Swati Biswas

Professor and Associate Head

Department of Mathematical Sciences

University of Texas at Dallas

Contact Information

 

The University of Texas at Dallas

Department of Mathematics, FO 35

800 West Campbell Road

Richardson, TX 75080-3021, USA

 

 

Office: FO 3.704K

Phone: 972-883-6686

Fax: 972-883-6622

Email: swati.biswas AT utdallas DOT edu

Education

B.Sc., Statistics, University of Delhi, India, 1994

M.Sc., Statistics, University of Delhi, India, 1996

Ph.D., Biostatistics, Ohio State University, Columbus, OH, 2003

Post doctoral, MD Anderson Cancer Center, Houston, TX, 2005

Research Interests

  • Biostatistics
  • Statistical Genetics
  • Genetic Epidemiology
  • Cancer Genetics
  • Risk Prediction Models
  • Bayesian Clinical Trials

Software

Peer-Reviewed Publications (Note: The underlined co-authors are my former/current students)

1. Biswas, S., Papachristou, C., Irwin, M. and Lin, S. (2003) “Linkage analysis of the simulated data Evaluations and comparisons of methods”, BMC Genetics, 4:S70.

 

2.      Biswas, S. and Lin, S. (2004) “Evaluations of maximization procedures for estimating linkage parameters under heterogeneity”, Genetic Epidemiology, 26:206-217.

 

3.      Lin, S. and Biswas, S. (2004) “On modeling locus heterogeneity using mixture distributions”, BMC Genetics, 5:29.

 

4.      Biswas, S. and Berry, D.A. (2005) “Determining joint carrier probabilities of cancer causing genes using Markov chain Monte Carlo methods”, Genetic Epidemiology, 29:141-154.

 

5.      Biswas, S., Lin, S., and Berry, D.A. (2005) “A new Bayesian approach incorporating covariate information for heterogeneity and its comparison with HLOD”, BMC Genetics, 6:S138.

 

6.      Biswas, S. and Lin, S. (2006) “A Bayesian approach for incorporating variable rates of heterogeneity in linkage analysis”, Journal of the American Statistical Association, 101:1341-1351.

 

7.      Huang, X., Biswas, S., Estey, E. H., and Berry, D.A. (2006) “Building and validating a prognostic index for biomarker studies”, Cancer Biomarkers, 2:97-101.

 

8.      Huang, X., Biswas, S., Oki, Y., Issa, J-P., and Berry, D.A. (2007) “A parallel phase I/II clinical trial design for combination therapies”, Biometrics 63:429-436.

 

9.      Biswas, S. and Lin, S. (2007) “Incorporating covariates in mapping heterogeneous traits A hierarchical model using empirical Bayes estimation”, Genetic Epidemiology, 31:684-696.

 

10.  Biswas, S., Liu, D.D., Lee, J.J and Berry, D.A. (2009) “Bayesian Clinical Trials at the University of Texas MD Anderson Cancer Center”, Clinical Trials, 6:205-216.

 

11.  Ahmad, N., Biswas, S., Bae, S., Meador, K., Huang, R., and Singh, K. (2009) “Association between obesity and asthma in US children and adolescents”, Journal of Asthma, 46:642-646.

 

12.  Biswas, S. and Papachristou, C. (2010) “Accounting for Disease Model Uncertainty in Mapping Heterogeneous Traits - A Bayesian Model Averaging Approach”, Human Heredity, 69:242-253.

 

13.  Biswas, S. and Lin, S. (2012) “Logistic Bayesian LASSO for Identifying Association with Rare Haplotypes and Application to Age-Related Macular Degeneration”, Biometrics, 68:587-597.

 

14.  Biswas, S., Tankhiwale, N., Blackford, A., Gutierrez Barrera, A.M., Ready, K., Lu, K., Amos, C.I., Parmigiani, G., Arun, B. (2012) “Assessing the Added Value of Breast Tumor Markers in Breast Cancer Genetic Risk Prediction Model BRCAPRO”, Breast Cancer Research and Treatment, 133:347-355.

 

15.  Lingineni, R., Biswas, S., Ahmad, N., Jackson, B., Bae, S., Singh, K.P. (2012) “Risk Factors for Attention Deficit/Hyperactivity Disorder Among US Children: Results from a National Survey”, BMC Pediatrics, 12:50.

 

16.  Shetty P.K., Thamake S.I., Biswas, S., Johansson S.L. and Vishwanatha J.K. (2012) “Reciprocal Regulation of Annexin A2 and EGFR with Her-2 in Her-2 Negative and Herceptin-Resistant Breast Cancer”, PLOS One, 7(9):e44299.

 

17.  Biswas, S., Atienza, P., Chipman, J., Hughes, K., Gutierrez Barrera, A.M., Amos, C.I., Arun, B., Parmigiani, G. (2013) “Simplifying Clinical Use of the Genetic Risk Prediction Model BRCAPRO”, Breast Cancer Research and Treatment, 139: 571-579.

 

18.  Biswas, S. and Papachristou, C. (2014) “Evaluation of Logistic Bayesian LASSO for Identifying Association with Rare Haplotypes”, BMC Proceedings, 8(Suppl 1): S54.

 

19.  Satten, G.A., Biswas, S., Papachristou, C., Turkmen, A., and König, I.R. (2014) “Population-based association and gene by environment interactions in the Genetic Analysis Workshop 18”, Genetic Epidemiology, 38: S49-S56.

 

20.  Biswas, S.*, Xia, S., and Lin, S.* (2014) “Detecting Rare Haplotype-Environment Interaction with Logistic Bayesian LASSO”, Genetic Epidemiology, 38: 31-41.

 

21.  Biswas, S., Arun, B., and Parmigiani, G. (2014) “Reclassification of Predictions for Uncovering Sub-Group Specific Improvement”, Statistics in Medicine, 33: 1914 - 1927.

 

22.  Lykens, K., Moayad, N., Biswas, S., Reyes-Ortiz, C., Singh, K.P. (2014) “Impact of a community based implementation of REACH II Program for caregivers of Alzheimer’s patients”, PLOS One , 9(2): e89290.

 

23.  Zhang, Y. and Biswas, S. (2015) “An Improved Version of Logistic Bayesian LASSO for Detecting Rare Haplotype-Environment Interactions With Application to Lung Cancer”, Cancer Informatics, 14(S2): 11-16.

 

25.  Mazzola, E., Blackford, A., Parmigiani, G., and Biswas, S. (2015) “Recent Enhancements to the genetic risk prediction model BRCAPRO”, Cancer Informatics, 14(S2): 147-157.

 

24.  Datta, A.S., Zhang, Y., Zhang, L., and Biswas, S. (2016) “Association of Rare Haplotypes on ULK4 and MAP4 Genes with Hypertension”, BMC Proceedings, 10(Suppl 7):44.

 

26.  Datta, A.S. and Biswas, S. (2016) “Comparison of Haplotype-based Statistical Tests for Disease Association with Rare and Common Variants”, Briefings in Bioinformatics, 17:657-671.

 

27.  Biswas, S., Atienza, P., Chipman, J., Blackford, A.L., Arun, B., Hughes, K., Parmigiani, G. (2016) “A Two-Stage Approach to Genetic Risk Assessment in Primary Care”, Breast Cancer Research and Treatment, 155: 375-383.

 

28.  Franklin, G., Harari, H., Ahsan, S., Bello, D., Sterling, D.A., Nedrelow, J., Raynaud, S., Biswas, S., Liu, Y. (2016) “Residual isocyanates in medical devices and products: A qualitative and quantitative assessment”, Environmental Health Insights, 10: 1-16.

 

29.  Zhang, Y., Lin, S., and Biswas, S. (2017) “Detecting Rare and Common Haplotype-Environment Interaction under Uncertainty of Gene-Environment Independence Assumption”, Biometrics, 73:344-355.

 

30.  Chowdhury, M., Euhus, D., Onega, T., Biswas, S.*, Choudhary, P.K.* (2017) “A Model for Individualized Risk Prediction of Contralateral Breast Cancer”, Breast Cancer Research and Treatment, 161:153-160.

 

31.  Zhang, Y., Hofmann, J., Purdue, M., Lin, S.*, and Biswas, S.* (2017) "Logistic Bayesian LASSO for Genetic Association Analysis of Data from Complex Sampling Designs", Journal of Human Genetics, 62:819-829.

 

32.  Chowdhury, M., Euhus, D., O'Donnell, M., Onega, T., Choudhary, P.K.*, Biswas, S.* (2018) “Dose-Dependent Effect of Mammographic Breast Density on the Risk of Contralateral Breast Cancer”, Breast Cancer Research and Treatment, 170:143-148.

 

33.  Chowdhury, M., Euhus, D., Banu, A., Umbricht, C., Biswas, S.*, Choudhary, P.K.* (2018) "Validation of a Personalized Risk Prediction Model for Contralateral Breast Cancer", Breast Cancer Research and Treatment, 170:415-423.

 

34.  Li, X., Choudhary, P., Biswas, S., Wang, S. (2018) "A Bayesian Latent Variable Approach to Aggregation of Partial and Top Ranked Lists in Genomic Studies", Statistics in Medicine, 37:4266-4278.

 

35.  Datta, A.S., Lin, S., Biswas, S. (2018) "A Family-Based Rare Haplotype Association Method for Quantitative Traits", Human Heredity, 83:175-195.

 

36.  Yuan, X. and Biswas, S. (2019) "Bivariate Logistic Bayesian LASSO for Detecting Rare Haplotype Association with Two Correlated Phenotypes", Genetic Epidemiology, 43:996-1017 https://doi.org/10.1002/gepi.22258.

 

37.  Papachristou, C. and Biswas, S. (2020) "Comparison of Haplotype-based Statistical Tests for Detecting Rare Haplotype-Environment Interactions", Briefings in Bioinformatics, 21:851-862 https://doi.org/10.1093/bib/bbz031.

 

38.  Zhang, L., Papachristou, C., Choudhary, P.K., and Biswas, S. (2020) "A Bayesian Hierarchical Framework for Pathway Analysis in Genome-Wide Association Studies", Human Heredity, 84:240-255 https://doi.org/10.1159/000508664.

 

39.  Rajapaksha, R.M.D.S., Hammonds, R., Filbey, F., Choudhary, P.K.*, and Biswas, S.* (2020) "A Preliminary Risk Prediction Model for Cannabis Use Disorder", Preventive Medicine Reports,20:101228. https://doi.org/10.1016/j.pmedr.2020.101228.

 

40.  Yuan, X. and Biswas, S. (2021) "Detecting Rare Haplotype Association with Two Correlated Phenotypes of Binary and Continuous types", Statistics in Medicine,40:1877-1900. https://onlinelibrary.wiley.com/doi/abs/10.1002/sim.8877.

 

41.  Ruberu, T.L.M., Kenyon, E.A., Hudson, K.A., Filbey, F., Ewing, S.W.F., Biswas, S.*, Choudhary, P.K.* (2022) "Joint Risk Prediction for Hazardous Use of Alcohol, Cannabis, and Tobacco among Adolescents: A Preliminary Study Using Statistical and Machine Learning", Preventive Medicine Reports, 25:101674. https://www.sciencedirect.com/science/article/pii/S221133552100365X.

 

42.  Sajal, I.H., Chowdhury, M., Wang, T., Euhus, D., Choudhary, P.K.*, Biswas, S.* (2022) "CBCRisk-Black: A Personalized Contralateral Breast Cancer Risk Prediction Model for Black Women", Breast Cancer Research and Treatment, 194:179-186. https://pubmed.ncbi.nlm.nih.gov/35562619/.

 

43.  Rajapaksha, R.M.D.S., Filbey, F., Biswas, S.*, and Choudhary, P.K.* (2022) "A Bayesian Learning Model to Predict the Risk for Cannabis Use Disorder", Drug and Alcohol Dependence, 236:109476. https://www.sciencedirect.com/science/article/abs/pii/S0376871622002137.

 

44.  Sajal, I.H. and Biswas, S. (2023) "Bivariate Quantitative Bayesian LASSO for Detecting Association of Rare Haplotypes with Two Correlated Continuous Phenotypes", Frontiers in Genetics: Statistical Genetics and Methodology, 14:1104727. https://www.frontiersin.org/articles/10.3389/fgene.2023.1104727/full.

 

45.  Ruberu, T.L.M., Rajapaksha, R.M.D.S., Heitzeg, M.M., Klaus, R., Boden, J.M., Biswas, S.*, and Choudhary, P.K.* (2023) "Validation of a Bayesian Learning Model to Predict the Risk for Cannabis Use Disorder", Addictive Behaviors, 146:107799. https://www.sciencedirect.com/science/article/pii/S0306460323001946?via%3Dihub.

 

46.  Ruberu, T.L.M., Braun, D., Parmigiani, G., Biswas, S. (2023) "Bayesian Meta-Analysis of Penetrance for Cancer Risk", Biometrics, Accepted; Discussions invited; Rejoinder accepted. https://arxiv.org/abs/2304.01912

 

47.  Ruberu, T.L.M., Braun, D., Parmigiani, G., Biswas, S. (2024) "Meta-Analysis of Breast Cancer Risk for Individuals with PALB2 Pathogenic Variants", Genetic Epidemiology, To appear. https://www.medrxiv.org/content/10.1101/2023.05.31.23290791v1

 

48.  Ruberu, T.L.M., Braun, D., Parmigiani, G., Biswas, S. (2023) Rejoinder to "Bayesian Meta-Analysis of Penetrance for Cancer Risk", Biometrics, To appear. https://arxiv.org/abs/2304.01912

 

*Co-Corresponding authors

 

Other Publications

 

1.  Archer, K.J., Dobbin, K, Biswas, S., Day, R.S., Wheeler, D.C. and Wu, H. (2015) “Introductory Editorial: Computer Simulation, Bioinformatics, and Statistical Analysis of Cancer Data and Processes”, Cancer Informatics, Suppl. 2:247-251.

 

Grants and Contracts

1.      Title: Multifrequency ultrasound imaging for monitoring breast cancer response to neoadjuvant chemotherapy, Funding Agency: NIH, R01CA279313, Role: Co-I, 06/01/2023 - 05/31/2028 (PI: K Hoyt)

 

2.      Title: Advances in Statistical and Computational Methods for Analysis of Biomedical, Genetic, and Omics Data, Funding Agency: NSF, DMS-2232547, Role: PI, 1/1/2023 - 12/31/2023

 

3.      Title: Recruiting for a diversified Mathematical Sciences faculty at UT Dallas, Funding Agency: UT Dallas ASPIRE² STRIDE Faculty Recruitment Grant Program, Role: Co-PI, 11/7/2022 - 11/6/2023 (PI: J Zweck)

 

4.      Title: High content analgesic screening from human "nociceptors”, Funding Agency: NIH, R61AT011938-01A1, Role: Co-I, 11/1/2022 - 10/31/2025 (PI: B Kolber)

 

5.      Title: SEED: Workshop: Advances in Statistical and Computational Methods for Analysis of Biomedical, Genetic, and Omics Data, Funding Agency: UT Dallas, Role: PI, 6/1/2022 - 7/31/2023

 

6.      Title: The Equitable Student Pathways Project, Funding Agency: The University of Texas System’s Lumina Grant, Role: UTD PAIR Cohort member, 1/24/2022 - 1/23/2024

 

7.      Title: Accelerating Credentials of Purpose and Value Grant Program: Creating Undergraduate Certificate in Data Science, Funding Agency: Texas Higher Education Coordinating Board, Role: Co-PI, 1/28/2022 - 9/30/2022 (PI: V Dragovic)

 

8.      Title: A Bayesian Meta-Analysis Approach for Estimation of Penetrance and its Application to PALB2 gene for Breast Cancer Risk, Funding Agency: NCI/NIH, R03CA242562-01A1, Role: PI, 3/6/2020 - 2/28/2024

 

9.      Title: Risk Prediction for Substance Use Disorders, Funding Agency: UT Dallas, SPIRe seed grant, Role: PI, 07/01/2019 - 12/31/2021

 

10.      Title: Collaborative Research: A Texan Partnership towards a Diverse PhD Pool in Mathematics, Funding Agency: NSF, DMS-1820765, Role: Co-PI, 09/01/2018 - 08/31/2022 (PI: V Dragovic)

 

11.      Title: A Model for Individualized Risk Prediction of Contralateral Breast Cancer, Funding Agency: NCI/NIH, R21 CA186086-01, Role: PI, 09/01/2014 - 08/31/2018

 

12.      Title: Efficacy Study of A Nicotine Barrier Cream, Funding Agency: CDC/UNT Health Science Center, R03-OH009815-01A1, Role: Co-I, 09/01/2014 - 08/31/2015 (PI: Y Liu)

 

13.      *Title: Genetic Risk Prediction in Primary Care, Funding Agency: NCI/NIH, R03 CA173834-01, Role: PI, 01/01/2013 - 12/31/2016.

*This project was also selected for funding by the Cancer Prevention Research Institute of Texas (CPRIT).

 

14.      Title: Modeling Reclassification of Predictions, Funding Agency: Dana Farber Cancer Institute, Role: PI, 01/01/2014 - 06/30/2014

 

15.      Title: Identifying rare haplotype-environment interactions using Logistic Bayesian Lasso, Funding Agency: NCI/NIH, R03 CA171011-01, Role: PI, 07/10/2012 - 06/30/2015

 

16.      Title: Preliminary Evidence for a Two-Stage Approach for Adapting BRCAPRO to Primary Clinical Settings, Funding Agency: Dana Farber Cancer Institute, Role: PI, 10/01/2011 - 09/30/2012

 

17.      Title: Evaluation of Tarrant County United Way Healthy Aging and Independent Living Initiative, Funding Agency: United Way of Tarrant County, Role: Co-I, Duration: 07/01/2010 - 06/30/2012 (PI: K Lykens).

 

18.      Title: Improvement and Validation of BRCAPRO, Funding Agency: Susan G Komen for Cure, KG081303, Role: Co-I, Duration: 09/01/2009 - 07/31/2011 (PI: G Parmigiani).

 

19.      Title: Validation of a Statistical Model for Prediction of Carrying Breast/Ovarian Cancer Susceptibility Genes BRCA1 and BRCA2, Funding agency: UNTHSC SPH Intramural Seed Grant, Role: PI, Duration: 10/06/2008 - 10/06/2009.

 

20.      Title: Post-Baccalaureate Research Education Program and Retention Enhancement (PREPARE), Funding Agency: National Institute of General Medical Sciences, NIH, R25 GM078398-01, Role: Evaluation Coordinator, Duration: 09/30/2006 - 08/31/2010 (PI: JK Vishwanatha).

 

21.      Title: Statistical methods for gene mapping - a new paradigm, Funding Agency: NHGRI/NIH, R01 5HG002657-03, Role: Consultant, Duration: 8/20/2005 - 12/31/2006 (PI: S. Lin)

Doctoral Students

1.  Yuan Zhang (2016) (Winner of the Best Dissertation Award of the School) “Detecting Rare Haplotype-Environment Interaction Under Uncertainty of Gene-Environment Independence Assumption with An Extension to Complex Sampling Data"

 

2.  Ananda Datta (2016) “Detecting Rare Haplotype Disease Association: Comparison of Existing Population-Based Methods and a New Family-Based Quantitative Bayesian LASSO Method"

 

3.  Philamer Atienza (2017) “Adaptation of the Genetic Risk Prediction Model BRCAPRO For Primary Care Settings"

 

4.  Marzana Chowdhury (2018; Co-advisor: Dr. Pankaj Choudhary) (Winner of the Best Dissertation Award of the School) “Prediction of Individualized Risk of Contralateral Breast Cancer"

 

5.  Lei Zhang (2018) “A Bayesian Hierarchical Model for Pathway Analysis with Simultaneous Inference on Pathway-Gene-SNP Structure"

 

6.  Xiaochen Yuan (2020) “Bivariate Logistic Bayesian LASSO for Detecting Rare Haplotype Association with Two Correlated Phenotypes"

 

7.  Dhanushka Mudalige Rajapaksha (2022; Co-advisor: Dr. Pankaj Choudhary) “Risk Prediction Models for Substance Use Disorders"

 

8.  Thanthirige Lakshika Ruberu (2023) (Winner of the Best Dissertation Award of the School) “Some Contributions to the Risk Prediction of Hereditary Breast Cancer and Substance Use Disorders"

 

9.  Ibrahim Sajal (current)

 

10.  Tingfang Wang (current; Co-advisor: Dr. Pankaj Choudhary)

 

11.  Sharaf Anjum (current; Co-advisor: Dr. Pankaj Choudhary)

 

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3/27/2024