This curriculum vitae summarizes the qualifications and experience of Weiliang Qiu. Qiu has over 12 years of experience in data analysis, especially of clinical trial and observational data. He has published over 70 peer-reviewed papers and edited two academic journals. Qiu has a Ph.D. in Statistics and is currently an Associate Biostatistician and Assistant Professor at Brigham and Women's Hospital, where he provides statistical support for clinical trials and develops novel statistical methods.
To date, the Registry has epidemiology and quality-of-life data from 11,180 self-registered patients with 4,196
well-characterized samples of DNA, lymphoblast lines, and sera for future research studies.
A recognized expert with 16 years of experience on translational bioinformatics, genomics, and genetics, specialized on developing databases, genome repositories, pipeline, clinical application, and commercial products to analyze next generation sequencing data, and interpret personal genomes and electronic medical records for clinical diagnosis, therapeutics, precision medicine, and predictive disease risk.
Summary, outcomes and action plan presented by Dr. Angela Christiano at the end of the two-day Alopecia Areata Research Summit held November 14-15, 2016 in New York, NY.
Advancing Innovation and Convergence in Cancer Research: US Federal Cancer Mo...Jerry Lee
Special Seminar at the 8th Taiwan Biosignatures Workshop to share overall work of NCI's Center for Strategic Scientific Initiatives since 2003 as well as CSSI's influence on select projects initiated by the 2016 WH Cancer Moonshot Task Force that include Applied Proteogenomics Organizational Learning and Outcomes (APOLLO) network, International Cancer Proteogenome Consortium, and the Blood Profiling Atlas in Cancer (BloodPAC) commons.
To date, the Registry has epidemiology and quality-of-life data from 11,180 self-registered patients with 4,196
well-characterized samples of DNA, lymphoblast lines, and sera for future research studies.
A recognized expert with 16 years of experience on translational bioinformatics, genomics, and genetics, specialized on developing databases, genome repositories, pipeline, clinical application, and commercial products to analyze next generation sequencing data, and interpret personal genomes and electronic medical records for clinical diagnosis, therapeutics, precision medicine, and predictive disease risk.
Summary, outcomes and action plan presented by Dr. Angela Christiano at the end of the two-day Alopecia Areata Research Summit held November 14-15, 2016 in New York, NY.
Advancing Innovation and Convergence in Cancer Research: US Federal Cancer Mo...Jerry Lee
Special Seminar at the 8th Taiwan Biosignatures Workshop to share overall work of NCI's Center for Strategic Scientific Initiatives since 2003 as well as CSSI's influence on select projects initiated by the 2016 WH Cancer Moonshot Task Force that include Applied Proteogenomics Organizational Learning and Outcomes (APOLLO) network, International Cancer Proteogenome Consortium, and the Blood Profiling Atlas in Cancer (BloodPAC) commons.
Rhetorical moves and audience considerations in the discussion sections of ra...jodischneider
European Conference on Argumentation talk
Jodi Schneider, Graciela Rosemblat, Shabnam Tafreshi and Halil Kilicoglu “Rhetorical moves and audience considerations in the discussion sections of Randomized Controlled Trials of health interventions” [Conference Panel Presentation], 2nd European Conference on Argumentation: Argumentation and Inference, Fribourg, Switzerland, June 20-23
1 of 3 talks in Jodi Schneider and Sally Jackson, organizers, “Innovations in Reasoning and Arguing about Health ”[Conference Panel], 2nd European Conference on Argumentation: Argumentation and Inference, Fribourg, Switzerland, June 20-23.
Contribution of genome-wide association studies to scientific research: a pra...Mutiple Sclerosis
Vito A. G. Ricigliano, Renato Umeton, Lorenzo Germinario, Eleonora Alma, Martina Briani, Noemi Di Segni, Dalma Montesanti, Giorgia Pierelli, Fabiana Cancrini, Cristiano Lomonaco, Francesca Grassi, Gabriella Palmieri, and Marco Salvetti,
Struan Frederick Airth Grant, Editor
The factual value of genome-wide association studies (GWAS) for the understanding of multifactorial diseases is a matter of intense debate. Practical consequences for the development of more effective therapies do not seem to be around the corner. Here we propose a pragmatic and objective evaluation of how much new biology is arising from these studies, with particular attention to the information that can help prioritize therapeutic targets. We chose multiple sclerosis (MS) as a paradigm disease and assumed that, in pre-GWAS candidate-gene studies, the knowledge behind the choice of each gene reflected the understanding of the disease prior to the advent of GWAS. Importantly, this knowledge was based mainly on non-genetic, phenotypic grounds. We performed single-gene and pathway-oriented comparisons of old and new knowledge in MS by confronting an unbiased list of candidate genes in pre-GWAS association studies with those genes exceeding the genome-wide significance threshold in GWAS published from 2007 on. At the single gene level, the majority (94 out of 125) of GWAS-discovered variants had never been contemplated as plausible candidates in pre-GWAS association studies. The 31 genes that were present in both pre- and post-GWAS lists may be of particular interest in that they represent disease-associated variants whose pathogenetic relevance is supported at the phenotypic level (i.e. the phenotypic information that steered their selection as candidate genes in pre-GWAS association studies). As such they represent attractive therapeutic targets. Interestingly, our analysis shows that some of these variants are targets of pharmacologically active compounds, including drugs that are already registered for human use. Compared with the above single-gene analysis, at the pathway level GWAS results appear more coherent with previous knowledge, reinforcing some of the current views on MS pathogenesis and related therapeutic research. This study presents a pragmatic approach that helps interpret and exploit GWAS knowledge.
Citation practices and the construction of scientific fact--ECA-facts-preconf...jodischneider
Citation practices and the construction of scientific fact. Presentation at the European Conference on Argumentation preconference on status, relevance, and authority of facts.
TIGA: Target Illumination GWAS AnalyticsJeremy Yang
Aggregating and assessing experimental evidence for interpretable, explainable, accountable gene-trait associations. Presentation for NIH IDG Annual Meeting, Feb 9-11, 2021.
From Bits to Bedside: Translating Big Data into Precision Medicine and Digita...Dexter Hadley
Lecture Objectives:
1) To use examples from my research to define and introduce the ideals of precision medicine and digital health. 2) To introduce how large scale population-wide analysis of data can be used to facilitate these two ideals. 3) To introduce how freely available open data can be used to facilitate these two ideals. 4) To show how mobile technology can be used to facilitate these two ideals.
Forum on Personalized Medicine: Challenges for the next decadeJoaquin Dopazo
Bioinformatics and Big Data in the era of Personalized Medicine
10th Anniversary Instituto Roche Forum on Personalized Medicine: Challenges for the next decade.
Santiago de Compostela (Spain), September 25th 2014
The reality of moving towards precision medicineElia Stupka
How do we move towards precision medicine? How can we deliver on the big data in health promise? Who will be the enablers and players? Pharma, Big Tech, or newcomers?
What WikiCite can learn from biomedical citation networks--Wikicite2017--2017...jodischneider
This is a quick, high-level tour of some ideas from evidence-based medicine, citation-related ontologies for argumentation and evidence curation and biomedicine.
Rhetorical moves and audience considerations in the discussion sections of ra...jodischneider
European Conference on Argumentation talk
Jodi Schneider, Graciela Rosemblat, Shabnam Tafreshi and Halil Kilicoglu “Rhetorical moves and audience considerations in the discussion sections of Randomized Controlled Trials of health interventions” [Conference Panel Presentation], 2nd European Conference on Argumentation: Argumentation and Inference, Fribourg, Switzerland, June 20-23
1 of 3 talks in Jodi Schneider and Sally Jackson, organizers, “Innovations in Reasoning and Arguing about Health ”[Conference Panel], 2nd European Conference on Argumentation: Argumentation and Inference, Fribourg, Switzerland, June 20-23.
Contribution of genome-wide association studies to scientific research: a pra...Mutiple Sclerosis
Vito A. G. Ricigliano, Renato Umeton, Lorenzo Germinario, Eleonora Alma, Martina Briani, Noemi Di Segni, Dalma Montesanti, Giorgia Pierelli, Fabiana Cancrini, Cristiano Lomonaco, Francesca Grassi, Gabriella Palmieri, and Marco Salvetti,
Struan Frederick Airth Grant, Editor
The factual value of genome-wide association studies (GWAS) for the understanding of multifactorial diseases is a matter of intense debate. Practical consequences for the development of more effective therapies do not seem to be around the corner. Here we propose a pragmatic and objective evaluation of how much new biology is arising from these studies, with particular attention to the information that can help prioritize therapeutic targets. We chose multiple sclerosis (MS) as a paradigm disease and assumed that, in pre-GWAS candidate-gene studies, the knowledge behind the choice of each gene reflected the understanding of the disease prior to the advent of GWAS. Importantly, this knowledge was based mainly on non-genetic, phenotypic grounds. We performed single-gene and pathway-oriented comparisons of old and new knowledge in MS by confronting an unbiased list of candidate genes in pre-GWAS association studies with those genes exceeding the genome-wide significance threshold in GWAS published from 2007 on. At the single gene level, the majority (94 out of 125) of GWAS-discovered variants had never been contemplated as plausible candidates in pre-GWAS association studies. The 31 genes that were present in both pre- and post-GWAS lists may be of particular interest in that they represent disease-associated variants whose pathogenetic relevance is supported at the phenotypic level (i.e. the phenotypic information that steered their selection as candidate genes in pre-GWAS association studies). As such they represent attractive therapeutic targets. Interestingly, our analysis shows that some of these variants are targets of pharmacologically active compounds, including drugs that are already registered for human use. Compared with the above single-gene analysis, at the pathway level GWAS results appear more coherent with previous knowledge, reinforcing some of the current views on MS pathogenesis and related therapeutic research. This study presents a pragmatic approach that helps interpret and exploit GWAS knowledge.
Citation practices and the construction of scientific fact--ECA-facts-preconf...jodischneider
Citation practices and the construction of scientific fact. Presentation at the European Conference on Argumentation preconference on status, relevance, and authority of facts.
TIGA: Target Illumination GWAS AnalyticsJeremy Yang
Aggregating and assessing experimental evidence for interpretable, explainable, accountable gene-trait associations. Presentation for NIH IDG Annual Meeting, Feb 9-11, 2021.
From Bits to Bedside: Translating Big Data into Precision Medicine and Digita...Dexter Hadley
Lecture Objectives:
1) To use examples from my research to define and introduce the ideals of precision medicine and digital health. 2) To introduce how large scale population-wide analysis of data can be used to facilitate these two ideals. 3) To introduce how freely available open data can be used to facilitate these two ideals. 4) To show how mobile technology can be used to facilitate these two ideals.
Forum on Personalized Medicine: Challenges for the next decadeJoaquin Dopazo
Bioinformatics and Big Data in the era of Personalized Medicine
10th Anniversary Instituto Roche Forum on Personalized Medicine: Challenges for the next decade.
Santiago de Compostela (Spain), September 25th 2014
The reality of moving towards precision medicineElia Stupka
How do we move towards precision medicine? How can we deliver on the big data in health promise? Who will be the enablers and players? Pharma, Big Tech, or newcomers?
What WikiCite can learn from biomedical citation networks--Wikicite2017--2017...jodischneider
This is a quick, high-level tour of some ideas from evidence-based medicine, citation-related ontologies for argumentation and evidence curation and biomedicine.
In the late Fall and Winter of 2018, the Pistoia Alliance in cooperation with Elsevier and charitable organizations Cures within Reach and Mission: Cure ran a datathon aiming to find drugs suitable for treatment of childhood chronic pancreatitis, a rare disease that causes extreme suffering. The datathon resulted in identification of four candidate compounds in a short time frame of just under three months. In this webinar our speakers discuss the technologies that made this leap possible
5th Tumor Models Boston July 2017 BrochureDiane McKenna
Tumor Models Boston 2017 will address the preclinical & clinical developments of the most promising therapies including targeted therapies, check-point inhibitors & CAR-T therapies and how these findings can be utilized to bridge the gap between preclinical and clinical studies.
This year's 3rd Annual TCGC: The Clinical Genome Conference, held June 10-12, 2014 in San Francisco, is a three-day event that weaves together the science of sequencing and the business of implementing genomics in the clinic. It uniquely illustrates the mutual influence of those areas and the need to therefore consider the needs, challenges and opportunities of both - from next-generation sequencing and variant interpretation to insurance reimbursement and electronic health records - throughout the entire research process.Learn more at http://www.clinicalgenomeconference.com
Leveraging Publicly Accessible Clinical Trails Data Sharing, Dissemination an...Vaticle
In the broader realm of the advancement of science and the betterment of the human condition, there are several purported benefits for sharing clinical trials and research data. The scientific community has just begun to embrace open-access datasets to build their knowledge base, gain insight into new discoveries, and generate novel data-driven hypotheses that were not initially formulated in the studies. With the increasing amount of clinical trial data available, comes the need to leverage a multitude of shared datasets. Your knowledge base needs to facilitate discovery across research domains.
This talk highlights the data sharing, dissemination, and repurposing of clinical and molecular studies generated by government-funded research consortia. Further, we are building a new knowledge base resource, IMMGRAKN to facilitate translational discovery from crowd-sourced clinical trials data in ImmPort (www.immport.org), an NIH-NIAID funded open-access immunology database and analysis portal. The case studies demonstrating the use of IMMGRAKN will be discussed
Mel Reichman on Pool Shark’s Cues for More Efficient Drug DiscoveryJean-Claude Bradley
Mel Reichman, senior investigator and director of the LIMR Chemical Genomics Center at the Lankenau Institute for Medical Research presents at the chemistry department at Drexel University on November 12, 2009.
Modern drug discovery by high-throughput screening (HTS) begins with testing hundreds of thousands of compounds in biological assays. The confirmed hit rate for typical HTS is less than 0.5%; therefore, 99.5% of the costs of HTS are for generating null data. Orthogonal convolution of compound libraries (OCL) is 500% more efficient than present HTS practice. The OCL method combines 10 compounds per well. An advantage of this method is that each compound is represented twice in two separately arrayed pools. The potential for the approach to better enable academic centers of excellence to validate medicinally relevant biological targets is discussed.
Early diagnosis of cancers is a major requirement for patients and a
complicated job for the oncologist. If it is diagnosed early, it could have made
the patient more likely to live. For a few decades, fuzzy logic emerged as an
emphatic technique in the identification of diseases like different types of
cancers. The recognition of cancer diseases mostly operated with inexactness,
inaccuracy, and vagueness. This paper aims to design the fuzzy expert system
(FES) and its implementation for the detection of prostate cancer. Specifically,
prostate-specific antigen (PSA), prostate volume (PV), age, and percentage
free PSA (%FPSA) are used to determine prostate cancer risk (PCR), while
PCR serves as an output parameter. Mamdani fuzzy inference method is used
to calculate a range of PCR. The system provides a scale of risk of prostate
cancer and clears the path for the oncologist to determine whether their
patients need a biopsy. This system is fast as it requires minimum calculation
and hence comparatively less time which reduces mortality and morbidity and
is more reliable than other economic systems and can be frequently used by
doctors.
1. 1
Curriculum Vitae
Weiliang Qiu
Channing Division of Network Medicine
Brigham and Women’s Hospital/Harvard Medical School
Tel: 1-617-999-8101
Email: stwxq@channing.harvard.edu
Website: https://sites.google.com/a/channing.harvard.edu/weiliang-qiu/
Professional Summary
Highly experienced in experimental design in clinical trials, sample size/power calculations,
and statistical consultation; Strong research ability; Proven management and teamwork ability.
Core Qualifications
Ø Statistical Consult for Shenogen Pharma Group Ltd.
Ø Have 12+ years of data analysis experience (clinical trial data and observational data)
Ø Published over 70 peer-reviewed papers and 1 book chapter
Ø Editorial Board Member for Insights in Genetics and Genomics
Ø Statistical Editor for British Journal of Nutrition
Ø Reviewed manuscripts for more than 20 academic journals (e.g. JASA, Bioinformatics,
PLoS One, AJE) (Many of the manuscripts I reviewed are results of clinical trials)
Ø Published 10 R packages published in CRAN or Bioconductor (Ace, correctedAUC,
clusterGeneration, clues, GeneSelectMMD, iCheck, powerMediation, powerSurvEpi,
powerEQTL, sizepower). PowerMediation and sizepower contain functions for power
calculations in clinical trials.
Ø Experienced in SAS macro programming
Education
Ø Ph.D. in Statistics (2004), University of British Columbia, Vancouver, Canada
Ø M.Sc. in Statistics (1999), Beijing Polytechnic University, Beijing, China
Ø B.Sc. in Applied Mathematics and Software (1996), Beijing Polytechnic University,
Beijing, China
Work Experience
Ø 05/2013 – present, Associate Biostatistician/Assistant Professor, Brigham and Women's
Hospital/Harvard Medical School
o Data analysis for the clinical trial: The Vitamin D Antenatal Asthma Reduction
Trial (VDAART)
o Investigate the best treatments and evaluate risk factors for older patients with
cardiovascular diseases
o Develop novel equal-variance tests in the application of DNA methylation data
o Compare performance of equal-variance tests in the application of DNA
methylation data
o Develop novel mixture of Bayesian hierarchical models to do gene clustering for
gene expression data generated from paired/matched design
o Evaluate effects of microRNAs and DNA methylation on the response to asthma
drug treatment
2. 2
o Detect differential connectivity of gene regulatory networks that distinguishes
corticosteroid response in asthma
o Investigate epigenetic mechanism of chronic obstructive pulmonary disease and
idiopathic pulmonary fibrosis
o Develop novel statistical method to correct area under ROC for measurement
error
o Apply empirical Bayes’ methods to predict the rate of decline in
electroretinography at the individual level among patients with retinitis pigmentosa
o Investigate effect of longitudinally measured modifiable risk factors on prostate
cancer mortality
o Provide statistical support (experimental design, sample size calculation, data
preprocessing, data analysis, grant/manuscript review) to colleagues and post-doc
research fellows
o Supervise graduate students, college students, and high school interns
Ø 11/2016 – Editorial Board Member for Insights in Genetics and Genomics
o Review manuscripts
Ø 02/2016 – Statistical Editor for British Journal of Nutrition
o Review manuscripts
Ø 2015-2016 – Statistical Consult for Shenogen Pharma Group Ltd.
o Provide statistical supports
Ø 11/2015 – present, Webmaster, Lifetime Data Analysis (LIDA) Interest Group, American
Statistical Association
o Maintain LIDA Interest group microsite
Ø 01/2016 – 05/2016, Part-time Lecturer, Bouvé College of Health Sciences in the Health
Sciences, Northeastern University
o Teach PHTH 6210 Applied Regression Analysis to graduate students for the
Spring 2016 semester
Ø 07/2006 – 04/2013, Associate Biostatistician/Instructor, Brigham and Women's
Hospital/Harvard Medical School
o Data analysis for the clinical trial: The Vitamin D Antenatal Asthma Reduction
Trial (VDAART)
o Investigate the best treatments and evaluate risk factors for older patients with
cardiovascular diseases
o Integrate gene differential analysis and expression quantitative trait loci analysis to
identify novel loci for pharmacogenomics
o Investigate epigenetic mechanism of chronic obstructive pulmonary disease
o Investigate genetic effects on gene expression in chronic obstructive pulmonary
disease
o Develop extended Mann-Whitney U test to assess discrimination of risk prediction
rules in a clustered data setting
o Develop novel method to correct for measurement error when using cumulative
average model in the survival analysis of nutritional data
o Develop novel method to incorporate censoring and informative dropouts in HIV
viral dynamic models
o Develop novel mixture of marginal distributions to do gene clustering for gene
expression data
o Contribute to GeneticsDesign R package to calculate power for testing if disease
is associated with marker in a case-control study
3. 3
o Provide statistical support (experimental design, sample size calculation, data
preprocessing, data analysis, grant/manuscript review) to colleagues and post-doc
research fellows
Ø 10/2004 – 06/2006, Research Fellow, Brigham and Women's Hospital/Harvard Medical
School
o Develop a web-based calculator for sample size and power calculation in
microarray studies (http://sph.umd.edu/department/epib/sample-size-and-power-
calculations-microarray-studies)
o Identify antibodies to help diagnose prostate cancer based on data collected from
“reverse capture” autoantibody microarrays
o Compare generalized rank tests with multivariate rank tests for microarray data
Ø 2001 – 2003, Webmaster, Department of Statistics, University of British Columbia,
Canada
o Maintain website for Department of Statistics, UBC
Ø 09/1998 – 05/1999, Research Associate, Department of Management Sciences, City
University of Hong Kong, China
o Investigate product reliability
Ø 09/1995 – 05/1996, Intern, Institute of Software, Chinese Academy of Sciences, Beijing,
China
o Develop graph automatic vectorization software using C++
Publication
1. Qiu W, Joe H. Separation Index and Partial Membership for Clustering. Comput
Stat Data An. 2006;50(3):585-03.
2. Qiu W, Joe H. Generation of random clusters with specified degree of
separation. J Classif. 2006;23:315-34.
3. Qiu W, Lee ML. SPCalc: A Web-based calculator for sample size and power
calculations in micro-array studies. Bioinformation. 2006;1(7):251-2. PMID:
17597901; PMCID: PMC1891702.
4. Qin S, Qiu W, Ehrlich JR, Ferdinand AS, Richie JP, O'leary MP, Lee ML, Liu BC.
Development of a “reverse capture” autoantibody microarray for studies of
antigen-autoantibody profiling. Proteomics. 2006;6(10):3199-209. PMID:
16596707.
5. Ehrlich JR, Caiazzo RJ Jr, Qiu W, Tassinari OW, O’Leary MP, Richie JP, Liu BC.
A native antigen “reverse capture” microarray platform for autoantibody profiling of
prostate cancer sera. Proteomics Clin Appl. 2007;1(5):476-85. PMID: 21136699.
6. Wang S, Qiu W, Zamar RH. CLUES: A non-parametric clustering method based
on local shrinking. Comput Stat Data An. 2007;52(1):286-98.
7. Lazarus R, Taylor J, Qiu W, Nekrutenko A. Toward the commoditization of
translational genomic research: Design and implementation features of the Galaxy
genomic workbench. Summit on Translational Bioinformatics. 2008;56-60. PMID:
21347127; PMCID: PMC3041519.
8. Qiu W, He W, Wang X, Lazarus R. A Marginal mixture model for selecting
differentially expressed genes across two types of tissue samples. Int J
Biostat. 2008;4(1):Article20. PMID:20231912; PMCID: PMC2835454.
9. Ma J, Li H, Giovannucci E, Mucci L, Qiu W, Nguyen PL, Gaziano JM, Pollak M,
Stampfer MJ. Prediagnostic body-mass index, plasma C-peptide concentration,
4. 4
and prostate cancer-specific mortality in men with prostate cancer: a long-term
survival analysis. Lancet Oncol. 2008;9(11):1039-47. PMID:18835745; PMCID:
PMC2651222.
10.Shappley WV 3rd, Kenfield SA, Kasperzyk JL, Qiu W, Stampfer MJ, Sanda MG,
Chan JM. Prospective study of determinants and outcomes of deferred treatment
or watchful waiting among men with prostate cancer in a nationwide cohort. J Clin
Oncol. 2009;27(30):4980-5. PMID: 19720918; PMCID: PMC2799054.
11.Estes SJ, Ye B, Qiu W, Cramer D, Hornstein MD, Missmer SA. A proteomic
analysis of IVF follicular fluid in women <or=32 years old. Fertil Steril.
2009;92(5):1569-78. PMID: 18980758; PMCID: PMC2915999.
12.Qiu W, Rosner B. Measurement error correction for the cumulative average model
in the survival analysis of nutritional data: application to Nurses' Health
Study. Lifetime Data Anal. 2010;16(1):136-53. PMID: 19757039; PMCID:
PMC2809827.
13.Qiu W, Wu L. HIV Viral Dynamic Models with Censoring and Informative
Dropouts. Statistics in Biopharmaceutical Research. 2010;2(2):220-8.
14.Chang F, Qiu W, Zamar RH, Lazarus R, Wang X. CLUES: An R package for
nonparametric clustering based on local shrinking. J Stat Softw. 2010;33(4):1-16.
15.Li H, Stampfer MJ, Mucci L, Rifai N, Qiu W, Kurth T, Ma J. A 25-year prospective
study of plasma adiponectin and leptin concentrations and prostate cancer risk
and survival. Clin Chem. 2010;56(1):34-43. PMID: 19910504; PMCID:
PMC2858593.
16.Zhang L, Wang D, Jiang W, Edwards D, Qiu W, Barroilhet LM, Rho JH, Jin L,
Seethappan V, Vitonis A, Wang J, Mok SC, Crum C, Cramer DW, Ye B. Activated
networking of platelet activating factor receptor and FAK/STAT1 induces malignant
potential in BRCA1-mutant at-risk ovarian epithelium. Reprod Biol Endocrinol.
2010;8:74. PMID: 20576130; PMCID: PMC2903602.
17.Nguyen PL, Ma J, Chavarro JE, Freedman ML, Lis R, Fedele G, Fiore C, Qiu W,
Fiorentino M, Finn S, Penney KL, Eisenstein A, Schumacher FR, Mucci LA,
Stampfer MJ, Giovannucci E, Loda M. Fatty acid synthase polymorphisms, tumor
expression, body mass index, prostate cancer risk, and survival. J Clin Oncol.
2010;28(25):3958-64. PMID: 20679621; PMCID: PMC2940394.
18.Kunadian V, Zaman A, Spyridopoulos I, Qiu W. Sodium bicarbonate for the
prevention of contrast induced nephropathy: a meta-analysis of published clinical
trials. Eur J Radiol. 2011;79(1):48-55. PMID: 20074886.
19.Kunadian V, Zaman A, Qiu W. Revascularization among patients with severe left
ventricular dysfunction: a meta-analysis of observational studies. Eur J Heart Fail.
2011;13(7):773-84. PMID: 21478241; PMCID: PMC3125123.
20.Jiang W, Qiu W, Wang Y, Cong Q, Edwards D, Ye B, Xu C. Ginkgo may prevent
genetic-associated ovarian cancer risk: multiple biomarkers and anticancer
pathways induced by ginkgolide B in BRCA1-mutant ovarian epithelial cells. Eur J
Cancer Prev. 2011;20(6):508-17. PMID: 21857521.
21.Wang S, Zhao X, Khimji I, Akbas R, Qiu W, Edwards D, Cramer DW, Ye B,
Demirci U. Integration of cell phone imaging with microchip ELISA to detect
ovarian cancer HE4 biomarker in urine at the point-of-care. Lab Chip.
2011;11(20):3411-8. PMID: 21881677.
22.Qiu W, Cho MH, Riley JH, Anderson WH, Singh D, Bakke P, Gulsvik A, Litonjua
AA, Lomas DA, Crapo JD, Beaty TH, Celli BR, Rennard S, Tal-Singer R, Fox SM,
5. 5
Silverman EK, Hersh CP; ECLIPSE Investigators. Genetics of sputum gene
expression in chronic obstructive pulmonary disease. PLoS One.
2011;6(9):e24395. PMID: 21949713. PMCID: PMC3174957.
23.Tantisira KG, Lasky-Su J, Harada M, Murphy A, Litonjua AA, Himes BE, Lange C,
Lazarus R, Sylvia J, Klanderman B, Duan QL, Qiu W, Hirota T, Martinez FD,
Mauger D, Sorkness C, Szefler S, Lazarus SC, Lemanske RF Jr, Peters SP, Lima
JJ, Nakamura Y, Tamari M, Weiss ST. Genomewide association between GLCCI1
and response to glucocorticoid therapy in asthma. N Engl J Med.
2011;365(13):1173-83. PMID: 21991891 [PubMed - indexed for MEDLINE]
PMCID: PMC3667396.
24.Cao Y, Kenfield S, Song Y, Rosner B, Qiu W, Sesso HD, Gaziano JM, Ma J.
Cigarette smoking cessation and total and cause-specific mortality: a 22-year
follow-up study among US male physicians. Arch Intern Med. 2011;171(21):1956-
9. PMID: 22123811; PMCID: PMC3229033.
25.Aschard H, Qiu W, Pasaniuc B, Zaitlen N, Cho MH, Carey V. Combining effects
from rare and common genetic variants in an exome-wide association study of
sequence data. BMC Proc. 2011;5 Suppl 9:S44. PMID: 22373328; PMCID:
PMC3287881.
26.Qiu W, Baccarelli A, Carey VJ, Boutaoui N, Bacherman H, Klanderman B,
Rennard S, Agusti A, Anderson W, Lomas DA, Demeo DL. Variable DNA
methylation is associated with chronic obstructive pulmonary disease and lung
function. Am J Respir Crit Care Med. 2012;185(4):373-81. PMID: 22161163;
PMCID: PMC3297093.
27.Wan ES, Qiu W, Baccarelli A, Carey VJ, Bacherman H, Rennard SI, Agusti A,
Anderson W, Lomas DA, DeMeo DL. Cigarette smoking behaviors and time since
quitting are associated with differential DNA methylation across the human
genome. Human Molecular Genetics. 2012;21(13):3073-82. PMID: 22492999;
PMCID: PMC3373248.
28.Carpe N, Mandeville I, Kho AT, Qiu W, Martin JG, Tantisira KG, Raby BA, Weiss
ST, Kaplan F. Maternal allergen exposure reprograms the developmental lung
transcriptome in atopic and normo-responsive rat pups. American Journal of
Physiology - Lung Cellular and Molecular Physiology. 2012;303(10):L899-911
PMID: 22983352; PMCID: PMC3517678
29.Kunadian V, Pugh A, Zaman AG, Qiu WL. Percutaneous Coronary Intervention in
Patients with Severe Left Ventricular Dysfunction: A Meta-analysis of 19
Studies. Coronary Artery Disease. 2012 Nov;23(7):469-79. PMID: 22960383
30.Wan E.S., Qiu W., Baccarelli A, Carey V.J., Bacherman H., Rennard S.I., Agusti
A., Anderson W., Lomas D.A., DeMeo D.L., Systemic Steroid Exposure is
Associated with Differential Methylation in Chronic Obstructive Pulmonary
Disease. American Journal of Respiratory and Critical Care Medicine. 2012;
186(12):1248-55. PMID: 23065012 PMCID: PMC3622442
31.Kim DK, Cho MH, Hersh CP, Lomas DA, Miller BE, Kong X, Bakke P, Gulsvik A,
Agustí A, Wouters E, Celli B, Coxson H, Vestbo J, Macnee W, Yates JC, Rennard
S, Litonjua A, Qiu W, Beaty TH, Crapo JD, Riley JH, Tal-Singer R, Silverman EK;
on behalf of the ECLIPSE, ICGN, and COPDGene Investigators. Genome-Wide
Association Analysis of Blood Biomarkers in Chronic Obstructive Pulmonary
Disease. American Journal of Respiratory and Critical Care Medicine.
2012;186(12):1238-1247. PMID: 23144326
6. 6
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