Our research goal is to interpret and distill the complexity of cancer and other rare diseases through integration of large scale multi-omics data using dynamic modeling, graph theory, and machine learning methods. We aim to apply these methods to study challenging cancer biology problems, particularly how chromatin alterations influence cellular phenotypes in response to genetics, environments, and pharmacological perturbations. By integrating large datasets, we hope to extract relevant information necessary to make precise biological and clinical predictions and computationally direct experiments. The primary focus of our lab is to produce high-resolution computational models to study the effects of genetic and epigenetic perturbations on chromatin alterations that affect cellular states, elucidating the molecular mechanisms of cancer and other diseases.
Research Projects:
Cancer systems biology
-----Chromatin remodeling in cancer
-----Tumor microenvironment
-----Cancer subtyping
-----Targeting cancer stemness pathway in breast cancer
-----Single-cell approaches to address tumor heterogeneity
-----Pharmacodynamics & pharmacokinetics of anti-cancer drugs
-----Biomarker discovery in cancer
-----Cancer immunotherapy
Modeling gene regulatory networks
Genotype-phenotype correlation
Natural language processing
Mentorship and Commitment to Diversity Statement:
Xiang J, Lu M, Shi M, Cheng X, Kwakwa K, Davis J, Su X, Bakewell S, Zhang Y, Fontana F, Xu Y, Veis D, DiPersio J, Ratner L, Sanderson R, Noseda A, Mollah S, Li J, Weilbaecher, K. “Heparanase Blockade as a Novel Dual-Targeting Therapy for COVID-19”, 2022 Journal of Virology (in press)
Xiang J, Shi M, Fiala MA, Gao F, Rettig MP, Uy GL, Schroeder MA, Weilbaecher KN, Stockerl-Goldstein K, Mollah S, DiPersio JF. “Machine Learning-Based Scoring Models to Predict Peripheral Blood Hematopoietic Stem Cell Mobilization in Allogeneic Donors”, 2021 Blood Advances. 2021005149. (doi:/10.1182/bloodadvances. 2021005149)
Shi M., Mollah, S. “NeTOIF: A Network-based Approach for Time-Series Omics Data Imputation and Forecasting”, 2021. (doi.org/10.1101/2021.06.05.447209v1)
Klie A, Tsui B, Mollah S, Skola D, Dow M, Hsu C, Carter H. “Increasing metadata coverage of SRA BioSample entries using deep learning-based Named Entity Recognition”. Database, Volume 2021, 2021, baab021, (doi.org/10.1093/database/baab021)
Min Shi, Rintsen Sherpa, Liubou Klindziuk, Stefanie Kriel, Shamim Mollah. “A Non-Negative Tensor Factorization Approach to Deconvolute Epigenetic Microenvironment in Breast Cancer”. 2020. PLOS Computational Biology(in review) (doi.org/10.1101/2020.12.01.406249).
S. A. Mollah and S. Subramaniam, “Histone Signatures Predict Therapeutic Efficacy in Breast Cancer”. IEEE Open Journal of Engineering in Medicine and Biology, 2020, vol. 1, pp. 74 -82. (doi.org/10.1109/OJEMB.2020.2967105)
Mollah SA, Subramaniam S. Global chromatin profiling fingerprints reveal therapeutic efficacy in breast cancer. 2019, CELL-REPORTS. (doi.org/10.2139/ssrn.3413902)
Mollah S, Dobrin J, Feder R, Tse S, Matos I, Cheong C, Steinman R, Anandasabapathy N; Flt3L dependence helps define an uncharacterized subset of murine cutaneous dendritic cells, 2014, Journal of Investigative Dermatology, 134(5):1265-75. (doi.org/10.1038/jid.2013.515)
Anandasabapathy N, Feder R, Mollah S, Tse S, Longhi M, Mehandru S, Matos I, Cheong C, Ruane D, Brane L, Teixeira A, Dobrin J, Mizenina O, Park C, Meredith M, Clausen B, Nussenzweig M, Steinman R. Classical Flt3L-dependent dendritic cells control immunity to protein vaccine. 2014 Journal of Experimental Medicine, 25,211(9):1875-91. (doi.org/1084/jem.20131397)
Elbatarny M, Mollah S, Grabell J, Bae S, Deforest M, Tuttle A, Hopman W, Clark DS, Mauer AC, Bowman M, Riddel J, Christopherson PA, Montgomery RR, Zimmerman Program Investigators, Rand ML, Coller B, James PD, Normal Range of Bleeding Scores for the ISTH-BAT: Adult and Pediatric Data from The Merging Project, 2014, Haemophilia. 20 (6), 831-835. (doi.org/10.1111/hae.12503)