold2 and integrative machine learning
·
Predict the solubility of proteins using machine learning
·
Hodge Laplacian based deep learning models for drug design
·
Challenge-Learn: Developing and Assessing an Andragogical Programme and
System based on Co-Skilling to Enhance Employability and Learning
·
Host-pathogen protein-protein interaction approaches for predicting
virulence
·
The discovery of neutralizing antibodies for potential novel
coronavirus through machine learning approaches
·
Artificial intelligence for the prediction of alternative splicing from
epigenomics and transcriptomics data in cancer
·
Structural
analysis and characterization of protein complexes
·
AI Enhanced Creativity In Education
·
Hybrid Finite Element Method And Mixedlevel Coarse GrainingMolecular
Dynamics Simulation
·
Computational Virulence Model With Functional Information For Influenza
Viruses
·
Computational Systems Biology of Synthetic Lethality towards New Cancer
Medicine
·
CloudDock: Molecular Docking Platform on Cloud
·
Methodological Investigation for Automatic Detection of Primary Angle
Closure Condition (PAC) and PAC induced Glaucoma
·
Bioinformatics Algorithms for Detecting Genetic and Epigenetic
Determinants of Meiotic Recombination Hotspots from Genomic Data
·
Core-Attachment based Mining Technique: to detect Protein Complexes and
Protein-Small Molecule Interactions
·
Core-Attachment based Mining for Protein Complexes & Small-molecule
Interactions
·
Improved Design via Evoltionary Algorithms
·
The Protein Binding Hot Spots Are Water Free?
·
Neural Systems Modeling with functional MRI
·
Function MR Time-Series Analysis
·
Augmented reality for prosthesis cup placement
·
Cardiovascular & respiratory systems' signal simulation, processing
and analysis for ICU, or and telemedicine applications.Computational Virulence
Model with Functional Information for Influenza Viruses
·
Protein binding hotspots are water-free?
·
Analysis of Past DRG data for the study of LOS for better utilization
of Hospital Resources
·
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
·
Distributed Diagnosis and Home Healthcare (D2H2)
·
Development of a robotic semi-automated remote handling system for
radioiodine dispensing
·
Functional MR Time-Series Analysis
·
Augmented Reality for Prosthesis Cup Placement
·
Robotic Skull Based Surgery
·
Cardiovascular and Respiratory Systems' Signal Simulation, Processing
and Analysis for ICU, OR and Telemedicine Applications.
·
Strategic research: Interventive augmented reality for medical
applications.
·
Surgeon Assistant Robot for a Selected
urological disorder.
MY
GRADUATE STUDENTS
·
Tan
Lai Heng
·
Emadeldeen
Ahmed Ibrahim Ahmed Eldele
·
Zhang
Yu
·
Mohamed
Ragab Mohamed Adam
·
Lin
Zhuoyi
·
Hou
Yubo
·
Tjio
Ci'en Gabriel
·
Li
Xinya
·
Yin
Rui
·
Zhou
Xinrui
·
Ata
Kircali Sezin
·
Amr
Ali Mokhtar Alhossary
·
Aly
Mohamed Alaaeldin Aly Ezzat
·
Pradhan
Mohan Rajan
·
Pan
Hong – DNA methylation biomarkers of personal disease risk (PhD, 2012)
·
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)
·
Su
Tran To Chinh - Improving the Discrimination of Near-Native Complexes for
Protein Rigid Docking by Implementing Interfacial Water into Protein Interfaces
(PhD, 2015)
·
Yang
Peng - Computational Approaches for Disease Gene Identification (PhD, 2014)
·
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)
·
Zheng
Yun- Design Of Gene Expression Networks From Microarray Data (PhD, 2006)
·
Zhao
Ying- Efficient Model And Feature Selection For SVM In Biomedical Data Analysis
(M Eng, -2004)
·
Zhao
Jianhui- Human Animation from Motion
Recognition, Analysis and Optimisation ( PhD, 2003)
·
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