CURRICULUM VITAE

Mir S. Siadaty

Office:        Room 3242, Hospital West Complex,
Department of Public Health Sciences
University of Virginia
Charlottesville, VA 22908
Phone: (434) 390 3081
Fax: (413) 647-2985
Email: mirsiadaty@virginia.edu

EDUCATION

2002    Master of Science (in Biostatistics), University of Minnesota, Minneapolis, Minnesota
1996    Doctor of Medicine, Tehran University of Medical Sciences, School of Medicine, Tehran, Iran
1993    Research Methodology Diploma, World Health Organization- Eastern Mediterranean Office, Tehran


POSITIONS

2004-    Assistant Professor, Division of Clinical Informatics, Department of Public Health Sciences, University of Virginia
2002-2004    Senior Bioinformatician, Division of Biostatistics and Epidemiology, University of Virginia
2000-2002    Research Associate, Division of Health Services Research and Policy, School of Public Health, University of Minnesota
1999-2000    Biostatistics Research Fellow, Division of Cardiology, Department of Internal Medicine, University of Texas- Houston
1996-1998    Instructor, Tehran Institute of Higher Education in Statistics and Informatics, Tehran
1991-1995    Instructor, Center for Statistics and Informatics, University of Tehran


PUBLICATIONS
Informatics
1.    Siadaty MS, Shu J, Knaus WA. Relemed: Sentence-level search engine with relevance score for the MEDLINE database of biomedical articles. BMC Med Inform Decis Mak. 2007 Jan 10;7(1):1. PMID: 17214888.
2.    Siadaty MS, Knaus WA. Locating previously unknown patterns in data-mining results: a dual data- and knowledge-mining method. BMC Med Inform Decis Mak. 2006 Mar 7;6(1):13
3.    Mullins IM, Siadaty MS, Lyman J, Scully K, Garrett CT, Greg Miller W, Muller R, Robson B, Apte C, Weiss S, Rigoutsos I, Platt D, Cohen S, Knaus WA. Data mining and clinical data repositories: Insights from a 667,000 patient data set. Comput Biol Med. 2005 Dec 20;
4.    Lee JK, Laudeman T, Kanter J, James T, Siadaty MS, Knaus WA, Prorok A, Bao Y, Freeman B, Puiu D, Wen LM, Buck GA, Schlauch K, Weller J, Fox JW. GeneX Va: VBC open source microarray database and analysis software. Biotechniques. 2004 Apr;36(4):634-8, 640, 642. PMID: 15088382.

Biostatistics
5.    Siadaty MS, Shu J. Proportional odds ratio model for comparison of diagnostic tests in meta-analysis. BMC Med Res Methodol. 2004 Dec 10;4(1):27. PMID: 15588327
6.    Siadaty MS, Philbrick JT, Heim SW, Schectman JM. Repeated-measures modeling improved comparison of diagnostic tests in meta-analysis of dependent studies. Journal of Clinical Epidemiology 2004 July; 57(7):698-710.

Collaborative and clinical
7.    Ropka ME, Wenzel J, Phillips EK, Siadaty M, Philbrick JT. Uptake rates for breast cancer genetic testing: a systematic review. Cancer Epidemiol Biomarkers Prev. 2006 May;15(5):840-55. PMID: 16702359
8.    Wu Z, Siadaty MS, Riddick G, Frierson HF Jr, Lee JK, Golden W, Knuutila S, Hampton GM, El-Rifai W, Theodorescu D. A novel method for gene expression mapping of metastatic competence in human bladder cancer. Neoplasia. 2006 Mar;8(3):181-9. PMID: 16611411
9.    Oliver MN, Smith E, Siadaty M, Hauck FR, Pickle LW. Spatial analysis of prostate cancer incidence and race in Virginia, 1990-1999. Am J Prev Med. 2006 Feb;30(2 Suppl):S67-76.
10.    Oliver MN, Matthews KA, Siadaty M, Hauck FR, Pickle LW. Geographic bias related to geocoding in epidemiologic studies. Int J Health Geogr. 2005 Nov 10;4:29.
11.    Hauck FR, Omojokun OO, Siadaty MS. Do pacifiers reduce the risk of sudden infant death syndrome? A meta-analysis. Pediatrics. 2005 Nov;116(5):e716-23. Epub 2005 Oct 10.
12.    Casscells W, Vasseghi MF, Siadaty MS, Madjid M, Siddiqui H, Lal B, Payvar S. Hypothermia is a bedside predictor of imminent death in patients with congestive heart failure. Am Heart J. 2005 May;149(5):927-33. PMID: 15894979
13.    Griffin MP, Siadaty MS. Papaverine prolongs patency of peripheral arterial catheters in neonates. J Pediatr. 2005 Jan;146(1):62-5. PMID: 15644824
14.    Schectman JM, Schorling JB, Nadkarni MM, Lyman JA, Siadaty MS, Voss JD. The effect of physician feedback and an action checklist on diabetes care measures. Am J Med Qual. 2004 Sep-Oct;19(5):207-13. PMID: 15532913.
15.    Heim SW, Schectman JM, Siadaty MS, Philbrick JT. D-dimer testing for deep venous thrombosis: a metaanalysis. Clin Chem. 2004 Jul;50(7):1136-47. Epub 2004 May 13. PMID: 15142977.
16.    McClish JC, Ragosta M, Powers ER, Barringhaus KG, Gimple LW, Fischer J, Garnett J, Siadaty M, Sarembock IJ, Samady H. Effect of acute myocardial infarction on the utility of fractional flow reserve for the physiologic assessment of the severity of coronary artery narrowing. Am J Cardiol. 2004 May 1;93(9):1102-6. PMID: 15110200.
17.    Kane RL, Keckhafer G, Flood S, Bershadsky B, Siadaty MS. The effect of Evercare on hospital use. J Am Geriatr Soc. 2003 Oct;51(10):1427-34. PMID: 14511163.
18.    Lima RS, Watson DD, Goode AR, Siadaty MS, Ragosta M, Beller GA, Samady H. Incremental value of combined perfusion and function over perfusion alone by gated SPECT myocardial perfusion imaging for detection of severe three-vessel coronary artery disease. J Am Coll Cardiol. 2003 Jul 2;42(1):64-70. PMID: 12849661
19.    Casscells W, Hassan K, Vaseghi MF, Siadaty MS, Naghavi M, Kirkeeide RL, Hassan MR, Madjid M. Plaque blush, branch location, and calcification are angiographic predictors of progression of mild to moderate coronary stenoses. Am Heart J. 2003 May;145(5):813-20. PMID: 12766737
20.    Naghavi M, Madjid M, Gul K, Siadaty MS, Litovsky S, Willerson JT, Casscells SW. Thermography basket catheter: In vivo measurement of the temperature of atherosclerotic plaques for detection of vulnerable plaques. Catheter Cardiovasc Interv. 2003 May;59(1):52-9. PMID: 12720241
21.    Kane RL, Homyak P, Bershadsky B, Lum YS, Siadaty MS. Outcomes of managed care of dually eligible older persons. Gerontologist. 2003 Apr;43(2):165-74. PMID: 12677074
22.    Naghavi M, Wyde P, Litovsky S, Madjid M, Akhtar A, Naguib S, Siadaty MS, Sanati S, Casscells W.  Influenza infection exerts prominent inflammatory and thrombotic effects on the atherosclerotic plaques of apolipoprotein E-deficient mice. Circulation. 2003 Feb 11;107(5):762-8. PMID: 12578882
23.    Naghavi M, John R, Naguib S, Siadaty MS, Grasu R, Kurian KC, van Winkle WB, Soller B, Litovsky S, Madjid M, Willerson JT, Casscells W. pH Heterogeneity of human and rabbit atherosclerotic plaques; a new insight into detection of vulnerable plaque. Atherosclerosis. 2002 Sep;164(1):27-35. PMID: 12119190
24.    Merati K, said Siadaty M, Andea A, Sarkar F, Ben-Josef E, Mohammad R, Philip P, Shields AF, Vaitkevicius V, Grignon DJ, Adsay NV. Expression of inflammatory modulator COX-2 in pancreatic ductal adenocarcinoma and its relationship to pathologic and clinical parameters. Am J Clin Oncol. 2001 Oct;24(5):447-52. PMID: 11586094
25.    Naghavi M, Barlas Z, Siadaty S, Naguib S, Madjid M, Casscells W. Association of influenza vaccination and reduced risk of recurrent myocardial infarction. Circulation. 2000 Dec 19;102(25):3039-45. PMID: 11120692

EXPERIENCE & SKILLS

1.    Extensive collaboration and consultation experience with basic and clinical medical scientists, resulting in numerous grants, publications, and presentations.
2.    Software development and computer programming, both prototyping and machine-level optimization. Familiar with Perl, C, and Fortran.
3.    Mentoring graduate and professional students, teaching, and workshop facilitating.
4.    Data analysis, utilizing a variety of statistical software; Experienced in R (and S-Plus), SAS, Stata, and SPSS. Design and analysis of gene-array (and Genechip microarray) high throughput gene expression data. Experienced in survival analysis (including multiple events, competing risks, frailty, state-space and Markov chain), and repeated measures (random-effects models and marginal models).


Selected COLLABORATIVE PROJECTS

1.    Project title: A Systems Engineering Focus on Medical Informatics; Granting organization: National Library of Medicine T-15 Training Grant in Medical Informatics; Major goal: The UVa medical informatics training program is designed as a collaboration between the School of Engineering, Dept. of Systems and Information Engineering, and the School of Medicine, the Clinical Informatics Division; My role: co-mentor the PhD students and the post-doctoral fellows.
2.    Project title: Improving control with activity and nutrition; Granting organization: NIH/NIDDK; Major goal: To translate into practice recent findings regarding lifestyle modification in treatment and prevention of type 2 diabetes; My role: Collaborating with the insurance company (Southern Health) to develop SQL procedures for extracting medical and pharmacy claims data from their administrative data repositories, and then designing data refinement plans to validate and cleanse the claims data.
3.    Project title: Clinical decision-making using a data-driven display; Granting organization: National Library of Medicine; Major goal: Develop and test an adaptive user interface that presents patient data to clinicians in a graphical display and organizes the display based on patterns of change contained within the data; My role: consultation on advanced methods for adding inference to the findings.
4.    Project title: Signaling and Progression in Prostate Cancer; Granting organization: NIH; Major goal: Multidisciplinary program project to elucidate the signal transduction mechanisms that underlie the stepwise events association with progression of CAP from a localized and androgen sensitive tumor to a disseminated and androgen independent one; My role: Design and analysis of micro-array gene expression and proteomics experiments.
5.    Project title: Gene Chip/Microarray Bioinformatics Core; Granting organization: UVa Endowment; Major goal: Design and implementation of GeneX. GeneX Va is an Open Source database and Bioinformatics analysis system for archiving and analyzing Affymetrix GeneChip® data. Supported by the Virginia Bioinformatics Consortium (VBC), GeneX Va provides a set of sample management, sample documentation, and analysis tools designed to support a range of users; My role: Design, optimization, and implementation of analysis modules, using R and Perl.
6.    Project title: Academic Administrative Units in Primary Care-Family Medicine; Granting organization: Health Resources and Services Administration (HRSA); Major goal: To expand practice based research infrastructure at the University of Virginia, in community based and primary care settings; My role: Collaboration with the UVa Family Medicine faculty for study design and data analysis of their research projects.


Selected RESEARCH PROJECTS

1. Subject: information retrieval systems, search engines, relevance metrics, natural language processing:
Encountering extraneous articles in response to a query submitted to MEDLINE/PubMed is not uncommon. However, every one of the articles retrieved contains all of the query words. This led us to the conclusion that the presence of query words in an article is not a sufficient condition for the article to be relevant to user's query, although it is a necessary. About 83% of queries sent to PubMed, NLM's search engine for MEDLINE, are multi-word queries. When submitting a query with multiple words, the user is usually interested in some type of relationship between the words, such that the "presence of relationship" between the query words in the article also becomes a necessary condition for relevance. We proposed that if two words occur within an article, the probability that a relation between them is explained is clearly higher when the words occur within the same sentence (or adjacent sentences) versus remote sentences.
We have developed "Relemed", a search engine for MEDLINE. Relemed increases specificity and precision of retrieval by searching for query words within sentences rather than the whole article. It uses sentence-level concurrence as a statistical surrogate for the existence of relationship between the words. It also estimates a relevance score and sorts the results on this basis, thus shifting irrelevant articles lower down the list. We used distributed parallel search architecture, to keep the response time short despite the heavy natural language processing required.

2. Subject: information retrieval systems, multi-repository data mining, interestingness measures:
Data mining can be utilized to automate analysis of substantial amounts of data produced in many organizations. However, data mining produces large numbers of rules and patterns, many of which are not useful. Existing methods for pruning uninteresting patterns have only begun to automate the knowledge acquisition step (which is required for subjective measures of interestingness), hence leaving a serious bottleneck. In this project we proposed a method, an automatic acquisition of knowledge, to shorten the pattern list by locating the novel and interesting ones.
The dual-mining method is based on automatically comparing the strength of patterns mined from a database with the strength of equivalent patterns mined from a relevant knowledgebase. When these two estimates of pattern strength do not match, a high "Surprise score" is assigned to the pattern, identifying the pattern as potentially interesting. The surprise score captures magnitude of novelty or interestingness of the mined pattern. In addition, we show how to compute p values for each surprise score, thus filtering out noise and attaching statistical significance.
We have implemented the dual-mining method using scripts written in Perl and R. We applied the method to a large patient database (University of Virginia's Clinical Data Repository) and a biomedical literature citation knowledgebase (MEDLINE).

3. Subject: gene micro array analysis, aggregation of gene probes on chromosomes:
Detecting clusters of differentially expressed genes on chromosomes: a counting process approach. In gene micro array experiments, expression levels of a large group of genes are measured simultaneously. This possibility has advanced the biomedical research field enormously. There are different methods to identify genes that have significantly changed their expression level between a control and an experimental condition. When differentially expressed genes are discovered, in some cases, studying the proximities of the significantly differentially expressed genes on chromosomes may give additional insights. We need a methodology to evaluate whether such gene expression changes occur randomly throughout the chromosome or in distinct geographical "hot spots". When not associated with corresponding structural changes in the chromosome, the presence of such "hot spots" may be indicative hitherto unappreciated biological processes regulating regional gene expression. For instance, in cancer cell lines this may point to some pathological processes at the chromosome level. Hence a question can be formulated: Which areas of chromosomes are most commonly involved in gene expression changes? In other words, one wants to see whether there are places on any chromosome where significant numbers of differentially expressed genes are clustered. In this project we developed a bioinformatics approach to the discovery and mapping of such gene expression "hot spots".
A gene is an ordered sequence of nucleotides with a start and an end location on a specific chromosome. We assign each gene to a location on the chromosome midway between its start and end points. Moving along the nucleotides in a chromosome (from tip of the short arm p, to the end of long arm q), we encounter genes. Viewing each gene as an event, this resembles a Poisson process. The idea of detecting a hot spot (consisting of the significant genes) is testing whether the rate of the Poisson process (the lambda, l) changes (increases) dramatically in a certain locality, hence a non-stationary Poisson process (therefore the rate being a function of distance, l(d)).
To demonstrate this methodology, we used gene expression profiling data obtained from a model of human bladder cancer metastasis.

4. Subject: meta-analysis, outcome heterogeneity:
Proportional Odds Ratio model, for meta-analysis of diagnostic tests. Consider a meta-analysis where a 'head-to-head' comparison of diagnostic tests for a disease of interest is intended. Assume there are two or more tests available for the disease, where each test has been studied in one or more papers. Some of the papers may have studied more than one test, hence the results are not independent. Also the collection of tests studied may change from one paper to the other, hence incomplete matched groups. We proposed a model, the proportional odds ratio (POR) model, which makes no assumptions about the shape of OR0, a baseline function capturing the way OR changes across papers. The POR model does not assume homogeneity of ORs, but merely specifies a relationship between the ORs of the two tests. One may expand the domain of the POR model to cover dependent studies, multiple outcomes, multiple thresholds, multi-category or continuous tests, and individual-level data. The flexibility of POR model and its generalized applicability, coupled with ease with which it can be estimated in familiar software, suits the daily practice of meta-analysis and improves clinical decision-making.




Last updated January 2007