Dr. Marmar Moussa

EDUCATION

PhD, Computer Science and Engineering, University of Connecticut
MS, Computer Science and Engineering University of Connecticut
MS, Computer Science and Engineering, Alexandria University
BS, Computer Engineering & Automatic Control, Alexandria University

 
EXPERIENCE

Post-graduate training: Post-Doctoral Fellowship in Cancer Immunology & Computational Genomics, Carole and Ray Neag Cancer Center, University of Connecticut Health Center

RESEARCH INTERESTS

Algorithms, Machine Learning, Data Analytics, Computational Genomics, Bioinformatics and Computational Biology, Single Cell Omics, and Cancer Immunomics.

BIO
Dr. Marmar Moussa joined the University of Oklahoma in July 2023 as assistant professor at the School of Computer Science, Gallogly College of Engineering. Prior to that, Dr. Moussa served as assistant professor of Medicine at the University of Connecticut (2021-2023) where she was a postdoctoral fellow (2019-2021).  She is an experienced computer engineer and basic scientist with research interests in computational biology and cancer immuno-omics. She received her Ph.D. in Computer Science and Engineering 2019 with a focus on Computational Genomics from UConn, where she also received the prestigious CSE Taylor Booth Fellowship. Additionally, Dr. Moussa holds two M.Sc. degrees in CSE focus areas Bioinformatics (2018, University of Connecticut) and Cryptography(2004, Alexandria University, EG).

Dr. Moussa’s research passion includes the design of advanced algorithms and machine learning methods for single cell genomics applied to Cancer Immunology. Her work includes publications in Science Immunology, Nature, Journal of Clinical investigations, Nature Communications, BMC Genomics, Journal of Computational Biology, and others.  Dr. Moussa's recent funding includes the NIH-NCI K25 Career Development Award (K25CA270079 M. Moussa PI, $993,605) and NSF2212512 (M. Moussa PI, $200,000) for projects studying tumor etiology, evolution, and the tumor microenvironment in single cell resolution.  

AWARDS, HONORS AND PROFESSIONAL ACTIVITIES

The Taylor L. Booth Graduate Fellowship - CSE University of Connecticut's highest honor for doctoral students; CSE University of Connecticut

SISG Scholarship, University of Washington - 2019 for studying Statistical Genetics; SISG

CSE UConn Pre-Doctoral Fellowship – 2018 For Outstanding Scholarly Research Accomplishments; CSE UConn

SISG Scholarship, University of Washington – 2018 for studying Statistical Genetics; SISG

CSE UConn Predoctoral Award for Outstanding Scholarly Accomplishments and Promise of Continued Research Achievement; CSE UConn

PUBLICATIONS
See Google Scholar.

11th International Computational Advances in Bio and Medical Sciences (ICCABS 2021)
Bansal, M. S., Măndoiu, I. I., Moussa, M., Patterson, M., Rajasekaran, S., Skums, P., & Zelikovsky, A.
Journal of Computational Biology, 30(4), 363-365. (2023) doi.org/10.1089/cmb.2023.29085.msb

Computational Advances in Bio and Medical Sciences: 11th International Conference, ICCABS 2021, Virtual Event, December 16–18, 2021, Revised Selected Papers
Bansal, M. S., Măndoiu, I., Moussa, M., Patterson, M., Rajasekaran, S., Skums, P., & Zelikovsky, A. (Eds.)
Springer Nature (2022)

Sympathetic nervous system-mediated β-adrenergic signaling maintains the pool of mature natural killer cells
Geyer, R., Moussa, M., Mandoiu, I., Srivastava, P. K., & Nevin, J.
The Journal of Immunology, 208 (1_Supplement), 169-10. (2022) doi.org/10.4049/jimmunol.208.Supp.169.10

β-adrenergic signaling modulates the development and activity of erythroid suppressor cells
Chawla, A. K., Nevin, J., Moussa, M., Geyer, R., Mandoiu, I., & Srivastava, P. K.
The Journal of Immunology 208 (1_Supplement), 119.07-119.07 (2022) doi.org/10.4049/jimmunol.208.Supp.119.07

Reversion analysis reveals the in vivo immunogenicity of a poorly MHC I-binding cancer neoepitope
Ebrahimi-Nik, H., Moussa, M., Englander, R. P., Singhaviranon, S., Michaux, J., Pak, H. et al.
Nature Communications 12.1 (2021): 6423.
doi.org/10.1038/s41467-021-26646-5

Unbiased identification of tumor rejection mediating neoepitopes
Srivastava, P. K., Mandoiu, I. I., Brennick, C. A., George, M. M., & Moussa, M.
U.S. Patent Application No. 17/225,374. (2021)

Comparative cellular analysis of motor cortex in human, marmoset and mouse
Trygve E Bakken, Nikolas L Jorstad, Qiwen Hu, Blue B Lake, ... Marmar Moussa, ... Ed S Lein
Nature 598, no. 7879 (2021): 111-119.
doi.org/10.1038/s41586-021-03465-8

SC1: A Tool for Interactive Web-Based Single-Cell RNA-Seq Data Analysis
Moussa, M., & Măndoiu, I. I.
Journal of Computational Biology 28, no. 8 (2021): 820-841.
doi.org/10.1089/cmb.2021.0051

Tonic sympathetic nervous system signaling regulates the maturation of natural killer cells
Geyer, R., Moussa, M., Mandoiu, I. I., Srivastava, P. K., & Nevin, J. T. 
The Journal of Immunology 206, no. 1_Supplement (2021): 98-34.
doi.org/10.4049/jimmunol.206.Supp.98.34

An unbiased approach to defining bona fide cancer neoepitopes that elicit immune-mediated cancer rejection
Brennick, C. A., George, M. M., Moussa, M. M., ... & Srivastava, P. K.
The Journal of clinical investigation 131, no. 3 (2021).
doi.org/10.1172/JCI142823DS1

Computational cell cycle analysis of single cell RNA-Seq data
Moussa, M., & Măndoiu, I. I. 
Computational Advances in Bio and Medical Sciences: 10th International Conference, ICCABS 2020, Virtual Event, December 10-12, 2020, Revised Selected Papers 10, pp. 71-87. Springer International Publishing, (2021).
doi.org/10.1007/978-3-030-79290-9_7

The variation in gene expression profiles of cells captured in different phases of the cell cycle can interfere with cell type identification and functional analysis of single cell RNA-Seq (scRNA-Seq) data. In this paper, we introduce SC1CC (SC1 Cell Cycle analysis tool), a computational approach for clustering and ordering single cell transcriptional profiles according to their progression along cell cycle phases. We also introduce a new robust metric, Gene Smoothness Score (GSS) for assessing the cell cycle based order of the cells. SC1CC is available as part of the SC1 web-based scRNA-Seq analysis pipeline, publicly accessible at https://sc1.engr.uconn.edu/

CRISPR-guided reversion reveals the immunogenicity of a “non-MHC binding” cancer neoepitope in vivo
Ebrahimi-Nik, H., Moussa, M., Englander, R., Singhaviranon, S., Michaux, J., Pak, H., ... & Srivastava, P. 
(2020).
doi.org/10.21203/rs.3.rs-119173/v1

Sympathetic nervous tone limits the development of myeloid-derived suppressor cells
Nevin, J. T., Moussa, M., Corwin, W. L., Mandoiu, I. I., & Srivastava, P. K.
Science Immunology 5, no. 51 (2020): eaay9368.
doi.org/10.1126/sciimmunol.aay9368

Unbiased analysis of all possible neoepitopes of MC38-FABF tumor reveals a new universe of cancer neoepitopes with unexpected properties
George, M. M., Brennick, C. A., Moussa, M. M., Hagymasi, A. T., Seesi, S. A., Shcheglova, T. V., ... & Srivastava, P. K.
The Journal of Immunology 204, no. 1_Supplement (2020): 239-18.
doi.org/10.4049/jimmunol.204.Supp.239.18

Evolution of cellular diversity in primary motor cortex of human, marmoset monkey, and mouse
Trygve E Bakken, Nikolas L Jorstad, Qiwen Hu, Blue B Lake, ... Marmar Moussa, ... Ed S Lein
BioRxiv (2020): 2020-03.
doi.org/10.1101/2020.03.31.016972

Locality Sensitive Imputation for Single Cell RNA-Seq Data
Moussa, M., & Măndoiu, I. I.
Journal of Computational Biology 26, no. 8 (2019): 822-835.
doi.org/10.1089/cmb.2018.0236

Computational Methods for the Analysis of Single-Cell RNA-Seq Data
Moussa, M.
(2019)
https://opencommons.uconn.edu/dissertations/2135

SC1: A web-based single cell RNA-seq analysis pipeline
Moussa, M., & Măndoiu, I. I. 
2018 IEEE 8th international conference on computational advances in bio and medical sciences (ICCABS), pp. 1-1. IEEE, (2018)
doi.org/10.1109/ICCABS.2018.8542088

Single cell RNA-seq data clustering using TF-IDF based methods
Moussa, M., & Măndoiu, I. I. 
BMC genomics 19 (2018): 31-45.
doi.org/10.1186/s12864-018-4922-4

Differential Privacy Approach for Big Data Privacy in Healthcare
Moussa, M., & Demurjian, S. A.
Privacy and Security Policies in Big Data, pp. 191-213. IGI Global, (2017)
doi.org/10.4018/978-1-5225-2486-1.ch009

iClass: Combining Multiple Multi-label Classification with Expert Knowledge
Moussa, M., & Demurjian, S. A.
Privacy and Security Policies in Big Data, pp. 191-213. IGI Global, (2017)
doi.org/10.1109/ICMLA.2015.179

iClass-Applying Multiple Multi-Class Machine Learning Classifiers Combined With Expert Knowledge to Roper Center Survey Data.
Moussa, M., & Demurjian, S. A.
LWA, pp. 221-229. (2015)
http://ceur-ws.org/Vol-1458/F03_CRC42_Moussa.pdf

Clustering Single Cell RNA-Seq Data using TF-IDF based Methods
Măndoiu, I. I. 
https://dna.engr.uconn.edu/bibtexmngr/upload/Mou17.pdf

Clustering scRNA-Seq Data using TF-IDF
Moussa, M., & Măndoiu, I. I. 
https://dna.engr.uconn.edu/bibtexmngr/upload/MM17.pdf