Akram Mohammed, MS, PhD

APOL1 and chronic kidney disease in pediatrics: a study from the Biorepository and Integrative Genomics Initiative

Zahr RS, Chinthala L, Mohammed A, Kovesdy CP, Davis RL.

Insights from the Biorepository and Integrative Genomics pediatric resource

S Buonaiuto, F Marsico, A Mohammed, et al.

Machine learning models incorporating genotype and ancestry improve severe asthma risk prediction

Tahmin N, Chinthala LK, Marsico FL, Buonaiuto S, Mohammed A, et al.

Machine Learning Approach for Analysis of Metabolomics Predicts Persistent Albuminuria in Patients with Sickle Cell Anemia

A Mohammed, L Elsherif, et al.

GSDMB/ORMDL3 Rare/Common Variants Are Associated with Inhaled Corticosteroid Response among Children with Asthma

Voorhies K, Mohammed A, Chinthala L, et al.

Machine Learning Predicts Acute Kidney Injury in Hospitalized Patients with Sickle Cell Disease

Zahr RS, Mohammed A, Naik S, et al.

Features derived from blood pressure and intracranial pressure predict elevated intracranial pressure events in critically ill children

Ackerman K*, Mohammed A*, Chinthala L, et al. (*co-first author)

A Machine Learning-Enabled Partially Observable Markov Decision Process Framework for Early Sepsis Prediction

Liu Z, Khojandi A, Li X, Mohammed A, Davis RL, Kamaleswaran R.

Prediction of low Apgar score at five minutes following labor induction intervention in vaginal deliveries: machine learning approach for imbalanced data

Tarimo CS, Bhuyan SS, Zhao Y, Ren W, Mohammed A, et al.

Coexpression network analysis of human Candida infection reveals key modules and hub genes responsible for host-pathogen interactions

Naik S, Mohammed A.

Consensus Gene Network Analysis Identifies the Key Similarities and Differences in Endothelial and Epithelial Cell Dynamics after Candida albicans Infection

Naik S, Mohammed A.

Machine Learning Identifies Complicated Sepsis Course and Subsequent Mortality Based on 20 Genes in Peripheral Blood Immune Cells at 24 H Post-ICU Admission

Banerjee S, Mohammed A, Wang H, Palaniyar N, Kamaleswaran R.

HeMA: A Hierarchically Enriched Machine Learning Approach for Managing False Alarms in Real Time: A Sepsis Prediction Case Study

Liu Z, Khojandi A, Mohammed A, Li X, Chinthala LK, Davis RL, Kamaleswaran R.

Integrative Analyses of Circulating Small RNAs and Kidney Graft Transcriptome in Transplant Glomerulopathy

Kuscu C, Kiran M, Mohammed A, Kuscu C, Satpathy S, Wolen A, et al.

eARDS: A multi-center validation of an interpretable machine-learning algorithm of early onset ARDS among critically ill adults with COVID-19

Singhal L, Garg Y, Yang P, Tabaie A, Wong AI, Mohammed A, et al.

Electrocardiographic Changes Predate Parkinson's Disease Onset

Akbilgic O*, Kamaleswaran R*, Mohammed A*, et al. (*co-first author)

Temporal Differential Expression of Physiomarkers Predicts Sepsis in Critically Ill Adults

Mohammed A, Van Wyk F, Chinthala LK, Khojandi A, Davis RL, Coopersmith CM, Kamaleswaran R.

Using Machine Learning to Predict Early Onset Acute Organ Failure in Critically Ill ICU Patients with Sickle Cell Disease

Mohammed A, Podila P, Davis R, Ataga K, Hankins J, Kamaleswaran R.

Differential gene expression analysis reveals novel genes and pathways in pediatric septic shock patients

Mohammed A, Cui Y, Mas V, Kamaleswaran R.

A minimal set of physiomarkers in continuous high-frequency data streams predict adult sepsis onset earlier

van Wyk F, Khojandi A, Mohammed A, Begoli E, Davis RL, Kamaleswaran R.

CancerDiscover: An integrated pipeline for cancer biomarker and cancer class prediction from high-throughput sequencing data

Mohammed A*, Biegert G*, Adamec J, Helikar T.

A Mechanistic Computational Model Reveals That Plasticity of CD4+ T Cell Differentiation Is a Function of Cytokine Composition and Dosage

Puniya BL, Todd RG, Mohammed A, Brown DM, Barberis M, Helikar T.

Identification of potential tissue-specific cancer biomarkers and development of cancer versus normal genomic classifiers

Mohammed A, Biegert G, Adamec J, Helikar T.

Genome sequence analysis and characterization of Bacillus infantis NRRL B-14911 that has a potential to induce myocarditis in A/J mice

Mohammed A*, Massilamany C*, Loy JD, Purvis T, et al. (*co-first author)

Application of Hierarchical enzyme classification method reveals the role of gut microbiome in human metabolism

Mohammed A, Guda C.

LocSigDB: a database of experimental and predicted protein localization signals

Negi S, Pandey S, Srinivasan S, Mohammed A, Guda C.

Computational Approaches for Automated Classification of Enzyme Sequences

Mohammed A, Guda C.

Early Detection of Rare Disease Using Hierarchical Set-to-Sequence Modeling of Structured Electronic Health Records

Ma Y, Chinthala LK, Mohammed A, Davis RL, Colonna V.

Identity-by-descent captures shared environmental factors at biobank scale

Marsico F, Buonaiuto S, Amos-Abanyie EK, Chinthala LK, Mohammed A, et al.

The Biorepository and Integrative Genomics resource for inclusive genomics: insights from a diverse pediatric and admixed cohort

Buonaiuto S, Marsico F, Mohammed A, Chinthala LK, et al.

Machine learning predicts early onset of fever from continuous physiological data of critically ill patients

Singh A, Mohammed A, Chinthala L, Kamaleswaran R.

A comprehensive logic-based model of the human immune system to study the dynamics responses to mono- and coinfections

Puniya BL, Moore R, Mohammed A, Amin R, La Fleur A, Helikar T.

Between Privacy and Utility: Navigating Inference Risks in De-Identified Health Data

Kar S, Chinthala L, Mohammed A, Davis R, Sakib SK.

Characterization and Prediction of Norepinephrine Response in Critically Ill Adults

Gunturkun F, Khojandi A, Ayvat P, Davis RL, Baucum M, Chinthala L, Mohammed A, Shafi N.

The Biorepository and Integrative Genomics (BIG) Initiative: Addressing genomic health care disparities in underserved communities

Brown C, Rooney R, Arnold S, Davis R, Hendrix C, Mohammed A, et al.

Machine Learning Predicts Acute Kidney Injury in Hospitalized Patients with Sickle Cell Disease

Zahr R*, Mohammed A*, Naik S, Faradji D, Lebensburger JD, Ataga KI, Davis RL. (*co-first author)

Hemodynamic Features Predict Elevated Intracranial Pressure in Critically Ill Children

Ackerman K, Mohammed A, Chinthala L, Davis R, Kamaleswaran R, et al.

Identification of Molecular Markers for Liver Cirrhosis by Single-Nucleus RNA Sequencing

Kuscu C, Kuscu C, Mohammed A, Shetty A, Maluf DG, Eason J, Mas V.