Thank You for referencing this work, if you find it useful!
Citation of a related scientific book:
Wac, K., Wulfovich, S. (2021). Quantifying Quality of Life, Series: Health Informatics, Springer Nature, Cham, Switzerland. The talk details:
Katarzyna Wac, “Remote quality of life assessment: ‘What is always speaking silently is the body'”. Digital Health Connect Conference, Sion, Switzerland
Video: https://www.digitalhealthconnect.ch/en/
Human Genome Project is a worldwide scientific achievement. It was a thirteen-year project initiated in 1990 and completed in 2003. Human Genome Project helped a lot in the identification of diseased genes as DNA is very significant for understanding the diseased gene and their functions. It helped in the identification of disease loci for many diseases and presented their treatment through preventive measures. It identified the gene loci for many diseases like cancer, asthma, high blood pressure, diabetes type 2, obesity, Alzheimer's disease, Down's syndrome, Turner's syndrome, depression and many types of heart diseases including cardiovascular disease and coronary artery disease. This project does not directly treat the diseases but it helps in the identification of disease gene loci and then allows the treatment of disease through its preventive measures before the appearance of symptoms or at the initial stages of the disease through many techniques like gene therapy, pharmacogenomics, and targeted drug therapy. These are the helpful techniques in the diagnoses of the human disease gene locus.
Bioinformatics in the Clinical Pipeline: Contribution in Genomic Medicineiosrjce
In this review report we like to focus on the new challenges in methodology of modern biology be
used in medical science. Today human health is a primary issue to cure disease, undoubtedly the answer to this
is bioinformatics or (In-silco) tools has change the concept of treating patients to understand the need of
genomic medicine in use. Those with new modes of action in clinical treatment, is a major health concern in
medical science. On global prospective scientific role in constructing new ideas to remediate health care to
treat disease exciting in nature is challenging task. So awareness needs to accelerate store clinical datasets for
scientific represents to design genomic drugs. This new outline will drive the medical to discover public data
and create a cognitive approach to use technology cheaper at cost effective mode.
What is exposome?
The exposome can be defined as the measure of all the exposures of an individual in a lifetime and how those exposures relate to health.
The aging of the skin may be influenced by various internal or external factors.
Here, we explore the role of various exposures in skin aging.
Thank You for referencing this work, if you find it useful!
Citation of a related scientific book:
Wac, K., Wulfovich, S. (2021). Quantifying Quality of Life, Series: Health Informatics, Springer Nature, Cham, Switzerland. The talk details:
Katarzyna Wac, “Remote quality of life assessment: ‘What is always speaking silently is the body'”. Digital Health Connect Conference, Sion, Switzerland
Video: https://www.digitalhealthconnect.ch/en/
Human Genome Project is a worldwide scientific achievement. It was a thirteen-year project initiated in 1990 and completed in 2003. Human Genome Project helped a lot in the identification of diseased genes as DNA is very significant for understanding the diseased gene and their functions. It helped in the identification of disease loci for many diseases and presented their treatment through preventive measures. It identified the gene loci for many diseases like cancer, asthma, high blood pressure, diabetes type 2, obesity, Alzheimer's disease, Down's syndrome, Turner's syndrome, depression and many types of heart diseases including cardiovascular disease and coronary artery disease. This project does not directly treat the diseases but it helps in the identification of disease gene loci and then allows the treatment of disease through its preventive measures before the appearance of symptoms or at the initial stages of the disease through many techniques like gene therapy, pharmacogenomics, and targeted drug therapy. These are the helpful techniques in the diagnoses of the human disease gene locus.
Bioinformatics in the Clinical Pipeline: Contribution in Genomic Medicineiosrjce
In this review report we like to focus on the new challenges in methodology of modern biology be
used in medical science. Today human health is a primary issue to cure disease, undoubtedly the answer to this
is bioinformatics or (In-silco) tools has change the concept of treating patients to understand the need of
genomic medicine in use. Those with new modes of action in clinical treatment, is a major health concern in
medical science. On global prospective scientific role in constructing new ideas to remediate health care to
treat disease exciting in nature is challenging task. So awareness needs to accelerate store clinical datasets for
scientific represents to design genomic drugs. This new outline will drive the medical to discover public data
and create a cognitive approach to use technology cheaper at cost effective mode.
What is exposome?
The exposome can be defined as the measure of all the exposures of an individual in a lifetime and how those exposures relate to health.
The aging of the skin may be influenced by various internal or external factors.
Here, we explore the role of various exposures in skin aging.
Thank You for referencing this work, if you find it useful!
Citation of a related scientific paper:
Berrocal, A., Manea, V., De Masi, A., Wac, K. mQoL-Lab: Step-by-Step Creation of a Flexible Platform to Conduct Studies Using Interactive, Mobile, Wearable and Ubiquitous Devices, MobiSPC 2020
The talk details:
Alexandre De Masi, Igor Matias, Vlad Manea, Allan Berrocal, Katarzyna Wac, “mQoL: Methodology for Assessing and Modeling Human Aspects in Interactive, Mobile, Wearable and Ubiquitous Computing in Situ”, International Transdisciplinarity Conference (ITD) 2021, September 2021.
Video: https://youtu.be/dIxOnqd5g8E
MINING OF IMPORTANT INFORMATIVE GENES AND CLASSIFIER CONSTRUCTION FOR CANCER ...ijsc
Microarray is a useful technique for measuring expression data of thousands or more of genes
simultaneously. One of challenges in classification of cancer using high-dimensional gene expression data
is to select a minimal number of relevant genes which can maximize classification accuracy. Because of the
distinct characteristics inherent to specific cancerous gene expression profiles, developing flexible and
robust gene identification methods is extremely fundamental. Many gene selection methods as well as their
corresponding classifiers have been proposed. In the proposed method, a single gene with high classdiscrimination
capability is selected and classification rules are generated for cancer based on gene
expression profiles. The method first computes importance factor of each gene of experimental cancer
dataset by counting number of linguistic terms (defined in terms of different discreet quantity) with high
class discrimination capability according to their depended degree of classes. Then initial important genes
are selected according to high importance factor of each gene and form initial reduct. Then traditional kmeans
clustering algorithm is applied on each selected gene of initial reduct and compute missclassification
errors of individual genes. The final reduct is formed by selecting most important genes with
respect to less miss-classification errors. Then a classifier is constructed based on decision rules induced
by selected important genes (single) from training dataset to classify cancerous and non-cancerous samples
of experimental test dataset. The proposed method test on four publicly available cancerous gene
expression test dataset. In most of cases, accurate classifications outcomes are obtained by just using
important (single) genes that are highly correlated with the pathogenesis cancer are identified. Also to
prove the robustness of proposed method compares the outcomes (correctly classified instances) with some
existing well known classifiers.
How is machine learning significant to computational pathology in the pharmac...Pubrica
• Plentiful amassing of advanced histopathological pictures has prompted the expanded interest for their examination; for example, PC supported determination utilizing AI procedures.
• In this blog, Pubrica explains the applications of machine learning in digital pathology field using Biostatistics Services.
Continue Reading: https://bit.ly/37Vp6co
Reference: https://pubrica.com/services/research-services/biostatistics-and-statistical-programming-services/
Why Pubrica?
When you order our services, Plagiarism free|onTime|outstanding customer support|Unlimited Revisions support|High-quality Subject Matter Experts.
Contact us :
Web: https://pubrica.com/
Blog: https://pubrica.com/academy/
Email: sales@pubrica.com
WhatsApp : +91 9884350006
United Kingdom: +44- 74248 10299
Part of the "2016 Annual Conference: Big Data, Health Law, and Bioethics" held at Harvard Law School on May 6, 2016.
This conference aimed to: (1) identify the various ways in which law and ethics intersect with the use of big data in health care and health research, particularly in the United States; (2) understand the way U.S. law (and potentially other legal systems) currently promotes or stands as an obstacle to these potential uses; (3) determine what might be learned from the legal and ethical treatment of uses of big data in other sectors and countries; and (4) examine potential solutions (industry best practices, common law, legislative, executive, domestic and international) for better use of big data in health care and health research in the U.S.
The Petrie-Flom Center for Health Law Policy, Biotechnology, and Bioethics at Harvard Law School 2016 annual conference was organized in collaboration with the Berkman Center for Internet & Society at Harvard University and the Health Ethics and Policy Lab, University of Zurich.
Learn more at http://petrieflom.law.harvard.edu/events/details/2016-annual-conference.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Unravelling the molecular linkage of co morbid diseaseseSAT Journals
Abstract ABSTRACT : The incidence of Diabetes Mellitus (DM), Hypertension (HTN) and Coronary artery disease (CAD) in the country has increased alarmingly. Since decades DM and HTN have been proved to be independent risk factors for CAD. Gene and its regulatory action through a protein are vital for the normal metabolism. Any abnormality in regulation would lead to a disease. Our study used the principles of network biology to understand the comorbidity of diseases at the molecular level. We have collected disease genes of DM, HTN and CAD from various public databases and extracted genes common to all the three diseases. We constructed a biological network by considering the protein interaction data obtained from Human Protein Reference Database (HPRD).The network was validated using power law distribution and the genes were ranked using Centiscape. Finally we identified the crucial genes with literature validation which could play a major role in causing disease co-morbidity. Keywords –Biological Network, Coronary Artery Disease, Diabetes Mellitus, Hypertension and Systems Biology
Thank You for referencing this work, if you find it useful!
Citation of a related scientific paper:
Berrocal, A., Manea, V., De Masi, A., Wac, K. mQoL-Lab: Step-by-Step Creation of a Flexible Platform to Conduct Studies Using Interactive, Mobile, Wearable and Ubiquitous Devices, MobiSPC 2020
The talk details:
Alexandre De Masi, Igor Matias, Vlad Manea, Allan Berrocal, Katarzyna Wac, “mQoL: Methodology for Assessing and Modeling Human Aspects in Interactive, Mobile, Wearable and Ubiquitous Computing in Situ”, International Transdisciplinarity Conference (ITD) 2021, September 2021.
Video: https://youtu.be/dIxOnqd5g8E
MINING OF IMPORTANT INFORMATIVE GENES AND CLASSIFIER CONSTRUCTION FOR CANCER ...ijsc
Microarray is a useful technique for measuring expression data of thousands or more of genes
simultaneously. One of challenges in classification of cancer using high-dimensional gene expression data
is to select a minimal number of relevant genes which can maximize classification accuracy. Because of the
distinct characteristics inherent to specific cancerous gene expression profiles, developing flexible and
robust gene identification methods is extremely fundamental. Many gene selection methods as well as their
corresponding classifiers have been proposed. In the proposed method, a single gene with high classdiscrimination
capability is selected and classification rules are generated for cancer based on gene
expression profiles. The method first computes importance factor of each gene of experimental cancer
dataset by counting number of linguistic terms (defined in terms of different discreet quantity) with high
class discrimination capability according to their depended degree of classes. Then initial important genes
are selected according to high importance factor of each gene and form initial reduct. Then traditional kmeans
clustering algorithm is applied on each selected gene of initial reduct and compute missclassification
errors of individual genes. The final reduct is formed by selecting most important genes with
respect to less miss-classification errors. Then a classifier is constructed based on decision rules induced
by selected important genes (single) from training dataset to classify cancerous and non-cancerous samples
of experimental test dataset. The proposed method test on four publicly available cancerous gene
expression test dataset. In most of cases, accurate classifications outcomes are obtained by just using
important (single) genes that are highly correlated with the pathogenesis cancer are identified. Also to
prove the robustness of proposed method compares the outcomes (correctly classified instances) with some
existing well known classifiers.
How is machine learning significant to computational pathology in the pharmac...Pubrica
• Plentiful amassing of advanced histopathological pictures has prompted the expanded interest for their examination; for example, PC supported determination utilizing AI procedures.
• In this blog, Pubrica explains the applications of machine learning in digital pathology field using Biostatistics Services.
Continue Reading: https://bit.ly/37Vp6co
Reference: https://pubrica.com/services/research-services/biostatistics-and-statistical-programming-services/
Why Pubrica?
When you order our services, Plagiarism free|onTime|outstanding customer support|Unlimited Revisions support|High-quality Subject Matter Experts.
Contact us :
Web: https://pubrica.com/
Blog: https://pubrica.com/academy/
Email: sales@pubrica.com
WhatsApp : +91 9884350006
United Kingdom: +44- 74248 10299
Part of the "2016 Annual Conference: Big Data, Health Law, and Bioethics" held at Harvard Law School on May 6, 2016.
This conference aimed to: (1) identify the various ways in which law and ethics intersect with the use of big data in health care and health research, particularly in the United States; (2) understand the way U.S. law (and potentially other legal systems) currently promotes or stands as an obstacle to these potential uses; (3) determine what might be learned from the legal and ethical treatment of uses of big data in other sectors and countries; and (4) examine potential solutions (industry best practices, common law, legislative, executive, domestic and international) for better use of big data in health care and health research in the U.S.
The Petrie-Flom Center for Health Law Policy, Biotechnology, and Bioethics at Harvard Law School 2016 annual conference was organized in collaboration with the Berkman Center for Internet & Society at Harvard University and the Health Ethics and Policy Lab, University of Zurich.
Learn more at http://petrieflom.law.harvard.edu/events/details/2016-annual-conference.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Unravelling the molecular linkage of co morbid diseaseseSAT Journals
Abstract ABSTRACT : The incidence of Diabetes Mellitus (DM), Hypertension (HTN) and Coronary artery disease (CAD) in the country has increased alarmingly. Since decades DM and HTN have been proved to be independent risk factors for CAD. Gene and its regulatory action through a protein are vital for the normal metabolism. Any abnormality in regulation would lead to a disease. Our study used the principles of network biology to understand the comorbidity of diseases at the molecular level. We have collected disease genes of DM, HTN and CAD from various public databases and extracted genes common to all the three diseases. We constructed a biological network by considering the protein interaction data obtained from Human Protein Reference Database (HPRD).The network was validated using power law distribution and the genes were ranked using Centiscape. Finally we identified the crucial genes with literature validation which could play a major role in causing disease co-morbidity. Keywords –Biological Network, Coronary Artery Disease, Diabetes Mellitus, Hypertension and Systems Biology
Stanford University Neuroradiology Talks: Personalizing and Customizing AI Ex...Instituto Superior Técnico
Invited to present the work under development by Institute for Systems and Robotics (ISR-Lisboa) and Interactive Technologies Institute (ITI), the one-hour presentation and discussion were held in the 27th of October 2022. The work was presented remotely to the Department of Rad/Neuroimaging and Neurointervention at Stanford University in California. For this talk, I was invited to present our team, project, and work to the research team of Prof. Greg Zaharchuk. In the end, the presentation proposes and discusses how personalizing and customizing the answers coming from the AI outputs can positively affect the clinical workflow. Moreover, we present how those strategies are promoting the unbiased behavior of clinicians while improving the clinical workflow.
Top downloaded article in academia 2020 - International Journal of Computatio...ijcsity
International Journal of Computational Science and Information Technology (IJCSITY) focuses on Complex systems, information and computation using mathematics and engineering techniques. This is an open access peer-reviewed journal will act as a major forum for the presentation of innovative ideas, approaches, developments, and research projects in the area of Computation theory and applications. It will also serve to facilitate the exchange of information between researchers and industry professionals to discuss the latest issues and advancement in the area of advanced Computation and its applications.
References on Reproducibility Crisis in Science by D.V.M. BishopDorothy Bishop
References to accompany talk delivered at Rhodes House, Oxford on 3rd May 2016.
For slides see: http://www.slideshare.net/deevybishop/what-is-the-reproducibility-crisis-in-science-and-what-can-we-do-about-it
References on Reproducibility Crisis in Science by D.V.M. Bishop
ORG_CV_CWANG
1. Chenguang Wang, Ph.D.
Contact
Information
Division of Biostatistics and Bioinformatics
Sidney Kimmel Comprehensive Cancer Center
School of Medicine
Johns Hopkins University
550 N. Broadway Suite 1103
Baltimore, MD 21205
Office: (410)614-4961
Email: cwang68@jhmi.edu
Academic
Training
Doctor of Philosophy, Statistics, University of Florida 2010
Master of Science, Statistics, University of Nebraska-Lincoln 2004
Master of Engineering, Computer Science, Dalian University of Technology 2001
Bachelor of Engineering, Computer Science, Dalian University of Technology 1998
Professional
Experience
Assistant Professor, Johns Hopkins University 2011-present
Mathematical Statistician, FDA 2009 - 2011
Statistician, Children’s Oncology Group 2004 - 2009
Articles
Published
2016 Wang C, Scharfstein D, Colantuoni E, Girard TD, Yan Y. "Inference in Randomized
Trials With Death and Missingness". Biometrics. Accepted.
2016 Henderson NC, Louis TA, Wang C, Varadhan R. "Bayesian Analysis of Heterogeneous
Treatment Effects for Patient-Centered Outcomes Research". Health Services and Outcomes
Research Methodology. Accepted.
2016 Alvarez RD, Huh WK, Bae S, Lamb LS, Conner MG, Boyer J, Wang C, Hung C,
Sauter E, Paradis M, others. "A pilot study of pNGVL4a-CRT/E7 (detox) for the treatment
of patients with HPV16+ cervical intraepithelial neoplasia 2/3 (CIN2/3)". Gynecologic
oncology. vol. 140, pp. 245-252. PMCID:4724445.
2016 Huang C, Wang C, Wang M. "Nonparametric analysis of bivariate gap time With
competing risks". Biometrics. pp. 1-11.
2015 Peng S, Wang JW, Karanam B, Wang C, Huh WK, Alvarez RD, Pai SI, Hung C, Wu
T-, Roden RBS. "Sequential Cisplatin Therapy and Vaccination With Hpv16 E6e7l2 Fusion
Protein in Saponin Adjuvant Gpi-0100 for the Treatment of a Model Hpv16+ Cancer". PLoS
ONE. vol. 10, pp. e116389.
2014 Daniels M, Wang C, Marcus B. "Fully Bayesian inference under ignorable missingness
in the presence of auxiliary covariates". Biometrics. vol. 70, pp. 62-72. PMCID:4007313.
2014 Kharrazi H, Wang C, Scharfstein D. "Prospective EHR-based clinical trials: the chal-
lenge of missing data". Journal of general internal medicine. vol. 29, pp. 976-978. PM-
CID:4061350.
2014 Maldonado L, Teague JE, Morrow, P. M, Jotova I, Wu T, Wang C, Desmarais C, Boyer
JD, Tycko B, Robins HS, others. "Intramuscular therapeutic vaccination targeting HPV16
induces T cell responses that localize in mucosal lesions". Science translational medicine.
1 of 6
2. vol. 6, pp. 221ra13-221ra13. PMCID:4086631.
2014 Wu C, Yang L, Yang H, Knoff J, Peng S, Lin Y, Wang C, Alvarez RD, Pai SI,
Roden RB, others. "Enhanced cancer radiotherapy through immunosuppressive stromal cell
destruction in tumors". Clinical cancer research. vol. 20, pp. 644-657. PMCID:3946442.
2013 Kwak K, Jiang R, Jagu S, Wang JW, Wang C, Christensen ND, Roden RB. "Multi-
valent human papillomavirus l1 DNA vaccination utilizing electroporation". PloS one. vol.
8, pp. e60507. PMCID:3607584.
2013 Liu G, Kong L, Wang Z, Wang C, Wu R. "Systems mapping of metabolic genes through
control theory". Advanced drug delivery reviews. vol. 65, pp. 918-928. PMCID:4233129.
2013 Zhang Z, Wang C, Nie L, Soon G. "Assessing the heterogeneity of treatment effects
via potential outcomes of individual patients". Journal of the royal statistical society: series
C (applied statistics). vol. 62, pp. 687-704. PMCID:4261197.
2013 Zhang Z, Kotz RM, Wang C, Ruan S, Ho M. "A causal model for joint evaluation of
placebo and treatment-specific effects in clinical trials". Biometrics. vol. 69, pp. 318-327.
PMCID:4133792.
2013 Lee SY, Huang Z, Kang TH, Soong R, Knoff J, Axenfeld E, Wang C, Alvarez RD,
Chen C, Hung C, others. "Histone deacetylase inhibitor Ar-42 enhances E7-specific Cd8+ T
cell-mediated antitumor immunity induced by therapeutic HPV DNA vaccination". Journal
of molecular medicine. vol. 91, pp. 1221-1231. PMCID:3783646.
2012 Daniels MJ, Chatterjee AS, Wang C. "Bayesian model selection for incomplete data
using the posterior predictive distribution". Biometrics. vol. 68, pp. 1055-1063. PM-
CID:3890150.
2012 Wang C, Li H, Wang Z, Wang Y, Wang N, Wang Z, Wu R. "A maximum likelihood
approach to functional mapping of longitudinal binary traits". Statistical applications in
genetics and molecular biology. vol. 11, pp. 1-17. PMCID:3856886.
2011 Bonangelino P, Irony T, Liang S, Li X, Mukhi V, Ruan S, Xu Y, Yang X, Wang C.
"Bayesian approaches in medical device clinical trials: a discussion with examples in the
regulatory setting". Journal of biopharmaceutical statistics. vol. 21, pp. 938-953.
2011 Wang W, Scharfstein D, Wang C, Daniels M, Needham D, Brower R. "Estimating the
causal effect of low tidal volume ventilation on survival in patients with acute lung injury".
Journal of the royal statistical society: series C (applied statistics). vol. 60, pp. 475-496.
PMCID:3197806.
2011 Wang C, Daniels MJ. "A note on MAR, identifying restrictions, model comparison,
and sensitivity analysis in pattern mixture models with and without covariates for incomplete
data". Biometrics. vol. 67, pp. 810-818. PMCID:3136648.
2011 Wang C, Wang Z, Prows DR, Wu R. "A computational framework for the inheritance
pattern of genomic imprinting for complex traits". Briefings in bioinformatics. vol. 13, pp.
34-45. PMCID:3278998.
2010 Li Q, Huang Z, Xu M, Wang C, Gai J, Huang Y, Pang X, Wu R. "Functional mapping
of genotype-environment interactions for soybean growth by a semiparametric approach".
Plant methods. vol. 6, pp. 13. PMCID:2903578.
2010 Wang C, Wang Z, Luo J, Li Q, Li Y, Ahn K, Prows DR, Wu R. "A model for
transgenerational imprinting variation in complex traits". PLoS one. vol. 5, pp. e11396.
PMCID:2904369.
2010 Wang C, Daniels M, Scharfstein DO, Land S. "A Bayesian shrinkage model for in-
complete longitudinal binary data with application to the breast cancer prevention trial".
Journal of the American statistical association. vol. 105, pp. 1333-1346. PMCID:3079242.
2 of 6
3. 2009 Crawford Jr T, Eskridge K, Wang C, Maranville J. "Multi-compartmental modeling
of nitrogen translocation in sorghums differing in nitrogen use efficiency". Journal of plant
nutrition. vol. 32, pp. 335-349.
2009 Daniels MJ, Wang C. "Comments on: missing data methods in longitudinal studies:
a review". TEST (Journal of the Spanish society of statistics and operations research). vol.
18, pp. 51-58.
2009 Pinedo PJ, Wang C, Li Y, Rae, Owen D, Wu R. "Risk haplotype analysis for bovine
paratuberculosis". Mammalian genome. vol. 20, pp. 124-129.
2009 Schultz KR, Bowman WP, Aledo A, Slayton WB, Sather H, Devidas M, Wang C,
Davies SM, Gaynon PS, Trigg M, others. "Improved early event-free survival with imatinib
in Philadelphia chromosome-positive acute lymphoblastic leukemia: a Children’s Oncology
Group study". Journal of clinical oncology. vol. 27, pp. 5175-5181. PMCID:2773475.
2009 Wen S, Wang C, Berg A, Li Y, Chang MM, Fillingim RB, Wallace MR, Staud R,
Kaplan L, Wu R. "Modeling genetic imprinting effects of DNA sequences with multilocus
polymorphism data". Algorithms for molecular biology. vol. 4, pp. 1-11. PMCID:2739217.
2008 Wang C, Cheng Y, Liu T, Li Q, Fillingim RB, Wallace MR, Staud R, Kaplan L,
Wu R. "A computational model for sex-specific genetic architecture of complex traits in
humans: implications for mapping pain sensitivity". Molecular pain. vol. 4, pp. 1-10.
PMCID:2422840.
2008 Zeng Y, Li J, Wang C, Chang M, Yang R, Wu R. "Genetic mapping of quantitative
trait loci". Principles and practices of plant genomics. vol. 1, pp. 175-204.
2007 Salzer WL, Devidas M, Shuster, J. J, Wang C, Chauvenet A, Asselin BL, Camitta BM,
Kurtzberg J. "Intensified PEG-L-asparaginase and antimetabolite-based therapy for treat-
ment of higher risk precursor-B acute lymphoblastic leukemia: a report from the Children’s
Oncology Group". Journal of pediatric hematology oncology. vol. 29, pp. 369-375.
2007 Wu S, Yang J, Wang C, Wu R. "A general quantitative genetic model for haplotyping
a complex trait in humans". Current genomics. vol. 8, pp. 343-350. PMCID:2652406.
2007 Yap JS, Wang C, Wu R. "A computational approach for functional mapping of quan-
titative trait loci that regulate thermal performance curves". PLoS One. vol. 2, pp. e554.
PMCID:1892808.
2001 Chi Z, Quan X, Wang C, Wang Z. "Using multiagent adapter to design component
software". Journal of Chinese computer systems. vol. 22, pp. 608-610.
2000 Chi Z, Quan X, Wang C, Wang Z. "An implementation of a MultiSac model based
distributed GIS system". Mini-macro system. vol. 21, pp. 40-42.
1999 Wang Z, Chi Z, Wang C. "Hypermedia management system on distributed object".
Journal of dalian university of technology. vol. 39, pp. 572-576.
Statistical
Software
"Inference in Randomized Controlled Trials with Death and Missingness". R package idem
on CRAN with web graphical user interface.
"Bayesian Analysis of Heterogeneous Treatment Effect". R package beanz on CRAN with
web graphical user interface.
Book Chapter 2000 Zeng Y, Li J, Wang C, Chang M, Yang R, Wu L. "Genetic Mapping of Quantitative
Trait Loci" in Principles and Practices of Plant Genomics. vol. 1, pp. 175-204.
2007 Adamchuk VI, Wang C. "Col-locating multiple self-generated data layers" in GIS
Applications in Agriculture, CRC Press, pp. 185-196.
3 of 6
4. Presentations 2016 Wang C. "A Bayesian Non-Parametric Causal Inference Model for Comparative Ef-
fectiveness Research". JSM, Chicago, IL
2016 Wang C. "BEANZ: A Web-Based Software for Bayesian Analysis of Heterogeneous
Treatment Effect". The 2nd International iCSA conference, Suzhou, China
2016 Wang C. "Global Sensitivity Analysis for Studies with Intermittent Missing Data and
Death". 34th Graduate Summer Institute of Epidemiology and Biostatistics, Baltimore, MD
2016 Wang C. "Design and Analysis of Traditional Therapy Studies". ICSA, Organizer and
chair, Atlanta, GA
2016 Wang C. "Discussion of subgroup identification and analysis". ICSA, Atlanta, GA
2016 Wang C. "BEANZ: A Web-Based Software for Bayesian Analysis of Heterogeneous
Treatment Effect". ICSA, Atlanta, GA
2016 Wang C, Varadhan R. "Introduction to web-application BEANZ". FDA, Washington
D.C.
2015 Wang C. "Next generation clinical trials". International symposium focused on clinical
research of anesthesiology and traditional Chinese medicine, Beijing, China
2015 Wang C. "A novel design for phase I cancer vaccine trials". FDA, CBER, Washington
D.C.
2015 Wang C, Varadhan R. "BEANZ: A web-based software for Bayesian analysis of HTE
in PCOR". ICHPS, Providence, RI
2015 Wang C, Scharfstein D. "Short course: global sensitivity analysis for studies with
intermittent missing data and death". FDA, Washington D.C.
2015 Wang C, Scharfstein D. "Short course: global sensitivity analysis for studies with
intermittent missing data and death". Johns Hopkins University, Baltimore, MD
2015 Wang C, Rosner G. "A Bayesian non-parametric model for synergy assessment in drug
combination studies ". ICSA/Graybill Joint Conference, Fort Collins, CO
2014 Wang C. "A novel design for phase I cancer vaccine trials". Duke-NUS, Singapore
2014 Wang C, Scharfstein D. "An intermittent missing data analysis strategy: for clinical
trials with death-truncated data". FDA Industry Statistical Workshop, Washington D.C.
2014 Wang C, Rosner G. "A Bayesian Dirichelet process mixture model for comparative
effectiveness research". IMS, Sydney, Australia
2014 Wang C, Rosner G. "A Bayesian non-parametric causal inference model for compar-
ative effectiveness research". SRCOS, Galveston, TX
2014 Zhang Z, Wang C, Nie L, Soon G. "Assessing the heterogeneity of treatment effects
via potential outcomes of individual patients". ENAR, Baltimore, MD
2014 Wang C, Hua Y. "A Bayesian missing data analysis model for estimating and com-
paring diagnostic test accuracy". ENAR, Baltimore, MD
2014 Wang J, Wang C. "An R package for sensitivity analysis on longitudinal data with
non-ignorable intermittent missingness". ENAR, Baltimore, MD
2014 Scharfstein D, Wang C. "Case study: death and missing data in a randomized trial".
Clinical Research Grand Rounds at the Welch Centre, Johns Hopkins University, Baltimore,
MD
2013 Wang C. "Statistical genetics: concepts and application". Ocean University of China,
Qingdao, China
2013 Wang C. "Missing data modeling and analysis concept and example". Tianjin University
of Finance and Economics, Tianjin, China
4 of 6
5. 2013 Wang C. "Missing data modeling and analysis". Shandong Coal Technology College,
Yantai, China
2013 Wang C, Scharfstein D. "A sensitivity analysis model for longitudinal data with non-
ignorable intermittent missingness". ICSA, Hongkong, China
2013 Wang C. "Short course: missing data analysis". SLAM, Johns Hopkins University,
Baltimore, MD
2013 Wang C. "Blinding and placebo effects in randomized clinical trials". Joint Statistical
Meeting, co-organizer and discussant, Montreal, Canada
2013 Wang C. "Causal inference in clinical trials". ICSA, chair, Washington D.C.
2013 Wang C. "A Bayesian missing data model for evaluating diagnostic medical devices".
ENAR, Orlando, FL
2012 Wang C. "A causal effect model with stochastic monotonicity assumption for clinical
trials with incomplete longitudinal outcome". JSM, San Diego, CA
2012 Wang C. "Traps of assumptions: Wilcoxon signed-rank test and more". The 4th
International Statistical Genetics Symposium, Beijing, China
2012 Wang C. "Short course: maximum likelihood approach, experimental design". The
4th International Statistical Genetics Symposium, Beijing, China
2012 Wang C. "A Bayesian causal effect model with weak monotonicity assumptions for
clinical trials". ISBA, Kyoto, Japan
2012 Wang C. "Fully Bayesian inference under ignorable missingness in the presence of
auxiliary covariates". SLAM, Johns Hopkins University, Baltimore, MD
2011 Wang C. "A Bayesian shrinkage model for incomplete longitudinal binary data".
Tianjin University of Finance and Economics, Tianjin, China
2011 Wang C. "Clinical significance in statistical hypothesis testing". International Drug
Discovery Science and Technology, Shenzhen, China
2011 Wang C. "A causal effect model with stochastic monotonicity assumption". ICSA,
New York, NY
2010 Wang C, Daniels M, Scharfstein D, Land S. "A Bayesian shrinkage model for incom-
plete longitudinal binary data with application to the breast cancer prevention trial". Joint
Statistical Meeting, Vancouver, Canada
2010 Wang C, Daniels M, Scharfstein D, Land S. "A Bayesian shrinkage model for incom-
plete longitudinal binary data". ENAR, New Orleans, LA
2010 Wang C, Scharfstein D, Daniels M. "Using R and OpenBUGS for evaluating the causal
effect of dynamic treatments regimes". Johns Hopkins University, Baltimore, MD
2010 Wang C, Scharfstein D, Daniels M. "Estimating the causal effect of dynamic treatment
regimes with application to an acute lung injury trial". FDA Industry Statistics Workshop,
Washington D.C.
2010 Wang C. "Missing data". FDA/MTLI Medical Device and IVD Statistics Workshop,
chair, Washington D.C.
2010 Wang C, Daniels M. "Bayesian shrinkage models for incomplete longitudinal binary
data". FDA, CDRH, Washington D.C.
2010 Wang C, Daniels M. "Bayesian shrinkage models for incomplete longitudinal binary
data". Yahoo, Sunnyvale, CA
2009 Wang C, Daniels M. "A note on MAR, identifying restrictions, and sensitivity analysis
in pattern mixture models". South Regional Conference on Statistics (SRCOS): Summer
5 of 6
6. Research Conferences (SRC), poster, Jekyll Island, GA
2009 Daniels M, Wang C, Scharfstein D. "Bayesian semi-parametric selection models with
application to a breast cancer prevention trial". ENAR, San Antonio, TX
2009 Wang C, Daniels M. "Identification strategies for pattern mixture models with covari-
ates". ENAR, San Antonio, TX
2008 Wang C, Daniels M, Scharfstein D. "Bayesian semi-parametric selection models with
application to a breast cancer prevention trial". South Regional Conference on Statistics
(SRCOS): Summer Research Conferences (SRC), poster, Charleston, SC
2007 Wang C, Berg A, Li Q, Wu R. "A statistical strategy for mapping imprinted quan-
titative trait loci: implications for genetic mapping in mice". Florida Genetics Symposium,
poster, Gainesville, FL
2007 Wang C, Li Q, Wu R. "High dimensional model for understanding the genetic network
of ontogenetic allometry". ENAR, Atlanta, GA
2005 Crawford T, Eskridge K, Wang C, Maryville J. "Nitrogen translocation in N-efficient
and N-inefficient cultivars of sorghum". North American Grain Congress, poster, Reno, NV
2004 Crawford T, Eskridge K, Wang C, Maryville J. "Nitrogen translocation in N-efficient
and N-inefficient cultivars of sorghum". American Society of Agronomy annual meeting,
poster, Seattle, WA
Awards Outstanding Junior Investigator, FDA, 2011
Professional
Activities
Associate Editor of Communications for Statistical Applications and Methods
Statistical Advisory Board Member of PLOS ONE
Review Editor of Frontiers in Statistical Genetics and Methodology
Reviewer of Biometrics, Biostatistics, Statistics in Medicine, Journal of Biopharmaceutical
Statistics
Reviewer of Statistical Modelling, Statistics and Its Interface, Journal of Biomedical Science
Reviewer of BMC Genetics, PLoS Genetics, Evolution, Theoretical Biology
Reviewer of Cancer Research, Clinical Trials, Advanced Drug Delivery
Professional
Membership
Society for Clinical Trials (SCT)
American Statistical Association (ASA)
Institute of Mathmatical Statistics (IMS)
International Society for Bayesian Analysis (ISBA)
Eastern North American Region/International Biometric Society (ENAR)
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