Welcome Message

Welcome Message from the General Chairs

On behalf of the Organizing Committee, it is our greatest pleasure to welcome you to the 5th International
Conference on Computational Systems-Biology and Bioinformatics (CSBio2014). The conference runs
from 10th to 12th November 2014 at Nanyang Technological University, which is a global university on
a rapid rise with sustainable research, and it is the worlds biggest engineering university. The conference
venue, Nanyang Executive Centre, is located in the Yunnan Garden Campus of Nanyang Technological
University, offering a premier venue for various events.
This years conference is jointly organised by Nanyang Technological University, National University ofSingapore, Agency for Science, Technology and Research and King Mongkut’s University of Technology
Thonburi, and we continue the good practice of collocating with IES2014.
The CSBio brings together researchers and practitioners to meet at this event to exchange ideas andstimulate research collaborations as a result of rapid advances in the generation of high-throughput”omics” data (such as deep sequencing). To extract biological knowledge from the data, and translate
it into benefits for society (e.g. better medicine and healthcare), novel computational tools and insights
are needed for data analysis and building models.
We are keeping to the tradition in carefully selecting a handful of high-quality papers from many countries.
These will be recommended for journal publications to the Journal of Bioinformatics and Computational
Biology (JBCB) or the Journal of Medical Imaging and Health Informatics (JMIHI).
This year we are very privilege to have Prof Ng Huck Hui, executive director of Genome Institute ofSingapore, to deliver his keynote titled Systems Biology of Stem Cells.
A series of social functions have been planned, which include a welcome reception, lunches, conference
banquet at Faber Peak Singapore, and tours to the Singapore Cable Car as well as Garden by theBay. These social activities serve as a good channel to establish new connections and foster everlasting
friendship among fellow counterparts.
Last but not least, we would like to express our sincere gratitude to everyone involved in making theconference a success. Many thanks go to advisory board members, the organizing committees, the keynote
and plenary speakers, the guest editors Prof (adj) Li Xiaoli, Prof Zheng Jie, and Prof Chou Siaw Mengfor the two journals (JCBC and JMIHI), the program committee and reviewers, many of whom are active
reviewers for the above journals, the conference participants, and of course, to all the contributing authors
who will be sharing the interesting results of their research.
With our best wishes for a wonderful and stimulating stay in Singapore!

Conference Committee

Conference Committee

General Chair
Kwoh Chee Keong, Singapore.
ASCKKWOH@ntu.edu.sg

General Co-Chair
Jonathan H. Chan, Thailand.
jonathan@sit.kmutt.ac.th

Program Chair
Li Xiaoli, Singapore.
xlli@i2r.a-star.edu.sg

Program Co-Chair
Zheng Jie, Singapore.
ZHENGJIE@ntu.edu.sg

Program Co-Chair
Chou Siaw Meng, Singapore.
MSMCHOU@ntu.edu.sg

Registration Chair
Huanjin Tang, Singapore.
htang@i2r.a-star.edu.sg

Publicity Chair
Wu Min, Singapore.
wumin@i2r.a-star.edu.sg

Local Arrangements Chair
Qi Cao, Singapore.

Secretary
Worrawat Engchuan, Thailand.

Conference Venue and Transportation

International Advisory Board
David W. Ussery
Oak Ridge National Lab, USA

Jens B. Nielsen
Chalmers University of Technology, Sweden

Bairong Shen
Soochow University, China

Kwong-Sak Leung
Chinese University of Hong Kong, Hong Kong

Nikola Kasabov
Auckland University of Technology, New Zealand

Nikhil R. Pal
Indian Statistical Institute, India

Richard Wintle
The Centre for Applied Genomics, Canada

Sissades Tongsima
National Center for Genetic Engineering andBiotechnology, Thailand

Stephen Wong
Houston Methodist Research Institute, USA

Sung-Bae Cho
Yonsei University, South Korea

Supapon Cheevadhanarak
King Mongkut’s University of Technology Thonburi, Thailand

Kay Chen Tan
National University of Singapore, Singapore

Tom Lenaerts
Universit libre de Bruxelles, Belgium

Yaochu Jin
University of Surrey, UK

Ong Yew Soon
Nanyang Technological University, Singapore

Conference Venue and Transportation

PC members
Shandar Ahmad
National Institute of Biomedical Innovation,
Japan

Tatsuya Akutsu
Kyoto University, Japan

Shunsuke Aoki
Kyushu Institute of Technology, Japan

Jonathan ChanKing Mongkut’s University of Technology Thonburi, Thailand

Xin Chen
Nanyang Technological University, Singapore

Jin ChenMichigan State University, USA

Jean-Paul Comet
University of Nice Sophia Antipolis, FranceFrank Eisenhaber
Bioinformatics Institute, A*STAR, Singapore

Worrawat Engchuan
King Mongkut’s University of Technology Thonburi, Thailand

Ge GaoPeking University, China

Ivo Grosse
Martin Luther University of Halle-Wittenberg,
Genmany

Chia-Lang Hsu
National Taiwan University, Taiwan

Hsuan-Cheng Huang
National Yang Ming University, Taiwan

Yaochu Jin
University of Surrey, UK

Tamer Kahveci
University of Florida, USA

Saowalak Kalapanulak
Bioinformatics and Systems biology Program,
ThailandAsif M. Khan
National University of Singapore, Singapore

Tsung Fei Khang
University of Malaya, Malaysia

Daisuke Kihara
Purdue University, USA

Jaebum Kim
Konkuk University, Korea

Sun KimNational Institue of Health, USA

Yoo-Ah Kim
National Institue of Health, USA

Kengo Kinoshita
Tohoku University, Japan

Akihiko Konagaya
Tokyo Institute of Technology, Japan

Bartosz Krawczyk
Wroclaw University Of Technology, Poland

Igor Kurochkin
Bioinformatics Institute, A*STAR, Singapore

Conference Venue and Transportation

Chee-Keong Kwoh
Nanyang Technological University, Singapore

Teeraphan Laomettachit
King Mongkut’s University of Technology Thonburi, Thailand

Hyunju Lee
Gwangju Institute of Science and Technology, Korea

Jeongjin Lee
Soongsil University, Korea

Tom Lenaerts
Universit libre de Bruxelles, Belgium

Jinyan Li
University of Technology, Sydney, Australia

Xiaoli Li
Institute for Infocomm Research, A*STAR, Singapore

C P LimDeakin University, Australia

Fenglou Mao
University of Georgia, USA

Osamu Maruyama
Kyushu University, Japan

Sebastian Maurer-Stroh
A*STAR, Singapore

Asawin MeechaiKing Mongkut’s University of Technology Thonburi, Thailand

Niranjan Nagarajan
Bioinformatics Institute, A*STAR, Singapore

Jin-Wu Nam
Hanyang University, Korea

Somnuk Phon-Amnuaisuk
Institut Teknologi Brunei, Brunei

Treenut Saithong
King Mongkut’s University of Technology Thonburi, Thailand

Yasubumi Sakakibara
Keio University, Japan

Christian Schnbach
Nazarbayev University, Republic of Kazakhstan

Kumar Selvarajoo
Keio University, Japan

Bairong Shen
Soochow University, China

Soo-Yong Shin
Asan Medical Center, Korea

Kyung-Ah Sohn
Ajou University, Korea

Chuan-Kang Ting
National Chung Cheng University, Taiwan

Wanwipa Vongsangnak
Soochow University, Thailand

Lusheng Wang
City University of Hong Kong, Hong Kong

Min WuA*STAR, Singapore

Xuegong Zhang
Tsinghua University, China

Shihua Zhang
University of Southern Canlifornia, China

Yun Zheng
Fudan University, China

Jie Zheng
Nanyang Technological University, Singapore

Shuigeng Zhou
Fudan University, China

Shanfeng Zhu
Fudan University, China

Zexuan Zhu
Shenzhen University, China

Conference Venue and Transportation

Conference Venue and Transportation

The Nanyang Executive Centre is the home of NTUs Executive Programmes and continuing education. It
provides an excellent environment away from the city and distractions of the office for executive learning.
The 170 guestrooms and suites have been designed to meet the needs of both training executives andbusiness travellers.All the guestrooms are equippedwith awork area and othermodern amenities to ensure
they provide guests with a pleasant stay. Complimentary Wi-Fi is available to all guests in their rooms,
lobby and function spaces. Further, facilities such as swimming pool, gym, football field, basketball court,
etc., are available around NEC.
Nanyang Executive Centre (General enquiries)

60 Nanyang View, Singapore, 639673.
Tel: +65 6790 6699/ 6790 6697
Email: ntu-nec@ntu.edu.sg
http: // www. ntu. edu. sg/ nec/ virtualTour/ Pages/ VirtualTour. aspx .

Nanyang Executive CentreConference Venue and Transportation

Map of Nanyang Executive CentreConference Venue and Transportation

Transportation form Singapore Changi Airport to Nanyang Executive Centre

Walk from Hall 8 to Nanyang Executive Centre

Keynote Speaker

Keynote Speech: Systems Biology of Stem Cells

Professor Huck-Hui NG
Genome Institute of Singapore
nghh@gis.a-star.edu.sg
Date/Time: 08:30 – 09:30, Tuesday, 11 Nov. 2014
Venue: Auditorium, NTU

Biosketch of the Speaker
Professor Huck-Hui NG is the Executive Director of the Genome Institute of Singapore. The Genome Institute of Singapore was started as a national flagship programme to tap into the innovation and impact of deciphering the genetic blueprint ofmankind. Today, it houses more than 200 researchers working on different aspects of
Human Genomics (Human Genetics, Infectious Diseases, Cancer Therapeutics andStratified Oncology, Stem Cell Genomics, Cancer Stem Cell Genomics, Translational
Genomics, Computational and Systems Biology).
Huck-Hui NG graduated from the National University of Singapore with a firstclass Honor degree in Molecular and Cell Biology and obtained his PhD from the
University of Edinburgh. He spent the next few years working at the Harvard
Medical School as a Damon Runyon-Walter Winchell research fellow. Upon returnto Singapore, he started his research programme on Stem Cell Genomics at the
Genome Institute of Singapore.
His lab works on different aspects of Systems Biology of Stem Cells. Specifically, hisgroup uses genome wide approaches to dissect the transcriptional regulatory networks in embryonic stem
cells with the aim to identify key nodes in this network. This had led to the first paper on the wholegenome and unbiased mapping of key transcription factors in mouse embryonic stem cells. His groupalso conducted the whole genome genetic screen for human embryonic stem cells. More recently, his lab
has begun to investigate the reprogramming code behind the induction of pluripotency in somatic cells.
Huck-Hui Ngs works have been published in journals such as Cell, Science and Nature. He also sits on
the Editorial Board of International Journals such as Genes & Development. His papers have received
over 12,000 citations and his H-index is 44.
His research work has earned him several prestigious national and international accolades including theSingapore Youth Award (2005 and 2010), the National Science Award 2007, the HUGO Chens New
Investigator Award 2010 and the Presidents Science Award 2011.
Huck-Hui NG holds Adjunct Professor appointment at the National University of Singapore (Departmentsof Biochemistry and Biological Sciences) and the Nanyang Technological University (School of Biological
Sciences). He is also the President of the Stem Cell Society, Singapore.

Keynote Speaker

Abstract
Embryonic stem (ES) cells are characterized by their ability to self-renew and remain pluripotent. Transcription factors have critical roles in the maintenance of ES cells through specifying an ES-cell-specificgene expression program. Deciphering the transcriptional regulatory network that describes the specificinteractions of these transcription factors with the genomic template is crucial for understanding thedesign and key components of this network. To gain insights into the transcriptional regulatory networks
in ES cells, we use chromatin immunoprecipitation coupled to ultra-high-throughput DNA sequencing(ChIP-seq) to map the locations of sequence specific transcription factors. These factors are known toplay different roles in ES cell biology. Our study provides new insights into the integration of these regulators to the ES cell-specific transcription circuitries. Collectively, the mapping of transcription factor
binding sites identifies new features of the transcriptional regulatory networks that define ES cell identity.
Using this knowledge, we investigate nodes in the network which when activated, will jump-start the EScell-specific expression program in somatic cells.

Technical ProgrammeTechnical Programme

Technical Programme – Day 1: Tuesday, 11 Nov. 2014

08:00 onwards

Registration

08:15 – 08:30

Opening of CSBio

08:30 – 09:30

CSBio Keynote: Systems Biology of Stem Cells by Professor Huck-Hui NG,
Genome Institute of Singapore

Venue

Auditorium, NTU

10:30 – 11:00

Coffee Break

12:00 – 13:00

Lunch BreakSessionInteraction, Biological Network and Pathway

Time

13:00 – 15:00

Venue

Auditorium, NTU

Chair

Kumar Selvarajoo

13:00 – 13:20

Invited Speech: Emergent Properties in Cell Signaling and Transcriptome-wide Response
Kumar Selvarajoo

13:20 – 13:40

Investigating noise tolerance in an efficient engine for inferring biological regulatory
networks.
Asako Komori, Yukihiro Maki, Isao Ono and Masahiro Okamoto.

13:40 – 14:00

Predicting Essential Genes and Synthetic Lethality via Influence Propagation in Signaling Pathways of Cancer Cell Fates.
Fan Zhang, Min Wu, Xuejuan Li, Xiaoli Li, Chee Keong Kwoh and Jie Zheng.

14:00 – 14:20

Computationally Predicting Protein-RNA Interactions Using Only Positive and Unlabeled Examples.
Zhanzhan Cheng, Shuigeng Zhou and Jihong Guan.

Technical Programme

14:20 – 14:40

Network Reconstruction vs. Edge Enrichment: Which is Better in Protein Function
Prediction Based on Protein-Protein Interaction Networks?
Wei Xiong, Luyu Xie, Jihong Guan and Shuigeng Zhou.

14:40 – 15:00

Detection of Highly Overlapping Communities in Complex Networks.
Madhusudan Paul, Rishav Anand and Ashish Anand.

15:00 – 15:30

Coffee BreakSession

Medical Data Diagnosis, Classification and SimulationTime

15:30 – 17:30

Venue

Auditorium, NTU

Chair

Shirley Sue

15:30 – 15:50

Microarray-based cancer diagnosis using an integrative gene-set analysis approach.
Worrawat Engchuan, Asawin Meechai, Sissades Tongsima and Jonathan Chan.

15:50 – 16:10

Comparison of Different ROI Sizes for Breast Density Classification: An Experimental
Study.
Vipul Sharma and Sukhwinder Singh.

16:10 – 16:30

Real-time Analysis of Vital Signs Using Incremental Data Stream Mining Techniqueswith a Case Study of ARDS under ICU Treatment.
Simon Fong, Shirley Siu, Suzy Zhou, Jonathan Chan, Sabah Mohammed and Jinan
Fiaidhi.

16:30 – 16:50

Numerical Simulation of Contaminant Control in Multi-patient Intensive Care Unit of
Hospital Using Computational Fluid Dynamics.
Tikendra Nath Verma and Shobha Lata Sinha.

16:50 – 17:10

Efficient Variation-based Feature Selection for Medical Data Classification.
Simon Fong, Justin Liang, Shirley Siu and Jonathan Chan.

17:10 – 17:30

Docking Score Calculation Using Machine Learning with an Enhanced Inhibitor
Database.
Masato Okada, Hayato Ohwada and Shin Aoki.

17:40 – 21:00

Award Banquet

Technical Programme

Technical Programme – Day 2: Wednesday, 12 Nov. 2014

SessionSequence, Structure and Parallel Computing

Time

08:20 – 10:40

Venue

Auditorium, NTU

Chair

TBA

08:20 – 08:40

FQC: a novel approach for efficient compression, archival and dissemination of fastq
datasets.
Anirban Dutta, Mohammed Monzoorul Haque, Tungadri Bose, Cvsk Reddy andSharmila Mande.

08:40 – 09:00

Grid-assembly: an oligo-nucleotide composition based partitioning strategy to aid
metagenomic sequence assembly.
Tarini Shankar Ghosh, Varun Mehra and Sharmila S Mande.

09:00 – 09:20

PSOVINA: the hybrid particle swarm optimization algorithm for protein-ligand docking.
Marcus C. K. Ng, Simon Fong and Shirley W. I. Siu.

09:20 – 09:40

Identifying SNP-SNP interactions with CNAs in lymphoma susceptibility.
Tse-Yi Wang, Yen-Ho Chen and Kuang-Chi Chen.

09:40 – 10:00

PARALLELFRAPPE: parallel version of admixture calculation.
Alongkot Burutarchanai and Prabhas Chongstitvatana.

10:00 – 10:20

Massively Parallel Tool for Protein Structure Comparison.
Ahmad Salah, Kenli Li and Tarek Gharib.

10:20 – 10:40

Embedding Assisted Prediction Architecture for Event Trigger Identification.
Yifan Nie, Wenge Rong, Yiyuan Zhang, Yuanxin Ouyang and Zhang Xiong.

10:40 – 11:00

Coffee Break

13:00 – 14:00

Lunch Break

14:00 – 18:00

Free TourAbstracts

Abstracts

Day 1: Tuesday, 11 Nov. 2014
Session
Time
Venue
Chair

Interaction, Biological Network
and Pathway
13:00 – 15:00
Auditorium, NTU
Kumar Selvarajoo

13:00 – 13:20
Invited Speech: Emergent Properties
in Cell Signaling and Transcriptomewide Responsea cell is increased. This resulted in deterministic cellular response that can be modeled using linear response rules. Adopting the rules, we have successfully predicted and experimentally verified novel signaling features, such as missing intermediates andcrosstalk mechanisms, and have identified novel targets for controlling proinflammatory response andcancer apoptosis. Specifically, I will provide an
overview on toll-like receptors, tumor necrosis factor(TNF) receptor, and TNF-related apoptosis-inducing
ligand receptor signaling. I will also show RNA-Seq
based single cell transcriptome-wide data analysis forembryonic developmental process (from oocyte to ES
cell stage).

Kumar Selvarajoo1,a

13:20 – 13:40
Investigating noise tolerance in an efficient engine for inferring biological regThe recent surge in systems biology efforts is reveal- ulatory networks
1

Institute for Advanced Biosciences Keio University,Japan
E-mail: a kumar@ttck.keio.ac.jp

ing many complexities that cannot be understood using traditional causal relationship approaches. For
example, gene expressions between identical single
cells are shown to be stochastic in time, and clonal
population of cells display heterogeneity in the abundance of a given protein per cell at any measured
time. Despite this, at population level, cells areable to execute well-defined biological processes suchas growth, division, differentiation and immune response. How do living systems overcome single cellfluctuations to produce deterministic population response? Our research focuses on fundamental cell behaviors. We investigate the dynamic instructive cellsignaling and highthroughput transcriptome-wide behaviors of immune, cancer and embryonic development cells, from single cells to population scale. As ageneral feature, we have found that single cell fluctuations reduce when cells form ensembles, due to the
reduction of dominant stochastic and transcriptomewide noise as the number of cells or molecules within

Asako Komori1,a , Yukihiro Maki2,c , Isao Ono3,d andMasahiro Okamoto1,2,b
1

Department of Bioinformatics, Graduate School of
Systems Life Sciences, Kyushu University Fukuoka,
8128582, Japan
2
Advanced Research Center for Synthetic Systems Biology, Kyushu University Fukuoka, 8128582, Japan
3
Graduate School of Computational Intelligence andSystems Science, Tokyo Institute of Technology
Tokyo, 2268502, JapanaE-mail:a-komori, b okahon@brs.kyushu-u.ac.jp,
cykhrmaki@tos.bbiq.jp, d isao@dis.titech.ac.jp
Biological systems are composed of biomolecules suchas genes, proteins, metabolites, and signaling components, which interact in complex networks. To understand complex biological systems, it is important tobe capable of inferring regulatory networks from experimental time series data. In previous studies, weAbstracts

developed efficient numerical optimization methods
for inferring these networks, but we have yet to test
the performance of our methods when considering theerror (noise) that is inherent in experimental data.
In this study, we investigated the noise tolerance ofour proposed inferring engine. We prepared the noisedata using the Langevin equation, and compared theperformance of our method with that of alternative
optimization methods.

ways to simulate the influence of single gene knockdown and double genes knock-down to cell death. A
pair of genes is considered as an SL candidate whendouble knock-down increases the probability of cell
death significantly, but single knock-down does not.
The single gene knock-down is confirmed according
to the human essential genes database. Our validation against literatures shows that the predicted SL
candidates agree well with wet-lab experiments. A
few novel reliable SL candidates are also predicted
Keywords: S-system formalism; real-coded genetic
by our model.
algorithm; gene regulatory networks; systems biology; system identification.
Keywords: Synthetic Lethality; Signaling Pathways;
Data-driven.

13:40 – 14:00
Predicting Essential Genes and Synthetic Lethality via Influence Propagation in Signaling Pathways of Cancer
Cell Fates

14:00 – 14:20
Computationally Predicting ProteinRNA Interactions Using Only Positive
and Unlabeled Examples

Fan Zhang1,a , Min Wu2,e , Xuejuan Li1,b , Xiaoli Li2,f , Zhanzhan Cheng1,a , Shuigeng Zhou1,b and Jihong
Chee Keong Kwoh1,c , Jie Zheng1,3,dGuan2,c1

1

A major goal of personalized anti-cancer therapy is toincrease the drug efects while reducing the side-efects
as much as possible. A novel therapeutic strategy
called synthetic lethality (SL) provides a great opportunity to achieve this goal. SL arises if mutationsof both genes lead to cell death while mutation ofeither single gene does not. Hence, the SL partnerof a gene mutated only in cancer cells could be a
promising drug target, and the identification of SL
pairs of genes is of great significance in pharmaceutical industry. In this paper, we propose a hybridized
method to predict SL pairs of genes. We combine adata-driven model with knowledge of signalling path-Protein-RNA interactions (PRIs) are considerably
important in a wide variety of cellular processes,
ranging from transcriptional and post-transcriptional
regulations of gene expression to the active defense of
host against virus. With the development of highthroughput technology, large amounts of proteinRNA interaction information is available for computationally predicting unknown PRIs. In recent
years, a number of computational methods for predicting PRIs have been developed in the literature,
which usually artificially construct negative samples
based on verified non-redundant datasets of PRIs totrain classifiers. However, such negative samples are
not real negative samples, some even may be un-

School of Computer Engineering, Nanyang Technological University, Singapore 639798
2
Data Analytic Department, Institute for Infocomm
Research, A*STAR, Singapore 138632
3
Genome Institute of Singapore, A*STAR, Singapore
138672E-mail: a fzhang005@e.ntu.edu.sg, b lixj, c asckkowh,defzhengjie@ntu.edu.sg,
wumin,
xlli@i2r.astar.edu.sg

Shanghai Key Lab of Intelligent Information Processing, and School of Computer Science Fudan University, 220 Handan Road, Shanghai 200433, China
2
Department of Computer Science and Technology
Tongji University, 4800 Cao’an Road, Shanghai
201804, ChinaaE-mail:zhanzhancheng, b sgzhou@fudan.edu.cn,
cjhguan@tongji.edu.cn

Abstracts

known positive samples. Consequently, the classifiers PPI networks have been proposed for protein functrained with such training datasets can not achieve tion prediction. Unfortunately, these PPI networks
satisfactory prediction performance.
face serious data quality challenges of false positives(noise) and false negatives (incompleteness), which
In this paper, we propose a novel method PRIPU thatadversely affects the performance of protein function
employs biased-SVM for predicting Protein-RNA Inprediction.
teractions using only Positive and Unlabeled examples. To the best of our knowledge, this is the first Methodology: To boost the performance of protein
work that predicts PRIs using only positive and un- function prediction based on PPI networks, two malabeled samples. We first collect known PRIs as our jor types of approaches were proposed to constructbenchmark datasets and extract sequence-based fea- more robust and reliable PPI networks, including nettures to represent each PRI. To reduce the dimen- work reconstruction and edge enrichment. Althoughsion of feature vectors for lowering computational various implementations of these two types of apcost, we select a subset of features by a filter-based proaches were reported, and definite performance imfeature selection method. Then, biased-SVM is em- provements were achieved, there lacks a systematicployed to train prediction models with different PRI performance comparison between these two types ofdatasets. To evaluate the new method, we propose a approaches. To answer this question, in this paper we
new performance measure called explicit positive re- carry out a comprehensive performance comparison
call (EPR), which is specifically suitable for the task of these two types of approaches. Concretely, we firstof learning positive and unlabeled data.
reconstruct and enrich PPI networks by using protein sequence similarity, local similarity indices andExperimental results over three datasets show that
global similarity indices, and then compare the preour method not only outperforms four existing methdiction performance of these reconstructed and enods, but also is able to predict unknown PRIs.
riched networks with that of the original networks of
Keywords: Protein-RNA Interactions; Biased-SVM; two real PPI datasets.
Prediction.
Conclusions: Experimental results demonstrate thatthe enriched networks achieves more accurate predictions than the original networks and the recon14:20 – 14:40
Network Reconstruction vs. Edge En- structed networks. Meanwhile, the reconstructed
network performs better than the original network
richment: Which is Better in Protein of the BioGRID dataset that contains only physical
Function Prediction Based on Protein- interactions. However, the original network of theProtein Interaction Networks?
STRING dataset that contains known and predictedinteractions outperforms the reconstructed network.
Wei Xiong1 , Luyu Xie1 , Jihong Guan2 and Shuigeng This means that network reconstruction is more effecZhou1,ative in relatively small and incomplete PPI networks.
Our empirical study also validates that sequence sim1
School of Computer Science, and Shanghai Key Lab
ilarity is more effective than global similarity and loof Intelligent Information Processing, Fudan Univercal similarity in PPI network enrichment.
sity, Shanghai, China
1
Department of Computer Science & Technology,
Tongji University, Shanghai, China
E-mail: a sgzhou@fudan.edu.cn
Background: The high-throughput technologies have
led to vast amounts of protein-protein interaction(PPI) data, and a number of approaches based on

Abstracts

14:40 – 15:00
Detection of Highly Overlapping Communities in Complex Networks

SessionDate/Time
Venue
Chair

Madhusudan Paul1,a , Rishav Anand1,b and Ashish
Anand1,cMedical Data Diagnosis, Classi––fication and Simulation
15:30 – 17:30
Auditorium, NTU
Shirley Sue

1

Department of Computer Science and Engineering
Indian Institute of Technology Guwahati, India
E-mail: a madhusudan, b anand.ashish@iitg.ernet.in,crishav@alumni.iitg.ernet.in

15:30 – 15:50
Microarray-based cancer diagnosis usDetecting communities in complex networks is one of ing an integrative gene-set analysis apthe most important aspects to understand complex proachsystems. In reality, many of these communities arehighly overlapping in nature, i.e., several nodes belong to more than three communities. Identificationof highly overlapping communities are strongly demanded in many applications such as systems biology
and social networks. Although there are algorithms
for detecting overlapping communities, majority ofthese are unable to detect highly overlapping communities properly. The performance of these algorithms
falls sharply when overlapping nodes belong to morethan three communities. In this paper, we propose
an extension of existing overlapping community detection algorithm, namely Greedy Clique Expansion(GCE). Due to lack of unavailability of real networks
with complete information of ground-truth communities, firstly, we experiment on state-of-the-art synthetic benchmark datasets and observe that our proposed extension exhibits better performance whenoverlapping nodes belong to more than three communities. We also experiment on real datasets andobserve competitive performance. The proposed extension can be applied on networks with highly overlapping community structure such as protein-protein
interaction networks.
Keywords:
Overlapping community detection;
Greedy Clique Expansion; complex net- works.

15:00 – 15:30: Coffee BreakWorrawat Engchuan1,a , Asawin Meechai2,b , Sissades
Tongsima3,c and Jonathan Chan1,d1

Data and Knowledge Engineering Laboratory,
School of Information Technology King Mongkuts
University of Technology Thonburi, Bangkok, Thailand
2
Department of Chemical Engineering, Faculty ofEngineering King Mongkuts University of Technology
Thonburi, Bangkok, Thailand
3
Biostatistics and Informatics Laboratory, Genome
Institute National Center for Genetic Engineeringand BiotechnologyaE-mail:worrawat.eng@st.sit.kmutt.ac.th,bcasawin.mee@kmutt.ac.th,sissades@biotec.or.th,djonathan@sit.kmutt.ac.th
Cancer is a complex disease that cannot be diagnosed
reliably using only single gene expression analysis.
Hence it is fast becoming the norm to do gene-set
analysis instead. This work provides a tool (Pathway
Activity Toolbox: PAT) that incorporates three geneset analysis methods, namely ANOVA-based featureset (AFS), Negatively Correlated Feature Set (NCFSi) and Conditional-Responsive gene (CORG)-based.
These methods identify subsets of phenotype-relevant
genes, which will be used to transform the gene expression levels to gene-set activities. As a case study,
the classifications using the expression levels of thesegene subsets without transformation and the geneset activities are compared in terms of the performance for disease diagnosis of three common cancer

Abstracts

types. Additionally, several gene-set collections andclassifiers can be optimally chosen for each disease.
We demonstrate the performance of PAT by running
it using three gene-set collections from MSigDB andthree traditional classifiers on eight actual microarray datasets of lung, colorectal and breast cancers.
Five-by-Two-fold cross-validation is used for parameters optimization and the overall classification performance is evaluated by cross dataset validation.
The comparison results show that the selections ofgene-set analysis methods, gene-set collections, andclassifiers can be made differently depending on the
disease. The results show that the use of Gene-SetAnalysis (GSA)-based gene markers is preferred togene-set activity markers for disease diagnosis. PAT
can be accessed via http://pat.sit.kmutt.ac.th
from which its java library for gene-set analysis, simple classification and a database with eight benchmark datasets of three cancers can be downloaded.

uated in terms of the performance of differentiating
between the fatty and dense breast tissue. From theexperimental results it has been found that ROI of
size 200200 pixels results in maximum overall accuracy of 97.2% with 100% sensitivity. The experimental results encourage the use of 200200 pixels ROI forclassification of breast density.
Keywords: Region of Interest; Breast Density; Classification; Texture Features; Feature Selection.

16:10 – 16:30
Real-time Analysis of Vital Signs Using
Incremental Data Stream Mining Techniques with a Case Study of ARDS under ICU Treatment

Simon Fong1,a , Shirley Siu1,b , Suzy Zhou2,c ,Jonathan Chan3,d , Sabah Mohammed4,e and Jinan
Keywords: Microarray; Gene expression analysis;
Fiaidhi4,fGene set; Classification; Feature selection; Breast
1
cancer; Lung cancer; Colorectal cancer.
Department of Computer and Information Science
University of Macau Macau SAR
2
Department of Products Management Mozat Pte
15:50 – 16:10
Ltd Singapore
Comparison of Different ROI Sizes for 3 School of Information Technology King Mongkut’s
Breast Density Classification: An Ex- University of Technology Thonburi Bangkok, Thailand
perimental Study
4
Department of Computer Science Lakehead University
Thunder Bay, Canada
Vipul Sharma1,a and Sukhwinder Singh1,bab
E-mail:ccfong,shirley.siu@umac.mo,1cd
UIET, Panjab University, Sector-25 Chandigarh, suzyzhou@mozat.com,
jonathan@sit.kmutt.ac.th,e160014, INDIA
sabah.mohammed, f jinan.fiaidhi@lakeheadu.caab
E-mail: vipuls85@gmail.com, sukhdalip@pu.ac.in
Analysing data streams of vital signs has been a
In this paper, an experimental study has been done to popular topic in research communities with techfind the optimal size of Region of Interest (ROI) for niques mainly of detection, classification and predicthe classification of breast density. ROI is a subpart tion. One drawback for data classification/predictionof the image that contains very important informa- is that the data mining model is built based on ation related to the diagnosis. Since an ROI is used as full set of stationary data. Updating the model for
representative of the image, and all further computa- sustaining the classification accuracy often needs thetions and diagnosis depends upon the ROI, therefore, whole dataset including the evolving data to be acit is very crucial to select an appropriate image area cessed. This nature of model rebuilding dampers theas ROI. For this purpose, different sized ROIs are possibility of mining vital signs in real-time and atselected from mammogram in such a way that their high speed. Unfortunately all the past papers incentral pixel is same. The effect of ROI size is eval- the literature were based on traditional data miningAbstracts

models. In this paper, a data stream mining model
which is flexible in configuring with different incremental data stream learning methods is tested as thereal-time classification engine for mining vital data
streams. A computer simulation experiment is conducted that is based on a case study of adult respiratory distress syndrome under twelve-hours of ICU
treatment. The results indicate promising possibilities of performing real-time prediction by the proposed model.
Keywords: Vital Signs Analysis, Data Stream Mining, Naviie Bayes, Support Vector Machine, Optimized Very Fast Decision Tree.

from the patient to the other people in the ICU of
hospital. A healthy environment can be achieved byminimizing the risk of contamination through appropriate filtration and air distribution scheme. It isobserved that stagnant zone near the doctor and patient is not healthy. Therefore suitable ventilation
arrangement and position of patient (bed) must be
provided for healthy environment in the ICU.
Keywords: Numerical simulation; ICU; Contaminantparticle; Lagrangian model.

16:50 – 17:10
Efficient Variation-based Feature Selection for Medical Data Classification

16:30 – 16:50
Numerical Simulation of Contaminant
Simon Fong1,a , Justin Liang1 , Shirley Siu1 andControl in Multi-patient Intensive Care Jonathan Chan2,bUnit of Hospital Using Computational 1
Department of Computer and Information Science,
Fluid Dynamics
Tikendra Nath Verma1,a and Shobha Lata Sinha1,b1

National Institute of Technology Raipur, Department of Mechanical Engineering, Raipur,
Chhattisgarh 492010, IndiaaE-mail:verma tikendra@yahoo.co.in,bshobha sinha1@rediffmail.com
The complex hospital environment requires special attention to ensure healthy indoor air quality (IAQ). It is necessary to protect to patientand healthcare workers against hospital acquired
pathogens/infections. A CFD analysis supported bymeasurement data has been carried out to simulate
the temperature distribution, air flow pattern in the
ICU and the contaminant dispersion from the patient. Numerical model solves conservation of mass,
momentum and energy equations. The contaminatedparticle (infection) which is simulated with a Lagrangian particle tracking model and the same has
been carried out by simulation. CFD analysis is used
to simulate and compare the removal of contaminatedparticle using a number of different ventilation systems. The study concerns about the health risk of
the airborne diseases (bacteria, fungus, viruses etc.)

University of Macau Taipa, Macau SAR
2
School of Information Technology, King Mongkut’s
University of Technology Thonburi Bangkok, ThailandbE-mail:jonathan@sit.kmutt.ac.th,accfong@umac.moMedical data which collected from sophisticated andsometimes different instruments are described by alarge number of feature variables and a historicalarchive of patients records, known as multivariate
medical dataset. In biomedical data mining, classification model is often built upon such dataset forpredicting which particular type of disease that a
new instance of record belongs to. One of the challenges in inferring a classification model with goodprediction accuracy is to select the relevant featuresthat contribute to maximum predictive power. Many
feature selection techniques have been proposed andstudied in the past, but none so far claimed to be
the best. In this paper, a novel and efficient feature selection method called Clustering Coefficientsof Variation (CCV) is proposed. CCV is based ona very simple principle of variance-basis which finds
an optimal balance between generalization and overfitting. Through a computer simulation experiment,Abstracts

eight medical datasets with substantially large number of features are tested by CCV in comparison to
three popular feature selection techniques. Results
show that CCV outperformed them in all aspects ofaveraged performances and speed. By the simplicity
of design it is anticipated that CCV will be a useful alternative of feature selection method for tasksof medical data classification especially with thosedatasets that are characterized by many features.
Keywords: Feature selection; Classification; Medical
datasets.

17:10 – 17:30
Docking Score Calculation Using Machine Learning with an Enhanced Inhibitor Database
Masato Okada1,a , Hayato Ohwada1,b and Shin
Aoki1,c1

Faculty of Science and Technology, Tokyo University of Science Noda, Chiba, 278-8510 JapanaE-mail:okada@ohwada-lab.net,bohwada@rs.tus.ac.jp, c shinaoki@rs.noda.tus.ac.jp
This paper describes a machine-learning method for
docking score calculation with the Database of Useful
Decoys: Enhanced (DUD-E). This database includes
both good inhibitors (ligands) and poor inhibitors(decoys), allowing machine learning to predict appropriate docking scores of a ligand and the associated decoys. This property enables us to find newinhibitor candidates with high accuracy and to screen
many compounds with excellent performance. The
proposed method can also be applied to any enzymes
without the use of the molecular structure of an enzyme, outperforming a number of traditional docking
software tools in both predictive accuracy and generality. We selected 10 enzymes from DUD-E and conducted a comparative study using two docking software tools. The classification performance was obtained from an experiment where 2869 ligands werepredicted from 2985 actual ligands, and 8923 decoys
were predicted from 8955 registered decoys. Such excellent performance is visualized by Receiver Operat-

ing Characteristic (ROC) curves at various thresholdsettings.
Keywords: Machine learning; In silico screening
Docking score.

Abstracts

Day 2: Wednesday, 12 Nov. 2014//metagenomics.atc.tcs.com/compression/FQC.
Session
Time
Venue
Chair

Sequence, Structure andParallel Computing
08:20 – 10:40
Auditorium, NTU
TBA

Keywords: Data compaction and compression; Algorithms for biological data management; NGS data;
Sequencing data archival.

08:40 – 09:00
Grid-assembly:
an oligo-nucleotide
composition based partitioning strat08:20 – 08:40
egy to aid metagenomic sequence asFQC: a novel approach for efficient semblycompression, archival and dissemination of fastq datasets
Tarini Shankar Ghosh1 , Varun Mehra1 and Sharmila
S Mande1,a1

1Anirban Dutta , Mohammed Monzoorul Haque , 1
Biosciences R&D Division, TCS Innovation Labs,
Tungadri Bose1 , Cvsk Reddy1 and Sharmila
54-B
Hadapsar Industrial Estate Pune, Maharashtra
Mande1,a411013, India
1
Bio-Sciences R&D Division, TCS Innovation Labs, E-mail: a sharmila@atc.tcs.com
Tata Consultancy Services Limited, 54-B, Hadapsar
Metagenomics approach involves extraction, sequencIndustrial Estate, Pune 411013, Maharashtra, India
ing and characterization of the genomic content ofaE-mail: sharmila.mande@tcs.com
entire community of microbes present in a given
Sequence data repositories archive and disseminate environment. In contrast to genomic data, accufastq data in compressed format. In spite of having rate assembly of metagenomic sequences is a chalrelatively lower compression efficiency, data reposito- lenging task. Given the huge volume and the diries continue to prefer GZIP over available special- verse taxonomic origin of metagenomic sequences,ized fastq compression algorithms. Ease of deploy- direct application of single genome assembly methment, high processing speed and portability are the ods on metagenomes are likely to not only lead toreasons for this preference. This study presents FQC an immense increase in requirements of computa– a fastq compression method that, in addition to tional infrastructure, but also result in the formaproviding significantly higher compression gains over tion of chimeric contigs. A strategy to address the
GZIP, incorporates features necessary for universal above challenge would be to partition metagenomic
adoption by data repositories/end-users. This study sequence datasets into clusters and assemble sepaalso proposes a novel archival strategy which allows rately the sequences in individual clusters using any
sequence repositories to simultaneously store and dis- single-genome assembly method. The current study
seminate lossless as well as (multiple) lossy variants of presents such an approach that uses tetra-nucleotide
fastq files, without necessitating any additional stor- usage patterns to first represent sequences as points
age requirements. For academic users, Linux, Win- in a 3D-space. The 3D-space is subsequently pardows and Mac implementations (both 32 and 64-bit) titioned into ’Grids’. Sequences within overlappingof FQC are freely available for download at: https: grids are then progressively assembled using any

Abstracts

available assembler. We demonstrate the applicability of the current Grid-Assembly method using various categories of assemblers as well as different simulated metagenomic datasets. Validation results indicate that the Grid-Assembly approach helps in improving the overall quality of assembly, in terms of
the purity and volume of the assembled contigs.

lays the foundation for the future development ofswarm-based algorithms in docking programs.

Keywords:
Metagenomics;
Tetra-nucleotide frequency.

09:20 – 09:40
Identifying SNP-SNP interactions with
CNAs in lymphoma susceptibility

Sequence Assembly;

Keywords: particle swarm optimization; proteinligand docking; flexible docking, conformational
search; AutoDock; drug design.

09:00 – 09:20
Tse-Yi Wang1,a , Yen-Ho Chen2,b and Kuang-Chi
PSOVINA: the hybrid particle swarm Chen2,c
optimization algorithm for protein- 1
Laboratory of Molecular Anthropology and Transligand docking

fusion Medicine, Department of Medical Research,
Mackay Memorial Hospital, New Taipei City 25160,
Marcus C. K. Ng , Simon Fong and Shirley W.
Taiwan1,cI. Siu2
Department of Medical Informatics, Tzu Chi Uni1
Department of Computer and Information versity, Hualien 97004, Taiwan
abtseyiwang,yhchen1210@gmail.com,
Science,
University of Macau Avenida da E-mail:
Universidade,Taipa Macau S.A.R, China c chichen6@mail.tcu.edu.twab
E-mail:marcus.ckng@gmail.com,
ccfong,
Genome-wide association studies (GWAS) identify
cshirleysiu@umac.momany single nucleotide polymorphisms (SNPs) that
Protein-ligand docking is an essential step in mod- are associated with diseases and are involved in theirern drug discovery process. The challenge here is to pathogenesis. The currently identified SNP variants
accurately predict and efficiently optimize the posi- only explain a portion of the heritability underlying
tion and orientation of ligands in the binding pocket the complex diseases. Some recent studies have foof a target protein. In this paper, we present a cused on investigating SNP-SNP interactions to exnew method called PSOVina which combined the plore the missing aspects of the heritability. Several
Particle Swarm Optimization (PSO) algorithm with have looked for genetic variations other than SNPs
the efficient BFGS local search method adopted in such as copy number alterations (CNAs). However,
AutoDock Vina to tackle the conformational search few have considered both SNP-SNP interactions andproblem in docking. Using a large and diverse data CNAs comprehensively. In our study, we employed
set of 201 protein-ligand complexes from the PDB- a fusion approach that incorporated the informationbind database, we assessed the predictive perfor- of copy numbers to identify SNP-SNP interactions inmance of PSOVina in comparison to the original Vina analyzing a public dataset of 214 lymphoma cases.
program. Our docking simulations showed that PSO- The copy numbers were first examined by clustering
Vina has a remarkable speed-up of over 45% in terms analysis, and then the SNP-SNP interactions wereof average docking time. Regarding docking accu- detected by the multifactor dimensionality reduction
racy, PSOVinas predicted ligand conformations are (MDR) method. The results showed that the idenon average 7% lower in RMSD compared to Vina tified SNP-SNP interactions with CNAs were morewith an increase of prediction success rate by 4%. significantly associated with lymphoma susceptibilOur work proves that PSO is superior to Monte Carlo ity than the SNP-SNP interactions detected without
search method in molecular docking applications and regarding CNAs. Therefore, we conclude that com1,a1,bAbstracts

bining SNP-SNP interactions with CNAs provides a Hunan University Changsha, Hunan 410082, China
more comprehensive strategy for disease association 4 Faculty of Computing and Information Technology,
studies.
King Abdulaziz University Jeddah, Saudi Arabia
5
Faculty of Computer and Information Sciences, Ain
Shams University Cairo, EgyptabE-mail:ahmad,
lkl@hnu.edu.cn,
09:40 – 10:00
ctfgharib@kau.edu.sa
PARALLELFRAPPE: parallel versionof admixture calculation
AlongkotBurutarchanai1,1Chongstitvatana1,bandPrabhas1

Department of Computer Engineering, Chulalongkorn University, Address Bangkok, 10330,
THAILANDaE-mail:Alongkot.bu@gmail.com,bprabhas.c@chula.ac.th

At the center of computational structure biology, protein structure comparison, or alignment, is a key
problem. The steady increase in the number of protein structures encourages the development of massively parallel methods and tools. While the focusof research is to propose data-analytical methods totackle this problem, there are limited practical efforts to propose generic tools to run these methods
in different, parallel environments other than the one
these methods are targeting. In the present work, we
propose a scalable tool to handle the rapid growthof the structural proteomic data using massively parallel environments. The proposed tool can run thesequential methods on parallel environments or extend the parallelism level from multicore to cluster. The tool requires no scripting or installation.
The tool achieves a linear, close to optimal, speedup.
The experimental results show no effect on the accuracy of the used methods. The tool is available at
http://biocloud.hnu.edu.cn/ppsc/.

ParallelFrappe is software for the analysis of individual admixture for Individual Identification by genetic data. The ParallelFrappe is a parallel version
of Frappe program. This version can achieve speed
up of the calculation with multicore processors. The
program was implemented with a 64-bit platform to
improve the performance on large dataset. The algorithm for the parallel machine was adapted to matchwith the multiprocessor architecture. The program
also has a milestone feature that it can re-executefrom the previous stop point which is very important
Keywords: Protein structure comparison; parallel
for a long running job.
tool; multicore; grid; speedup.
Keywords: Individual Identification; Admixture
Analysis; Expectation Maximization.

10:20 – 10:40
Embedding Assisted Prediction Architecture for Event Trigger Identification

10:00 – 10:20
Massively Parallel Tool for Protein
Structure Comparison
Yifan Nie1,a , Wenge Rong2,3,b , Yiyuan Zhang2,e ,Yuanxin Ouyang2,3,c and Zhang Xiong2,3,dAhmad Salah
1

1,2,a

3,b

, Kenli Li

and Tarek Gharib

4,5,cCollege of Information Science and Engineering,
Hunan University Changsha, Hunan 410082, China
2
College of Computers and Informatics, Zagazig
University Zagazig, Egypt
3
The National Supercomputing Center in Changsha
and College of Information Science and Engineering,1

Sino-French Engineering School, Beihang University, Beijing 100191, China
2
School of Computer Science and Engineering,
Beihang University, Beijing 100191, China
3
Research Institute of Beihang University in Shenzhen, Shenzhen 518057, ChinaaE-mail:yifan.nie@ecpk.buaa.edu.cn,

Abstractsbecw.rong,oyyx,
yyzhang@cse.buaa.edu.cn

dxiongz@buaa.edu.cn,

Molecular events normally have significant meanings
since they describe important biological interactions
or alternations such as binding of a protein. As acrucial step of biological event extraction, event trigger identification has attracted much attention andmany methods have been proposed. Traditionallythose methods can be categorised into rule-based approach and machine learning approach and machinelearning based approaches have demonstrated its potential and outperformed rule-based approaches inmany situations. However, machine learning based
approaches still face several challenges among which
one notable one is how to model semantic and syntactic information of different words and incorporate it into the prediction model. There exist many
ways to model semantic and syntactic information,
among which word embedding is an effective one.
Therefore, in order to address this challenge, in thisstudy, a word embedding assisted neural network prediction model is proposed to conduct event trigger
identification. The experimental study on commonlyused dataset has shown its potential. It is believedthat this study could offer researchers insights intosemantic-aware solutions for event trigger identification.
Keywords: Neural networks; Word embedding; Event
trigger identification.