Shamim A Mollah, PhD
Assistant Professor of Genetics
School of Medicine
Laboratory Website
Shamim A Mollah, PhD, is focused on applying network-based models on multi-omics data using machine-learning techniques to understand complex diseases at systems level.
Background
Shamim A Mollah, PhD, is an Assistant Professor of Genetics at Washington University School of Medicine in St. Louis. Dr. Mollah specializes in developing integrative network-based models using multi-omics data to study cellular processes. Her research goal is to interpret and distill the complexity of cancer and other rare diseases through genetics and epigenetic approaches using dynamic modeling, graph theory, and machine learning methods. She aims 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, she hopes to extract relevant information necessary to make precise biological and clinical predictions and computationally direct experiments. The primary focus of her 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. Previously, Dr. Mollah served as the bioinformatics scientist at the Rockefeller University, where she managed bioinformatics data analysis core for the Center of Clinical and Translational Science (CCTS). During her tenure at the Rockefeller University, her proposed bioinformatics research ideas led to a 2008 Obama challenge grant award and its renewal in 2011.
Dr. Mollah received her PhD in Bioinformatics and Systems Biology from UCSD. Her research was focused on applying network analysis-based models on multi-omics data using dynamic modeling, graph-theory, and machine-learning techniques to characterize drug responses in cancer cells. She studied the responses of drug individual/combinations on tumor cells and their effects on key proteins involved in cell signaling pathways. Dr. Mollah received her Master’s degree in Biomedical Informatics from Columbia University, where her research was focused on developing AI-based medical language parser using Natural Language Processing. She received her undergraduate degrees in Computer Science (B.S.) and Mathematics (B.A.) from Indiana University.
Hiring Postdoctoral Research Associate
Research Interests
- Cancer systems biology
- — Chromatin remo-
deling in cancer
(histone modifi-
cations) - — Cancer subtyping
- — Tumor microenvi-
ronment - — 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
Lab Members
- Charles Lu, BS, Bioinformatics Research Assistant, Washington University School of Medicine in St. Louis, Institute for Informatics
- Reetika Ghag, BS, Master's Student in Biomedical Informatics, Washington University School of Medicine in St. Louis, Institute for Informatics
- Stefanie Kriel, Undergraduate Student in Neuroscience with a Minor in Computer Science, Washington University in St. Louis
- Maya Natesan, Undergraduate Student in Computer Science with a Minor in Bioinformatics, Washington University in St. Louis
Selected Publications
- Histone Signatures Predict Therapeutic Efficacy in Breast Cancer. IEEE Open Journal of Engineering in Medicine and Biology
- Global Chromatin Profiling Fingerprints Reveal Therapeutic Efficacy in Breast Cancer. Cell Reports (preprint)
- Creating a Scalable Deep Learning Based Named Entity Recognition Model for Biomedical Textual Data by Repurposing BioSample Free-text Annotations. bioRxiv
- Flt3L Dependence Helps Define an Uncharacterized Subset of Murine Cutaneous Dendritic Cells. Journal of Investigative Dermatology
- Classical Flt3L-Dependent Dendritic Cells Control Immunity to Protein Vaccine. Journal of Experimental Medicine
- Normal Range of Bleeding Scores for the ISTH‐BAT: Adult and Pediatric Data from the Merging Project. Haemophilia
- Diagnostic Prediction of Von Willebrand Disease Using Multiple Bleeding Phenomics Datasets. AMIA 2013 Joint Summits on Translational Science Proceedings
- Ontology-Based Federated Data Access to Human Studies Information. AMIA 2012 Annual Symposium Proceedings (Distinguished Paper Award)
- The Human Studies Database Project: Federating Human Studies Design Data Using the Ontology of Clinical Research. AMIA 2010 Summit on Clinical Research Informatics Proceedings (Distinguished Paper Award)
- Initial Deployment of a Comprehensive, Ontology-Backed, Web-Based Bleeding History Phenotyping Instrument in Normal Individuals. Journal of Thrombosis and Haemostasis
- Development and Evaluation of a Study Design Typology for Human Research. AMIA 2009 Annual Symposium Proceedings (Distinguished Paper Award)
- Creating an Ontology‐Based Human Phenotyping System: The Rockefeller University Bleeding History Experience. Clinical and Translational Science
- Automatic Learning of the Morphology of Medical Language Using Information Compression. AMIA 2003 Annual Symposium Proceedings