Selected Publications

  • Shi M, Klindziuk L, Mollah S. “A Non-Negative Tensor Factorization Approach to Deconvolute Microenvironment in Breast Cancer”. 2020. (https://doi.org/10.1101/2020.12.01.406249). 
  • Klie ATsui B, Mollah SSkola DDow MHsu CCarter H. “Increasing metadata coverage of SRA BioSample entries using deep learning based Named Entity Recognition”. 2020. (https://doi.org/10.1101/414136). 
  • 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 (preprint). (doi.org/10.2139/ssrn.3413902)
  • Tsui B, Mollah S, Skola D, Dow M, Hsu C, Carter H. “Creating a scalable deep learning-based Named Entity Recognition model for biomedical textual data by repurposing biosample specimen free-text annotation”. 2018. (doi.org/10.1101/414136)
  • 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)
  • 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.
  • 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, 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. “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)
  • 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)
  • Mollah, SA, Johnson, SB. “Automatic learning of the morphology of medical language using information compression”. 49th American Medical Informatics Association proceeding. 2003, PMID:14728443.