Selected Publications

Single-cell genomics
  • Gisch DL, Brennan M, Lake BB, Basta J, Keller M, Ferreira RM, Akilesh S, Ghag R, Lu C, Cheng YH, Collins KS, Parikh SV, Rovin BH, Robbins L, Conklin KY, Diep D, Zhang B, Knoten A, Barwinska D, Asghari M, Sabo AR, Ferkowicz MJ, Sutton TA, Kelly KJ, Boer IH, Rosas SE, Kiryluk K, Hodgin JB, Alakwaa F, Jefferson N, Gaut JP, Gehlenborg N, Phillips CL, El-Achkar TM, Dagher PC, Hato T, Zhang K, Himmelfarb J, Kretzler M, Mollah S; Kidney Precision Medicine Project (KPMP); Jain S, Rauchman M, Eadon MT. The chromatin landscape of healthy and injured cell types in the human kidney. Nature Communications 15, 433 (2024). https://doi.org/10.1038/s41467-023-44467-6
  • Xiang, J., Lu, M., Shi, M., Cheng, X., Kwakwa, K. A., Davis, J. L., Su, X., Bakewell, S. J., Zhang, Y., Fontana, F., Xu, Y., Veis, D. J., DiPersio, J. F., Ratner, L., Sanderson, R. D., Noseda, A., Mollah, S., Li, J., & Weilbaecher, K. N. (2022). Heparanase Blockade as a Novel Dual-Targeting Therapy for COVID-19. Journal of Virology, 96(7), e0005722. (doi.org/10.1128/jvi.00057-22)
Chromatin remodeling in cancer
  • Du J, Sudlow LC, Shahverdi K, Zhou H, Michie M, Schindler TH, Mitchell JD, Mollah S, Berezin MY. Oxaliplatin-induced cardiotoxicity in mice is connected to the changes in energy metabolism in the heart tissue. bioRxiv [Preprint]. 2023 May 25:2023.05.24.542198. doi: 10.1101/2023.05.24.542198. PMID: 37292714; PMCID: PMC10245950.
  • Maya Natesan, Mitchelle Kong, Min Shi, Reetika Ghaag, Shamim Mollah. “CREWdb: Optimizing Chromatin Readers, Erasers, and Writers Database using Machine Learning-Based Approach”. 2022. (doi.org/10.1101/2022.06.02.494594)
  • Charles Lu, Rintsen Sherpa, Liubou Klindziuk, Stefanie Kriel, Shamim Mollah. “HOCMO: A Tensor-based Higher-Order Correlation Model to Deconvolute Epigenetic Microenvironment in Breast Cancer”. 2020. (doi.org/10.1101/2020.12.01.406249)
  • S. A. Mollah and S. Subramaniam, “Histone Signatures Predict Therapeutic Efficacy in Breast Cancer”. 2020, IEEE Open Journal of Engineering in Medicine and Biology, 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)
Innate and adaptive immunity
  • Nirschl C, Liu Y, Mollah S, Feder R, Wu P, Sage P, Sharpe A, Anandasabapathy N; “Migratory DC temper subcutaneous immunity through key tolerance pathways”, 2015, Journal of Investigative Dermatology, 135.
  • 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)
Predictive and diagnostic modeling
  • Shi M., Mollah, S.“NeTOIF: A Network-based Approach for Time-Series Omics Data Imputation and Forecasting”, 2022. IEEE Access. (doi.org/10.1101/2021.06.05.447209v1)
  • 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) 
  • 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)
  • Mollah, S, PB James, Grabell J, Barbour EM, Coller B, “Diagnostic Prediction of Von Willebrand Disease using multiple bleeding phenomics Datasets”, Join Summit on Translational Bioinformatics and Clinical Research Informatics conference, March 2013. PMID:24303262.
Ontology based instruments and federated data access
  • Sim, I, Carini, S, Mollah SA et al. “Ontology-based federated data access to human studies information”. American Medical Informatics Association proceeding, November 2012. Distinguished Paper Award. 2012:856-65. PMID:23304360.
  • Sim, I, Carini, S, Mollah SA et al. “The human studies database project: Federating human studies design data using the ontology of clinical research”, AMIA Clinical Research Informatics summit 2010. Distinguished Paper Award, 2010: 51–55. PMID:21347149.
  • Carini, S, Pollock, B H, Mollah, SA et a. “Development and evaluation of a study design typology for human subjects Research”. 55th American Medical Informatics Association proceeding. Distinguished Paper Award. 2009: 81–85. PMID:20351827.
  • Mauer, A C, Barbour, E, Mollah, SA et al. “Creating an ontology-based human phenotyping system: The Rockefeller University bleeding history experience”, Journal of The Society for Clinical and Translational Science, Vol2, Issue 5 2009,382:85. (doi.org/10.1111/j.1752-8062.2009.00147.x)
  • Mauer, A C, Barbour, E, Mollah, SA et al. “Initial deployment of a comprehensive, Ootology-backed, web-based bleeding history phenotyping instrument in normal individuals”. Journal of Thrombosis and Haemostasis, 2009,7:14. (insights.ovid.com/jthrh/200907002/00149457-200907002-00033)
Natural language processing (NLP) and knowledge representation

  • 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).
  • Mollah, SA, Cimino, C, “Indexing key concepts from biomedical text descriptions for image retrieval in medical education”, 55th American Medical Informatics Association proceeding, San Francisco, November 2009.
  • Mollah, SA, Cimino, C, “Semi-automatic indexing of postscript files using medical curriculum text indexer in medical education”, 53rd American Medical Informatics Association proceeding, Chicago, November 2007. PMID:18694151.
  • Mollah, SA, Johnson, SB. “Automatic learning of the morphology of medical language using information compression”. 49th American Medical Informatics Association proceeding. 2003, PMID:14728443.