Akram Mohammed

Bioinformatics Manager-Biorepository and Integrative Genomics (BIG)

Bio

I am interested in researching genomics, machine learning, and precision medicine. My current work at the University of Tennessee Health Science Center involves managing and analyzing whole-exome sequence and genotyping data and maintaining the Lab Information Management Systems (LIMS) for the BIG initiative. I develop and run bioinformatics pipelines for variant annotation, GWAS, PheWAS, candidate gene/SNP analyses, machine learning, clinical genomics, polygenic risk score, ancestry estimation, and multi-omics data analysis to study complex diseases and host-pathogen interactions.

I obtained my master's in Computer Science with a specialization in Bioinformatics from the State University of New York at Albany in 2009. I completed my Ph.D. in Bioinformatics from the University of Nebraska Medical Center's Genetics, Cell Biology, and Anatomy Department in August 2014. During my PhD, I developed a hierarchical machine learning method to predict the enzyme classes/subclasses from over 200,000 protein sequence data and applied it to study the host-pathogen interactions. 

From September 2014 to February 2018, I worked as a postdoc in the computational biology lab at the University of Nebraska-Lincoln on developing a logic-based human immune system model to study the dynamic responses to infections. From March 2018 to July 2019, I worked as a fellow at the Center for Biomedical Informatics at the UTHSC, designing and developing machine learning algorithms to predict the onset of abnormal conditions using high-frequency physiological data streams from ICU sensor data. Since then, I have also worked as a high-performance computing liaison, LIMS developer, and genomics data manager, providing bioinformatics solutions, analyzing next-generation sequencing data that include whole exome, whole genome, transcriptome, epigenome, and metagenome data and consequent functional characterization and association studies.

Publications

Voorhies K, Mohammed A, Chinthala L, Kong SW, Lee I-H, Kho AT, McGeachie M, Mandl KD, Raby B, Hayes M, et al. (2024) GSDMB/ORMDL3 Rare/Common Variants Are Associated with Inhaled Corticosteroid Response among Children with Asthma. Genes. 2024; 15(4):420. https://doi.org/10.3390/genes15040420

Zahr RS, Mohammed A, Naik S, Faradji D, Ataga KI, Lebensburger J, Davis RL. (2024) Machine Learning Predicts Acute Kidney Injury in Hospitalized Patients with Sickle Cell Disease. American Journal of Nephrology. 2024;55(1):18-24. https://doi.org/10.1159/000534864.

Naik S, Mohammed A. (2023) Consensus Gene Network Analysis Identifies the Key Similarities and Differences in Endothelial and Epithelial Cell Dynamics after Candida albicans Infection. International Journal of Molecular Sciences. https://doi.org/10.3390/ijms241411748

Ackerman K*, Mohammed A*, Chinthala L, Kamaleswaran R, Davis RL, et al. (2022) Features derived from blood pressure and intracranial pressure predict elevated intracranial pressure events in critically ill children. Scientific Reports https://doi.org/10.1038/s41598-022-25169-3 

Naik S and Mohammed A. (2022) Coexpression network analysis of human candida infection reveals key modules and hub genes responsible for host-pathogen interactions. Frontiers in Genetics https://doi.org/10.3389/fgene.2022.917636

Tarimo CS, Bhuyan SS, Zhao Y, Ren W, Mohammed A, Li Q, Gardner M, et al. (2022) Prediction of low Apgar score at five minutes following labor induction intervention in vaginal deliveries: machine learning approach for imbalanced data at a tertiary hospital in North Tanzania. BMC Pregnancy and Childbirth https://doi.org/10.1186/s12884-022-04534-0

Liu Z, Khojandi A, Li X, Mohammed A, Davis RL, Kamaleswaran R. (2022) A Machine Learning-Enabled Partially Observable Markov Decision Process Framework for Early Sepsis Prediction. INFORMS Journal on Computing https://doi.org/10.1287/ijoc.2022.1176

Singhal L, Garg Y, Yang P, Tabaie A, Wong AI, Mohammed A, et al. (2021) eARDS: A multi-center validation of an interpretable machine-learning algorithm of early onset Acute Respiratory Distress Syndrome (ARDS) among critically ill adults with COVID-19. PloS One  https://doi.org/10.1371/journal.pone.0257056

Kuscu C, Kiran M, Mohammed A, Kuscu C, Satpathy S, Wolen A, Bardhi E, et al. (2021) Integrative analyses of circulating small RNAs and kidney graft transcriptome in transplant glomerulopathy. International Journal of Molecular Sciences https://doi.org/10.3390/ijms22126218

Banerjee S, Mohammed A, Wang H, Palaniyar N, Kamaleswaran R. (2021) Machine Learning Identifies Complicated Sepsis Course and Subsequent Mortality Based on 20 Genes in Peripheral Blood Immune Cells at 24 H Post-ICU Admission. Frontiers in Immunology https://doi.org/10.3389/fimmu.2021.592303

Liu Z, Khojandi A, Mohammed A, Li X, Chinthala LK, Davis RL, Kamaleswaran R. (2021) HeMA: A Hierarchically Enriched Machine Learning Approach for Managing False Alarms in Real Time: A Sepsis Prediction Case Study. Computers in Biology and Medicine https://doi.org/10.1016/j.compbiomed.2021.104255

Mohammed A, Van Wyk F, Chinthala LK, Khojandi A, Davis RL, Coopersmith CM, Kamaleswaran R. (2020) Temporal Differential Expression of Physiomarkers Predicts Sepsis in Critically Ill Adults. SHOCK®: Injury, Inflammation, and Sepsis: Laboratory and Clinical Approaches https://doi.org/10.1097/shk.0000000000001670 (Editor's choice)

Akbilgic O*, Kamaleswaran R*, Mohammed A*, Ross W, Masaki K, Petrovitch H, Tanner CM, et al. (2020) Electrocardiographic Changes Predate Parkinson's Disease Onset. Scientific Reports https://doi.org/10.1038/s41598-020-68241-6

Mohammed A, Podila P, Davis R, Ataga K, Hankins J, Kamaleswaran R. (2020) Using Machine Learning to Predict Early Onset Acute Organ Failure in Critically Ill Intensive Care Unit Patients with Sickle Cell Disease: Retrospective Study. Journal of Medical Internet Research https://doi.org/10.2196/14693

Mohammed A, Cui Y, Mas V, Kamaleswaran R. (2019) Differential gene expression analysis reveals novel genes and pathways in pediatric septic shock patients. Scientific Reports https://doi.org/10.1038/s41598-019-47703-6

Wyko FV, Khojandi A, Mohammed A, Begoli E, Davis RL, Kamaleswaran R. (2019) A minimal set of physiomarkers in continuous high-frequency data streams predict adult sepsis onset earlier. International Journal of Medical Informatics https://doi.org/10.1016/j.ijmedinf.2018.12.002

Mohammed A*, Biegert G*, Adamec J, Helikar T. (2018) CancerDiscover: An integrated pipeline for cancer biomarker and cancer class prediction from high-throughput sequencing data. Oncotarget https://doi.org/10.18632/oncotarget.23511

Puniya, BL, Todd RG, Mohammed A, Brown DM, Barberis, M and Helikar T. (2018) A mechanistic computational model reveals that plasticity of CD4+ T cell differentiation is a function of cytokine composition and dosage. Frontiers in physiology https://doi.org/10.3389/fphys.2018.00878

Mohammed A, Biegert G, Adamec J, Helikar T. (2017) Identification of potential tissue-specific cancer biomarkers and development of cancer versus normal genomic classifiers. Oncotarget https://doi.org/10.18632/oncotarget.21127

Mohammed A*,  Massilamany C*,  Loy JD, Purvis T, Krishnan B, Basavalingappa RH, Kelly CM, et al. (2016) Genome sequence analysis and characterization of Bacillus infantis NRRL B-14911 that has a potential to induce myocarditis in A/J mice. BMC Genomics https://doi.org/10.1186/s12864-016-2900-2

Negi S, Pandey S, Srinivasan S, Mohammed A, and Guda C. (2015) LocSigDB: a database of experimental and predicted protein localization signals. Database https://doi.org/10.1093/database/bav003

Mohammed A, Guda C. (2015) Application of Hierarchical enzyme classification method reveals the role gut microbiome in human metabolism. BMC Genomics https://doi.org/10.1186/1471-2164-16-s7-s16

Mohammed A, Guda C. (2011) Computational Approaches for Automated Classification of Enzyme Sequences. Journal of Proteomics and Bioinformatics https://doi.org/10.4172/jpb.1000183

ABSTRACTS & PREPRINTS

Gunturkun G, Khojandi K, Ayvat P, Davis RL, Baucum M, Chinthala L, Mohammed A, Shafi N (2023) Characterization and prediction of norepinephrine response in critically ill adults. Critical Care Medicine https://doi.org/10.1097/01.ccm.0000910772.34232.c6

Zahr R*, Mohammed A*, Naik S, Faradji D, Lebensburger JD, Ataga KI, Davis RL. (2022) Machine Learning Predicts Acute Kidney Injury in Hospitalized Patients with Sickle Cell Disease. Blood https://doi.org/10.1182/blood-2022-165572

Kuscu C, Kuscu C, Mohammed A, Shetty A, Maluf DG, Eason J, & Mas V. (2021). Identification of Molecular Markers for Liver Cirrhosis by Single-Nucleus RNA Sequencing. American Journal of Transplantation https://atcmeetingabstracts.com/abstract/identification-of-molecular-markers-for-liver-cirrhosis-by-single-nucleus-rna-sequencing/

Puniya BL, Moore R, Mohammed A, Amin R, La Fleur A, Helikar T. (2020) A comprehensive logic-based model of the human immune system to study the dynamics responses to mono- and coinfections. bioRxiv https://doi.org/10.1101/2020.03.11.988238

Singh A, Mohammed A, Chinthala L, Kamaleswaran R. (2020) Machine learning predicts early onset of fever from continuous physiological data of critically ill patients. arXiv https://doi.org/10.48550/arXiv.2009.07103

*co-first author