Download Fuhai Li - The Ohio State University College of Medicine

Fuhai Li
Office-phone: (614)-685-8201
Email: [email protected]
Address: 340-G Lincoln Tower, 1800 Cannon Drive Columbus, OH
43210
ACADEMIC POSITIONS
Ohio State University, Department of Biomedical Informatics
Assistant Professor – Systems Biology, Bioinformatics
Houston Methodist Hospital Research Institute
Assistant Professor – Systems Biology, Bioinformatics
Weill Cornell Medical College
Assistant Professor of Computational Biology in Radiology
Jan.2014 - Present
Oct.2014 – Jan.2016
Mar.2015 – Jan.2016
Houston Methodist Hospital Research Institute
Instructor – Mathematical Modeling & Statistical Bioinformatics Analysis
Feb.2012 – Oct.2014
Houston Methodist Hospital Research Institute
Postdoctoral Fellow – Mathematical Modeling & Statistical Bioinformatics Analysis
Aug.2008 – Jan.2012
Harvard Medical School, Brigham and Women's Hospital
Research Fellow – Mathematical Modeling & Statistical Bioinformatics Analysis
Jan.2006 – Apr.2007
EDUCATION
Peking University – Beijing, China
Ph.D. – Applied Mathematics
Yantai University – Yantai, China
B.S. – Applied Mathematics
Sep. 2003 – Jul. 2008
Sep. 1999 – Jul. 2003
TEACHING EXPERIENCE
Assistant Professor, Houston Methodist Research Institute
Supervising and Supervised 10+ graduate students, postdocs and summer college students on
statistical analysis on large-scale biomedical data sets.
2009 - Now
Instructor, College of Applied Arts and Technology, Peking University, Beijing
- Teach the ‘Mathematical Modeling’ Course, and
Feb.2005 - Jul.2005
Grade students’ assignments solutions, quizzes and exams
- Teach the ‘Java Programming’ Course, and
Sep.2005 – Dec.2005
Grade students’ assignments solutions, quizzes and exams
Teaching Assistant of Calculus, Peking University, Beijing
Feb.2004 – Dec.2004
- Help the teaching professor to grade assignments solutions, quizzes and exams
SELECTED HORNORS
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Travel Award, the Society of Mathematical Biology (SMB) Annual Meeting and
Conference (Landahl-Busenberg program), June 10~13, 2013
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Young Investigator Award, “The Truth of Personalized Medicine: Our Commons Future”,
4th Commons Congress of Sage Bionetworks, San Francisco, April 19-20, 2013.
Excellent Doctoral Dissertation Award, Peking University (2010) (3 out of 41 in
School of Mathematical Science, and 59 out of 946 in Peking University)
Three Goods Student award, Peking University, Beijing, P.R. China. (2005)
Scholarship of May Fourth, Peking University, Beijing, P.R. China. (2005)
Scholarship of ‘Jiang Zehan’, Peking University, Beijing, P.R. China. (2004)
Outstanding Graduate of Shandong Province, YanTai University, Yantai, P.R. China.
(2003)
Model of Three Goods Student, YanTai University, Yantai, P.R. China (2002)
Second Award of Shandong Province, China Undergraduate Mathematical Contest
Modeling (CUMCM-2002), Yantai University, Yantai, P.R. China. (2002)
MEMBERSHIP
Member of IEEE (The Institute of Electrical and Electronics Engineers)
Associate Member of AACR (American Association for Cancer Research)
Full Member of SMB (The Society for Mathematical Biology)
Professional Member of ISCB (International Society for Computational Biology)
RESEARCH OBJECTIVE & INTERESTS
Research Objectives
I received B.S. and Ph.D. in applied mathematics and did my pre-doctoral and postdoctoral
training at Harvard Medical School and Houston Methodist Research Institute. My research
interest is in applying mathematical, statistical learning and machine learning approaches
on big and diverse data sets to solve biological and clinical challenges in treating complex
diseases, e.g., cancers. I have solid and rich experience in computational data analysis and data
mining, e.g., bioimage informatics, genomics data analysis, biomarker and signaling network
discovery, drug repositioning and drug combinations prediction, on several research projects
funded by NIH, DoD, CPRIT, and other public and private funding sources. My research is highly
collaborative and translational. My research objectives, building on and extending my ongoing
research in the emerging field of big data to knowledge, are: 1) applying statistical and machine
learning approaches to converting big data of pharmacogenomics data into precision medicine,
including biomarker identification, drug discovery and combinations by integrating and analyzing
large-scale genomic, imaging, clinical, and environmental data, in collaboration with scientists
and clinicians; 2) statistical and mathematical modeling of tumor-microenvironment
communication to uncover, model and disrupt the roles of the interactions among tumor cells,
stromal cells, vasculatures, and other niche cells in the development of metastatic cancer, and
drug response prediction integrating diverse genomics and imaging data; and 3) image analysis
and informatics for automotive and quantitative understanding the useful information embedded
in complex images. The research results will translate to better patient care and drug
development for cancer. Following research projects that I lead show my research trajectory.
1) Machine Learning Approaches to Convert Big Data of Pharmacogenomics into
Precision Medicine: Integrative Large-scale PharmacoGenomics for Drug/Target
Discovery
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Developed the BioRegistration Database to query the kinase omics profiling (Mutation,
Copy Number Aberration, mRNA expression, and mRNA expression relative to normal
tissues) in about 1000 cell lines, query commercially available kinase inhibitors
More computational biology functions are being added to this web-server for public access)
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Developed novel drug repositioning approaches based on gene signatures, network
biology (interactome data) and manifold learning in PharmacoGenomics space for
Pediatric Cancers
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Developed a tool, DrugComboRanker, based on the drug-network community concept to
predict unkonwn targets of drugs, repurpose drugs, and identify synergistic drug
combinations (The tool is being developed and improved to include all the TCGA cancer
types and subtypes’ data to predict synergistic drug combinations).
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Developed a tool for uncovering mechanism of action signaling network, MoaNetMiner,
to discover the mechanism of action and Network biomarkers of Dasatinib in B-raf
inactivating mutation Non-Small Cell Lung Cancers (NSCLC), and predict the individual cell
lines/patients response to Dasatinib
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Developed optimal drug discovery for individual patients integrating personal
genomics with public phamarcogenomics data
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Developed an approach for discovering driver mutation network module for cancer
subtypes
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Discovered clinical risk factors and predict the possibility of Penumonia patient ReAdmission
2) Computational and Statistical Modeling of Tumor-Stromal Communication in Tumor
Growth and Drug Resistance
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Uncover signaling networks regulating tumor, e.g., Cancer Stem Cell (CSC) and
Microenvironment (mE) communications in breast cancer, and stroma-tumor interactions
in lung cancer through computational biology studies of next-generation sequencing data.
Develop a computational tool, CCCExplorer (Cell-Cell Communication Explorer) for
uncover actived ligand-receptor interactions and signaling pathways that mediate cell-cell
communications based on RNAseq and interactome data.
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Discover all the potential ligand-receptor pairs, extracellular proteins and cell
membrane proteins from public database
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Uncover the downstream signaling of receptor/membrane protein of interest by using
random walk based method integrating genomics/proteomic variations and publica
available interactome data
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Developed a software package, TMENExplorer (Tumor MicroENvironment Explorer,
to simulate and visualize tumor development with customized tumor tumormicroenvironment interactions with Partial Differential Equations (PDEs) and Cellular
Automata (Agent-based Model), and graphical user interfaces tools (QT and Vtk).
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Modeling the cancer stem cell signaling heterogeneity in the tumor development and drug
resistance
3) Advanced Image Analysis and Informatics for Target/Drug Discovery and Tumor
Diagnosis
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Developed a bioimage informatics system, DCellIQ, for time-lapse cellular image analysis
for cell-cycle regulator discovery (download website: http://www.cbi-tmhs.org/Dcelliq/
(downloaded by 100+ institutes))
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Developed a bioimage informatics system, GCellIQ, for genome-wide RNAi cellular image
analysis for cell shape regulator discovery (download website: http://www.cbitmhs.org/GCellIQ/)
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Developed informatics systems for lung, breast, and prostate cancer diagnosis and
subtyping using Coherent anti-Stokes Raman spectroscopy (CARS) images
FUNDING
CURRENT GRANTS
1U01CA188388-01A1 “MODELING TUMOR-STROMA CROSSTALK IN LUNG CANCER TO IDENTIFY
TARGETS FOR THERAPY” (PI: Wong)
7/1/2015 – 30/06/2020
There is an unmet need for molecularly targeted therapies for the treatment of non-small cell lung cancer
(NSCLC). In this study, we have employed integrated experimental and computational approaches to
identify tumor-stroma crosstalk pathways that drive NSCLC progression. We will develop a multi-cellular
crosstalk signaling network modeling and visualization software tool and apply this model to multi-cellular
RNA-seq data to identify tumor-stroma crosstalk pathways; genes involved in these signaling mechanisms
will be considered potential candidates that mediate NSCLC tumor progression and will undergo rapid
validation using in vitro assays. Finally, we will determine the function of selected crosstalk pathways in
NSCLC progression and in mediating therapeutic resistance.
Role: Co-investigator.
CPRIT Gulf Coast Consortium HTS Facilities (TxSACT)
6/1/2011 – 4/30/2016
CPRIT
“CPRIT High Throughput Screening Program”-Bioinformatics Core
The Bioinformatics Core will actively interact with screening programs and investigators to well understand
the project purpose, collect related supporting data, satisfy the data analysis needs of screening projects,
and perform integrative and systemic data analysis to provide bioinformatics clues for experimental design
of focused and combinatorial screening projects, selection of true positive hits, secondary experimental
design, and interpretation of mechanism of action.
Role: Co-investigator, lead and coordinate the bioinformatics core.
COMPLETED GRANTS
U54-Center for modeling of cancer development
2/01/2010 – 1/31/2015
NIH/NCI
To identify protein markers and temporal-spatial distribution for breast cancer stem cells and niche and
develop 3D multi-scale computational models to predict treatment responses based on the wet lab
experimental results.
Role: Co-investigator
NCI ICBP SUPPLEMENTAL FUNDS (Fuhai LI)
2012
Awarded $5000 at the 2012 Junior Investigator meeting to support cancer systems biology research
Virginia and L.E. Simmons Foundation
3/15/2011-2/28/2014
“Integrated genomic analysis and validation of molecular classification and targeted therapeutics to
improve the outcome of patients with medullablastoma”
Apply computational and systems biology methods to identify subtypes and corresponding signaling
pathways of medulloblastoma for drug repositioning and development
Role: Co-investigator
R01-High content image analysis and modeling for RNAi genome‐wide screening
To develop new cell‐based assay and associated analytic tool for analyzing and modeling genome wide
RNAi screening for Rho family of proteins
9/30/2009–8/30/2012, NIH/NCI
Role: Research Assistant
R01-Cell tracking and analysis for time-lapse microscopy
The goal is to develop new computational imaging algorithms for analyzing mitotic phase behavior of
dynamic live cells treated with small anti-motitic molecule compounds acquired via automated, time-lapse
fluorescence microscopy.
06/01/2005 – 05/30/2008, NIH/NLM
Role: Research Assistant
PEER REVIEWED PUBLICATIONS
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Fuhai Li*, Lin Wang*, Ren Kong, Jianting Sheng, Huojun Cao, Xiaofeng Xia, Clifford Stephan,
Stephen T. C. Wong, “DrugMoaMiner: A computational tool for mechanism of action discovery and
personalized drug sensitivity prediction”, IEEE International Conference on Biomedical and Health
Informatics, to be held in Las Vegas, NV, USA on 24-27 February 2016. (*Co-first author)
Fuhai Li#, Ming Zhan, “Data-driven Biomarker and Drug Discovery using Network-based Approach”,
Journal of Genetics and Genome Research, Accepted, 2015 (#Corresponding author)
Puppala, M.; He, T.; Chen, S.; Ogunti, R.; Yu, X.; Li, Fuhai; Jackson, R.; Wong, S., "METEOR: An
Enterprise Health Informatics Environment to Support Evidence-based Medicine," Biomedical
Engineering, IEEE Transactions on , vol.PP, no.99, pp.1,1 (Developed the patient Re-admission
Risk Prediction Model), 2015
Jianting Sheng*, Fuhai Li*, Stephen T.C. Wong, “Optimal Drug Prediction from Personal Genomics
Profiles”, IEEE J Biomed Health Inform. 2015 Mar 13 (*Co-first author)
Hyejin Choi, Jianting Sheng, Dingcheng Gao, Fuhai Li, Anna Durrans, Seongho Ryu, Sharrell B. Lee,
Navneet Narula, Shahin Rafii, Olivier Elemento, Nasser K. Altorki, Stephen T.C. Wong, Vivek Mittal,
“Transcriptome Analysis of Individual Stromal Cell Populations Identifies Stroma-Tumor Crosstalk in
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Mouse Lung Cancer Model”, Cell Reports, Volume 10, Issue 7, p1187–1201, 24 February 2015. (Lead
and developed the software CCCExplorer (Cell-Cell Communication Explorer) software. URL:
http://209.160.41.231/u54/CCCExplorer/)
Lin Wang*, Fuhai Li*, Jianting Sheng, Stephen Wong, “A Computational Method for Clinically
Relevant Cancer Stratification and Driver Mutation Module Discovery Using Personal Genomics
Profiles”, (*Co-first authors), BMC Genomics 2015, 16(Suppl 7):S6.
Miriam Brandl, Eddy Pasquier, Hong Zhao, Fuhai Li, Dominik Beck, Sufang Zhang, Stephen TC Wong,
Maria Kavallaris, “Image-based identification of synergistic drug combinations in triple-negative
breast cancer”, Molecular Oncology, 2014 Jun 19. pii: S1574-7891(14)00131-8.
Lei Huang*, Fuhai Li*, Jianting Sheng, Jinwen Ma, Ming Zhan, Stephen Wong, “DrugComboRanker:
Drug Combination Discovery Based on Target Network Analysis”, Bioinformatics, 2014 *Co-first
authors
Xu Chen, Yanqiao Zhu, Fuhai Li, Zeyi Zheng, Eric Chang, Jinwen Ma, Stephen T.C. Wong, “A
Clustering Approach to Dividing Boundaries of Touching Cells in Color Images”, Neurocomputing, In
Press 2014.
Tegy J. Vadakkan, John D. Landua, Wen Bu, Wei Wei, Fuhai Li, Stephen T.C. Wong, Mary E.
Dickinson, Jeffrey M. Rosen, Michael T. Lewis, Mei Zhang, ‘Wnt-Responsive Cancer Stem Cells Are
Located Close to Distorted Blood Vessels and Not in Hypoxic Regions in a p53-Null Mouse Model of
Human Breast Cancer’, Stem Cell Translational Medicine, 2014 Jul;3(7):857-66.
Fuhai Li, Hua Tan, Jaykrishna Singh, Jian Yang, Xiaofeng Xia, Jiguang Bao, Yong Li, Jinwen Ma, Ming
Zhan and Stephen T.C. Wong, ‘A 3D multiscale model of cancer stem cell in tumor development’,
BMC Systems Biology, BMC Systems Biology 2013, 7(Suppl 2):S12.
H Zhao, G Jin, K Cui, D Ren, T Liu, P Chen, S Wong, Fuhai Li, Y Fan, A Rodriguez, J Chang, ST Wong,
“Novel Modeling of Cancer Cell Signaling Pathways Enables Systematic Drug Repositioning for
Distinct Breast Cancer Metastases”, Cancer Research, 2013 Oct 15;73(20):6149-63.
Yanqiao Zhu*, Fuhai Li*, Tegy J. Vadakkan, John Landua, Mei Zhang, Wei Wei, Derek Cridebring,
Jinwen Ma, Mary Dickinson, Jeffrey M. Rosen, Michael T. Lewis and Stephen T.C. Wong, “3D
Vasculature Reconstruction of Tumor Microenvironment via Adaptive Clustering and Classification”,
Interface Focus, August 6, 2013 3 4 20130015; 2042-8901 (Co-first authors)
Zheng Yin, Amine Sadok, Heba Sailem, Afshan McCarthy, Xiaofeng Xia, Fuhai Li, Mar Arias Garcia,
Louise Evans, Norbert Perrimon, Chris Marshall, Stephen T.C. Wong and Chris Bakal, “A Screen for
Morphological Complexity Identifies Evolutionary Conserved Regulators of Discrete Switch-like
Morphogenesis”, Nature Cell Biology 15, 860–871, 2013.
Jian Yang, Jing Fan, Ying Li, Fuhai Li, Peikai Chen, Yubo Fan, Xiaofeng Xia, Stephen T. Wong,
‘Genome-wide RNAi screening identifies genes inhibiting the migration of glioblastoma cells’, PLoS
ONE 8(4): e61915. doi:10.1371/journal.pone.0061915, 2013.
Fuhai Li, Z. Yin, G. Jin, H. Zhao, STC Wong, ‘Bioimage Informatics for Systems Pharmacology’, PLoS
Computational Biology 9(4): e1003043, 2013.
Liang Gao*, Ahmad A. Hammoudi*, Fuhai Li, Michael J. Thrall, Philip T. Cagle, Yuanxin Chen, Jian
Yang, Xiaofeng Xia, Yehia Massoud, Zhiyong Wang, Stephen T. C. Wong ‘Differential diagnosis of
lung carcinoma with three-dimensional quantitative molecular vibrational imaging’, Journal of
Biomedical Optics, 17, 066017, 2012 ( *My student is as Co-first author)
Liang Gao, Zhiyong Wang, Fuhai Li, Ahmad A. Hammoudi, Michael J. Thrall, Philip T. Cagle, Stephen
T. C. Wong, “Differential Diagnosis of Lung Carcinoma with Coherent anti-Stokes Raman Scattering
(CARS) Imaging,” Archives of Pathology & Laboratory Medicine, Dec;136(12):1502-10, 2012.
Sigoillot FD, Huckins JF, Li Fuhai, Zhou X, Wong STC, et al. 2011 A Time-Series Method for
Automated Measurement of Changes in Mitotic and Interphase Duration from Time-Lapse Movies.
PLoS ONE 6(9): e25511. doi:10.1371/journal.pone.0025511, 2011
Liang Gao*, Fuhai Li*, Michael J. Thrall, Yaliang Yang, Jiong Xing, Ahmad A. Hammoudi, Hong Zhao,
Yehia Massoud, Philip T. Cagle, Yubo Fan, Kelvin K. Wong, Zhiyong Wang, Stephen T.C. Wong, “Onthe-Spot Lung Cancer Differential Diagnosis by Label-free, Molecular Vibrational Imaging and
Knowledge-based Classification”, J Biomed Opt. Sep;16(9):096004 2011. ( *Co-first authors)
Yaliang Yang*, Fuhai Li*, Liang Gao*, Zhiyong Wang, Michael J. Thrall, Pengfei Luo, Kelvin K. Wong,
and Stephen T. C. Wong, ‘Differential diagnosis of breast cancer using quantitative, label-free and
molecular vibrational imaging’, Biomedical Optics Express, Vol. 2, No. 8, 2011. (*Co-first authors)
Liang Gao, Haijun Zhou, Michael J. Thrall, Fuhai Li, Yaliang Yang, Zhiyong Wang, Pengfei Luo, Kelvin
K. Wong, Ganesh S. Palapattu, Stephen T.C. Wong, “Label-free high resolution imaging of prostate
glands and cavernous nerves using coherent anti-Stokes Raman scattering microscopy”, Biomedical
Optics Express Vol. 2, Iss. 4, pp. 915–926, 2011.
[23] Hong Zhao, Kemi Cui, Fang Nie, Lulu Wang, Miriam B Brandl, Guangxu Jin, Fuhai Li, Yong Mao,
Zhong Xue, Angel Rodriguez, Jenny Chang, Stephen TC Wong, “The effect of mTOR inhibition alone
or combined with MEK inhibitors on brain metastasis: an in vivo analysis in triple-negative breast
cancer models”, Breast Cancer Research and Treatment, Jan;131(2):425-36 2012.
[24] Fuhai Li, X Zhou, W Huang, CC Chiang and STC Wong, “Conditional Random Pattern Model for Copy
Number Aberration Detection”, BMC Bioinformatics, 11:200, 2010.
[25] Xiaofeng Xia, Jian Yang, Fuhai Li, Ying Li, Xiaobo Zhou, Yue Dai, Stephen TC Wong, “Image-Based
Chemical Screening Identifies Drug Efflux Inhibitors In Lung Cancer Cells”, Cancer Research,
70(19):7723-33, 2010.
[26] Fuhai Li, X Zhou, J Ma, and STC Wong, “Optimal Multiple Nuclei Tracking Using Integer
Programming for Quantitative Cancer Cell Cycle Analysis”, IEEE Transactions on Medical Image, vol.
29, pp. 96-105, 2010.
[27] Yue Huang, Xiaobo Zhou, Benchun Miao, Marta Lipinski, Yong Zhang, Fuhai Li, Alexei Degterev,
Junying Yuan, Guangshu Hu, Stephen T.C. Wong, “A computational framework for studying neuron
morphology from in vitro high content neuron-based screening”, Journal of Neuroscience Methods,
vol. 190, pp. 299-309, 2010.
[28] J. Wang, X. Zhou, Fuhai Li, PL Bradley, SF Chang, N. Perrimon, and S.T.C. Wong, "An image score
inference system for
RNAi genome-wide screening based on fuzzy mixture regression modeling,"
Journal of biomedical informatics, vol. 42, pp. 32-40, 2009.
[29] X. Zhou, Fuhai Li, J. Yan, S.T.C. Wong, “A Novel Cell Segmentation Method and Cell Phase
Identification Using Markov Model”, IEEE Trans on Information Technology in Biomedicine, vol. 13,
pp.152-157, 2009
[30] Wu L, Zhou X, Li Fuhai, Yang X, Chang CC, Wong STC, “Conditional Random Pattern Algorithm for
LOH Inference and Segmentation”, Bioinformtics, vol. 25, pp. 61-67, 2009.
[31] Fuhai Li, X. Zhou, J. Zhu, W. Xia, J. Ma, and S. T. C. Wong, "Workflow and Methods of High-content
Time-Lapse Analysis for Qantifying Intracellular Calcium Signals," Neuroinformatics, vol. 6, pp. 97108, 2008.
[32] M. Wang, X. Zhou, Fuhai Li, J. Huckins, W. R. King, and T. C. S. Wong, "Novel Cell Segmentation
and Online SVM for Cell Cycle Phase Identification in Automated Microscopy," Bioinformatics, vol. 24,
pp. 94-101, 2008.
[33] Z. Yin, X. Zhou, C. Bakal, Fuhai Li, Y. Sun, N. Perrimon, S.T.C. Wong, "Using iterative cluster
merging with improved gap statistics to perform online phenotype discovery in the context of highthroughput RNAi screens," BMC Bioinformatics, vol. 9:264, 2008.
[34] J. Chen, j. Zhu, H.-H. Cho, K. Cui, Fuhai. Li, X. Zhou, J. T. Rogers, S. T. C. Wong, and X. Huang,
"Differential Cytotoxicity of Metal Oxide Nanoparticles," Journal of Experimental Nanoscience 3 (4),
pp. 321-328, 2008.
[35] Fuhai Li, Xiaobo Zhou, Jinmin Zhu, Jinwen Ma, X. Huang, and S. T. C. Wong, "High content image
analysis for H4 human neuroglioma cells exposed to CuO nanoparticles," BMC biotechnology, vol.
7:66, 2007.
[36] Fuhai Li, X. Zhou, J. Ma, and T. C. S. Wong, "An automated feedback system with the hybrid model
of scoring and classification for solving over-segmentation problems in RNAi high content screening,"
Journal of Microscopy, vol. 226, pp. 121-132, 2007.
[37] Lin Wang*, Fuhai Li*, Jianting Sheng, Stephen Wong, “A Computational Method for Clinically
Relevant Cancer Stratification and Driver Mutation Module Discovery Using Personal Genomics
Profiles”, (*Co-first author), International Conference on Intelligent Biology and Medicine (ICIBM),
San Antonio, December 4~6, 2014.
[38] Lei Huang*, Fuhai Li*, Jianting Sheng, Jinwen Ma, Ming Zhan, Stephen Wong, “DrugComboRanker:
Drug Combination Discovery Based on Target Network Analysis”, International Conference on
Intelligent Systems for Molecular Biology (ISMB), Boston, July 10~15, 2014 (*Co-first authors)
[39] Xu Chen, Yanqiao Zhu, Fuhai Li, Zeyi Zheng, Eric Chang, Jinwen Ma, Stephen T.C. Wong, “A
Geodesic Distance Based Clustering Approach to Delineating Boundaries of Touching Cells”,
International Symposium on Neural Networks (ISNN13), Dalian, China during July 4-6, 2013.
[40] Hua Tan*, Fuhai Li*, Jaykrishna Singh, Xiaofeng Xia, Derek Cridebring, Jian Yang, Jiguang Bao,
Jinwen Ma, Ming Zhan, Stephen T.C. Wong, “A 3-Dimentional Multiscale Model to Simulate Tumor
Progression in Response to Interactions between Cancer Stem Cells and Tumor Microenvironmental
Factors”, 2012 IEEE International Conference on Systems Biology (ISB), Xi'an, China, Aug. 18~20,
2012. (Co-first authors)
[41] Yanqiao Zhu, Fuhai Li, Tegy J. Vadakkan, John Landua, Mei Zhang, Wei Wei, Derek Cridebring,
Jinwen Ma, Mary Dickinson, Jeffrey M. Rosen, Michael T. Lewis and Stephen T.C. Wong, “Coupling
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Oriented Hidden Markov Random Field Model with Local Clustering for Segmenting Blood Vessels and
Measuring Spatial Structures in Images of Cancer Stem Cells and Tumor Microenvironment”, IEEE
International Conference of Bioinformatics & Biomedicine (BIBM), 2011, Atlanta, GA, U.S.A. Nov. 1215, 2011.
Ahmad A Hammoudi, Fuhai Li, Liang Gao, Zhiyong Wang, Stephen T.C. Wong, “Automated Nuclear
Segmentation of Coherent Anti-Stokes Raman Scattering Microscopy Images by Coupling Superpixel
Context with Artificial Neural Network”, Second International Workshop onMachine Learning in
Medical Imaging (MLMI) In conjunction with MICCAI 2011, Westin Harbour Castle, Toronto, Canada,
September 18, 2011 (Proceedings in Springer Lecture Notes in Computer Science (LNCS)).
Liang Gao, Fuhai Li, Jiong Xiong, Michael J. Thrall, Yaliang Yang, Zhiyong Wang, Pengfei Luo, Kelvin
K. Wong, Hong Zhao, Stephen T.C. Wong, “Diagnosing lung cancer using coherent anti-Stokes
Raman scattering microscopy”, SPIE Photonics West, The Moscone Center, San Francisco, CA, 22~27
Jan. 2011.
Fuhai Li, Xiaobo Zhou, Stephen T.C. Wong, “Optimal Live Cell Tracking for Cell Cycle Study Using
Time-lapse Fluorescent Microscopy Images”, First International Workshop on Machine Learning in
Medical Imaging (MLMI) In conjunction with MICCAI 2010, Beijing, China, Sep. 20, 2010
(Proceedings in Springer Lecture Notes in Computer Science (LNCS)).
Fuhai Li, X Zhou, H Zhao, and STC Wong, “Cell Segmentation Using Front Vector Flow Guided Active
Contours”, in 12th International Conference on Medical Image Computing and Computer Assisted
Intervention (MICCAI 2009): London, UK, Sep. 20-24, 2009.
H Peng, X Zhou, Fuhai Li, X Xia and STC Wong, “Integrating Multi-scale Blob/Curvilinear Detector
Techniques and Multi-level Sets for Automated Segmentation of Stem Cell Images”, Sixth IEEE
International Symposium on Biomedical Imaging (ISBI 2009), Boston, USA, June 28 – July 1, 2009
Fuhai Li, X. Zhou, j. Zhu, J. Ma, and T. C. S. Wong, "High content image sequence analysis for
quantifying calcium signals inside cells with mutant presenilin-1 of familial alzheimer disease," in Life
Science Systems and Applications Workshop Lister Hill Auditorium, NIH, Bethesda, Maryland, USA, 89 Nov., 2007.
L. Liang, X. Zhou, Fuhai Li, J. Huckins, R. King, T. C. S. Wong, "Mitosis Cell Identification with
Conditional Random Fields," in Life Science Systems and Applications Workshop Lister Hill Auditorium,
NIH, Bethesda, Maryland, USA, 8-9 Nov., 2007.
Fuhai Li, X. Zhou, and S. T. C. Wong, "Novel Nuclei Segmentation and Cell Phase Identification
Using Markov Model," in International Symposium on Computational Models for Life Sciences (CMLS)
Gold Coast, Queensland, Australia, 17-19 Dec., 2007.
Z. Yin, X. Zhou, C. Bakal, Fuhai Li, Y. Sun, and S. T. C. Wong, "Online Phenotype Discoery in HighContent RNAi Screens using Gap Statistics," in International Symposium on Computational Models of
Life Sciences Gold Coast, Queensland, Australia, 17-19 Dec., 2007.
X. Zhou, J. Chen, J. Zhu, Fuhai Li, X. Huang, and S. T. Wong, "Study of CuO Nanoparticle-induced
Cell Death by High Content Cellular Fluorescence Imaging and Analysis," in IEEE International
Symposium on Circuits and Systems (ISCAS) New Orleans, 27-30 May, 2007.
J. Chen, j. Zhu, H.-H. Cho, K. Cui, Fuhai Li, X. Zhou, J. T. Rogers, S. T. C. Wong, and X. Huang,
"Differential Cytotoxicity of Metal Oxide Nanoparticles," in NSTI Nanotechnology Conference and
Trade Show anta Clara Convention Center. Santa Clara, California, U.S.A., 7-11 May, 2007.
M. Wang, X. Zhou, Fuhai Li, J. Huckins, R. W. King, and S. T. C. Wong, "Novel cell segmentation
and online learning alorithms for cell phase identification in automated time-lapse microscopy," in
International Symposium on Biomedical Imaging Metro Washington DC, USA, 12-15, Apr., 2007.
J. Wang, X. Zhou, Fuhai Li, and S. Wong, "Classify Cellular Phenotype in High-Throughput
Fluorescence Microcopy Images for RNAi Genome-Wide Screening," in IEEE/NLM Life Science
Systems & Applications Workshop Bethesda, MD, 13-14 July, 2006.
Fuhai Li, J. Ma, and D. Huang, "MFCC and SVM based recognition of Chinese vowels," Lecture Notes
in Artificial Intelligence, vol. 3802, pp. 812-819, 2005.
J. Ma, Fuhai Li, and J. Liu, "Non-parametric statistical tests for informative gene selection," Lecture
Notes in Computer Science, vol. 3498, pp. 697-702, 2005.
Book Chapters (1)
[57] Zheng Yin, James J. Mancuso, Fuhai Li and Stephen T.C. Wong, “Genomics-Based Cancer
Theranostics”, Cancer Theranostics, Edited by: Xiaoyuan Chen and Stephen Wong, ISBN: 978-0-12407722-5
Abstracts
[58] Peikai Chen, Fuhai Li, Y. S. Huang, Chris Man, Ching C Lau, Stephen T.C. Wong, “An integrative
genomic analysis of medulloblastoma subtypes for drug repositioning based on core signaling
pathways”, AACR/NCI Cancer Systems Biology Meeting, San Diego, Feb. 27~Mar.02, 2011.
MAJOR SKILLS
I have been trained through undergraduate to post-doctoral programs in applied mathematical
and bio-statistical approaches to solve biological problems and multi-dimensional data analysis.
Specifically, I am highly proficient in following:
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Machine learning approaches for large-scale biomedical data analysis, e.g., Hidden Markov
Random Chain/Field, Hidden Semi-Markov Random Model, Hierarchical Clustering,
Consensus Clustering, Decision Tree, Artificial Neuron Network, Feature Selection, Bayes
Classification, Support Vector Machine (SVM), Semi-supervised Learning, Manifold
Learning, Principle Component Analysis, Independent Component Analysis, Bayesian
Factor Analysis, Partial Least Square Regression, Integer Programming, Dynamic
programming.
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Statistical analysis on biomedical data analysis, e.g., Power Analysis, T/F test, ranksum
test, ANOVA, Confidence Interval Analysis, Linear/Logistic Regression Models
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Mathematical modeling approaches of signaling heterogeneity, tumor-stroma
communication, tumor development and drug response, e.g., Ordinary Differential
Equation (ODE), Delay Differential Equation (DDE), Partial Differential Equation (PDE),
and Agent-based Modeling (Cellular Automata)
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Next-generation sequence data analysis approaches and flowchart in remote
supercomputers, e.g., bowtie, tophat, cufflinks, cuffdiff, DEseq.
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Integrative omics data analysis, e.g., mRNA, SNP, miRNA, RNAseq data analysis, and
integrative analysis with interactome (e.g., PPI, KEGG, Reactome pathways) data
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Network based analysis and systems biology analysis for drug discovery/repositioning,
signaling signature, mechanism of action discovery, and enrichment analysis using largescale pharmacogenomics data
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Genomics repositories, e.g., GEO, TCGA, CMAP, CCLE, GDSC, PPI, KEGG, DrugBank, NPC
browser
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Quantitative high content screening (cellular image informatics) for target/drug discovery
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Programming in R, Python, Matlab, C/C++, Bioconductor, Cytoscape
INVITED TALKS
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“Data Driven Mechanism of Action Discovery and Drug Response Prediction of Dasatinib in
Lung Cancer”, Systems Medicine and Bioengineering Seminar Series, July 14, 2015
“Genomics Big Data Driven Approaches for Drug Sensitivity Prediction and Drug
Combination Discovery”, Recent Advanced in the Development of Combinatorial Therapies
for Cancer, June 10~11, 2015.
“Computational modeling and prediction of tumor-microenvironment interactions”,
Multiscale Cancer Systems Biology Symposium, April 16-17, 2015
“Data-driven approaches for drug sensitivity prediction and drug combination discovery”,
Bioinformatics Department Faculty Seminar, MD Anderson, Texas, Mar. 11, 2015
“TmenExplorer - a computational tool for simulating tumor-microenvironment
interactions”, NCI-ICBP Mathematical Modeling Meeting, Tampa, FL, Feb. 27, 2015.
“Integrative Analysis on Pharmacogenomics Data for Biomarker and Drug Discovery”,
University of Houston SIAM (Society for Industrial and Applied Mathematics) Student
Chapter, Houston, Texas, Nov. 07, 2014.
“Biomathematical Analysis of Pharmacogenomics Data for Drug Repositioning and Tumor
Microenvironment”, Assistant Member Promotion Talk, Houston Methodist Research
Institute, Houston, Texas, Aug. 07, 2014.
“Mathematical Modeling of Tumor and Microenvironment”, Multi-scale Cancer Systems
Biology Symposium, NCI-Center for Modeling Cancer Development (CMCD) in conjunction
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with the University of Texas Health Science Center at San Antonio-Indiana University,
Houston, Texas, May 24, 2014.
“Mathematical Modeling of Disease Signaling and Development with Omics and Imaging
Data”, Research Conference of Diabetes Research Center, Houston Methodist Hospital
Research Institute, July 24, 2013.
“An Agent-based Modeling Study on Heterogeneity of Cancer Stem Cells in Tumor
Development and Drug Response”, The Society for Mathematical Biology (SMB) Annual
Meeting and Conference, Tempe Mission Palms Hotel and Conference Center, Arizona
State University, Tempe, June 10 - 13, 2013
“Public PharmacoGenomics Data for Drug Repositioning”, TxSACT Leadership Meeting, Mar.
08, 2013
“Cancer image informatics and modeling”, 2013 SA-IU CCSB and CMCD Symposium
Program, Feb 7-9, 2013
“Imaging and Modeling Studies on Heterogeneity of Cancers”, 28th Southern Biomedical
Engineering Conference at MD Anderson, May 4-6, 2012.
“Systematic Bioinformatics Facilitating The Screening”, Texas Screening Alliance for
Cancer Therapeutics (TxSACT) workshop, CPRIT-Workshop-SanAntonio, Tx, Apr. 26, 2012
“Modeling Cell-Cell Communication in Cancer Stem Cell Microenvironment by Considering
Molecular and Cellular Heterogeneities”, The second meeting of the Mathematics of
National Cancer Institute's (NCI), Integrative Cancer Biology Program (ICBP), Tampa, FL,
March 18-20, 2012.
“A 3D Quantitative Imaging-Driven Computational Model for the Development of Breast
Cancer Stem Cells under Tumor Microenvironment”, NCI-ICBP Junior Investigator Meeting,
Boston, MA, Nov 2~4, 2011
"Oriented Hidden Markov Random Field Model with Local Clustering for Characterizing
Blood Vessel Spatial Structure", 7th Annual Breast Cancer Research and Education
Program, Conore Lake, Houston, Sep 16~17, 2011
“CELLIQ (Cellular Image Quantitator): Integrated workflow for automated quantitative
cellular image analysis and interpretation for Systems Biology”, The Center for Modeling
Cancer Development (CMCD) Annual Interdisciplinary Symposium, Houston, Tx, Feb.
10~11, 2011
“A Preliminary Report on Quantitative Analysis of Ex-Vivo CARS for Differential Pathologic
Diagnosis of Lung Cancer”, Apr. 21, Radiology Department Faulty Seminar, The Methodist
Hospital, 2010
“Dynamic Cell Image Quantitator (DCELLIQ): A bioinformatics tool that enables timepopulation based approach in studying cancer cell biology”, Oct. 7, Radiology Department
Faulty Seminar, The Methodist Hospital, 2009
“Integrating Multi-Scale Blob/Curvilinear Detector Techniques and Multi-Level Sets for
Automated Segmentation of Stem Cell Images”, Sixth IEEE International Symposium on
Biomedical Imaging (ISBI09), Boston, MA, Jun. 28 – Jul. 1, 2009.
“Mitosis cell identification with conditional random fields”, IEEE-NIH Life Science Systems
and Applications Workshop (LiSSA), NIH Campus in Bethesda, MD, Nov 8~9. 2007.
INVITED PAPER REVIEW
- Bioinformatics, - Cytometry, - IEEE/ACM Transactions on Computational Biology and
Bioinformatics, - IEEE Transactions on Biomedical Circuits and Systems, - IEEE Transactions on
Medical Imaging, - IEEE Transactions on Image Processing, - Proceedings of the IEEE, Computerized Medical Imaging and Graphics, - Journal of Biomathematics, - Applied
Mathematical Modeling, - International Journal of Image and Graphics, - International Journal of
Biomedical Imaging, - Pattern Recognition, - International Conference on Medical Image
Computing and Computer Assisted Intervention, - International Workshop on Medical Imaging
and Augmented Reality, - International Conference on Advanced Computational Intelligence, International Conference on Intelligent Computing, - International Symposium on Neural
Networks, - IEEE Journal of Biomedical and Health Informatics, -IEEE Transactions on Biomedical
Engineering