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Presence
Current · Arkansas
Last published
2026
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OpenAlex · ORCID
Refreshed
2026-08-15
Y
Yasir Rahmatallah
Associate Professor
Also affiliated: Queen's University Belfast (2013); University of Arkansas Medical Center (2014–2025); University of Arkansas System (2020); Abterra Biosciences (United States) (2020); Tampere University (2015); Center for Cancer Research (2013); Arkansas Department of Agriculture (2018)
Faculty Researcher
Biomedical Informatics, College of Medicine
19 h-index
98 pubs
1,686 cited
Research Areas
Biomedical Subjects
Biography and Research Information
OverviewAI-generated summary
Yasir Rahmatallah's research focuses on the application of computational methods and machine learning to analyze biological data, with a particular emphasis on disease mechanisms and biomarker discovery. His work includes developing machine learning models for the early identification of Parkinson's disease using voice samples, as demonstrated in his 2023 and 2025 publications. Rahmatallah also investigates gene expression patterns in plants, specifically examining how beneficial bacteria influence rice growth and stress responses, as detailed in his 2022 and 2023 studies. His research extends to human health, with publications on inflammatory dysregulation and the comparative effects of antiplatelet medications in patients with chronic kidney disease. Rahmatallah has a significant publication record, with 98 total publications and 1,650 citations, and an h-index of 19. He collaborates with researchers at the University of Arkansas for Medical Sciences, including Mohammed S. Orloff, David W. Ussery, and Horacio Gómez-Acevedo, as well as Ebrahim Jakoet from the University of Arkansas at Little Rock.
Metrics
- h-index: 19
- Publications: 98
- Citations: 1,686
Selected Publications
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Author Correction: A machine learning method to process voice samples for identification of Parkinson’s disease (2026)
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Abstract 7642: Nicotine dependence partially mediates the association between <i>IP6K3</i> genetic variation and risk of lung squamous cell carcinoma among smokers (2026)
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ZNF16 is a nucleolar-associated protein that regulates expression of rDNA and cancer-associated genes (2025)
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SAT-016 Musashi Contributes to the Specification and Maintenance of Distinct Pituitary Cell Lineages. (2025)
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ZNF16 is a nucleolar-associated protein that regulates expression of the rDNA and cancer-associated genes (2025)
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A Tracts of Homozygosity Approach Identifies Methylation-Regulated <i>CSMD1</i> Expression Targets in Non–Small Cell Lung Cancers Related to Smoking Behavior (2025)
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Abstract 1923: A tract of homozygosity analysis reveals methylation-driven <i>CSMD1</i> expression in non-small cell lung cancers (2025)
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Improving data interpretability with new differential sample variance gene set tests (2025)
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Pre-trained convolutional neural networks identify Parkinson’s disease from spectrogram images of voice samples (2025)
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Pre-trained Convolutional Neural Networks Identify Parkinson’s Disease from Spectrogram Images of Voice Samples (2024)
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Higher Glycolysis in Circulating Leukocytes in Patients with CKD (2024)
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Improving data interpretability with new differential sample variance gene set tests (2024)
Grants & Funding
As listed on this researcher's institutional profile.
- Epigenetic regulation of differentially expressed genes in cutaneous T-cell lymphoma VA/CAVHS Co-Investigator
- Partnerships for Biomedical Research in Arkansas NIH Co-Investigator
- Formation of the IDeA National Resource for Proteomics NIH/NIGMS Co-Investigator
- Expand data science training, access to publicly available data, and computational resources within the Arkansas INBRE network NIH/NIGMS Co-Investigator
- Platelet-Leukocyte Axis in Patients with Chronic Kidney Disease NIH/NIGMS Co-Investigator
- Integrating Gene Expression Profiles from Different Platforms into a Robust and Clinically Relevant Prognostic and Predictive Tool for Pediatric Leukemia NIH/NIGMS Principal Investigator
- Center for Translational Pediatric Research NIH/NIGMS Co-Investigator
- Resources for Development and Validation of Radiomic Analyses and Adaptive Therapy NIH/NCI Co-Investigator
- Phone-Collected Speech Corpus for Remote Assessment of Parkinson’s Disease UAMS College of Medicine Principal Investigator
- Comparison of different health care delivery methods in a rural underserved population of People with Parkinson's Disease UAMS Translational Research Institute Co-Investigator
- Machine Learning Approaches for Remote Pathological Speech Assessment for Parkinson's Disease NSF Arkansas EPSCoR Select
Collaboration Network
Top Collaborators
Galina Glazko
University of Arkansas for Medical Sciences
8 shared publications
In database
- Common gene expression patterns are observed in rice roots during associations with plant growth-promoting bacteria, Herbaspirillum seropedicae and Azospirillum brasilense
- Azospirillum brasilense improves rice growth under salt stress by regulating the expression of key genes involved in salt stress response, abscisic acid signaling, and nutrient transport, among others
- 16S rRNA Gene-Based Metagenomic Analysis of Rhizosphere Soil Bacteria in Arkansas Rice Crop Fields
- Milk Formula Diet Alters Bacterial and Host Protein Profile in Comparison to Human Milk Diet in Neonatal Piglet Model
- Investigating the transcriptomic responses in rice roots during interactions with plant growth‐promoting bacteria, <i>Burkholderia unamae</i>
Showing 5 of 8 shared publications
Arijit Mukherjee
Conway School of Landscape Design (US)
4 shared publications
- Common gene expression patterns are observed in rice roots during associations with plant growth-promoting bacteria, Herbaspirillum seropedicae and Azospirillum brasilense
- The plant growth-promoting bacteria, Azospirillum brasilense, induce a diverse array of genes in rice shoots and promote their growth
- Azospirillum brasilense improves rice growth under salt stress by regulating the expression of key genes involved in salt stress response, abscisic acid signaling, and nutrient transport, among others
- Investigating the transcriptomic responses in rice roots during interactions with plant growth‐promoting bacteria, <i>Burkholderia unamae</i>
Anu Iyer
Georgia Institute of Technology (US)
4 shared publications
- A machine learning method to process voice samples for identification of Parkinson’s disease
- Pre-trained convolutional neural networks identify Parkinson’s disease from spectrogram images of voice samples
- A Machine Learning Method to Process Voice Samples for Identification of Parkinson’s Disease
- Pre-trained Convolutional Neural Networks Identify Parkinson’s Disease from Spectrogram Images of Voice Samples
Aaron S. Kemp
University of Arkansas for Medical Sciences
4 shared publications
In database
- A machine learning method to process voice samples for identification of Parkinson’s disease
- Pre-trained convolutional neural networks identify Parkinson’s disease from spectrogram images of voice samples
- A Machine Learning Method to Process Voice Samples for Identification of Parkinson’s Disease
- Pre-trained Convolutional Neural Networks Identify Parkinson’s Disease from Spectrogram Images of Voice Samples
Lakshmi Pillai
University of Arkansas for Medical Sciences
4 shared publications
In database
- A machine learning method to process voice samples for identification of Parkinson’s disease
- Pre-trained convolutional neural networks identify Parkinson’s disease from spectrogram images of voice samples
- A Machine Learning Method to Process Voice Samples for Identification of Parkinson’s Disease
- Pre-trained Convolutional Neural Networks Identify Parkinson’s Disease from Spectrogram Images of Voice Samples
Fred Prior
University of Arkansas for Medical Sciences
4 shared publications
In database
- A machine learning method to process voice samples for identification of Parkinson’s disease
- Pre-trained convolutional neural networks identify Parkinson’s disease from spectrogram images of voice samples
- A Machine Learning Method to Process Voice Samples for Identification of Parkinson’s Disease
- Pre-trained Convolutional Neural Networks Identify Parkinson’s Disease from Spectrogram Images of Voice Samples
Linda Larson‐Prior
University of Arkansas for Medical Sciences
4 shared publications
In database
- A machine learning method to process voice samples for identification of Parkinson’s disease
- Pre-trained convolutional neural networks identify Parkinson’s disease from spectrogram images of voice samples
- A Machine Learning Method to Process Voice Samples for Identification of Parkinson’s Disease
- Pre-trained Convolutional Neural Networks Identify Parkinson’s Disease from Spectrogram Images of Voice Samples
Tuhin Virmani
University of Arkansas for Medical Sciences
4 shared publications
In database
- A machine learning method to process voice samples for identification of Parkinson’s disease
- Pre-trained convolutional neural networks identify Parkinson’s disease from spectrogram images of voice samples
- A Machine Learning Method to Process Voice Samples for Identification of Parkinson’s Disease
- Pre-trained Convolutional Neural Networks Identify Parkinson’s Disease from Spectrogram Images of Voice Samples
John Cook
Conway School of Landscape Design
3 shared publications
In database
- The plant growth-promoting bacteria, Azospirillum brasilense, induce a diverse array of genes in rice shoots and promote their growth
- Azospirillum brasilense improves rice growth under salt stress by regulating the expression of key genes involved in salt stress response, abscisic acid signaling, and nutrient transport, among others
- Investigating the transcriptomic responses in rice roots during interactions with plant growth‐promoting bacteria, <i>Burkholderia unamae</i>
Zachariah Degon
Conway School of Landscape Design
3 shared publications
In database
- Common gene expression patterns are observed in rice roots during associations with plant growth-promoting bacteria, Herbaspirillum seropedicae and Azospirillum brasilense
- The plant growth-promoting bacteria, Azospirillum brasilense, induce a diverse array of genes in rice shoots and promote their growth
- Azospirillum brasilense improves rice growth under salt stress by regulating the expression of key genes involved in salt stress response, abscisic acid signaling, and nutrient transport, among others
Nishank Jain
University of Arkansas for Medical Sciences
3 shared publications
In database
- Platelet-Dependent Inflammatory Dysregulation in Patients with Stages 4 or 5 Chronic Kidney Disease: A Mechanistic Clinical Study
- Ticagrelor inhibits platelet aggregation and reduces inflammatory burden more than clopidogrel in patients with stages 4 or 5 chronic kidney disease
- Higher Glycolysis in Circulating Leukocytes in Patients with CKD
Stephanie D. Byrum
University of Arkansas for Medical Sciences
2 shared publications
In database
- Milk Formula Diet Alters Bacterial and Host Protein Profile in Comparison to Human Milk Diet in Neonatal Piglet Model
- SAT-016 Musashi Contributes to the Specification and Maintenance of Distinct Pituitary Cell Lineages.
Adam Corken
University of Arkansas for Medical Sciences
2 shared publications
In database
- Platelet-Dependent Inflammatory Dysregulation in Patients with Stages 4 or 5 Chronic Kidney Disease: A Mechanistic Clinical Study
- Ticagrelor inhibits platelet aggregation and reduces inflammatory burden more than clopidogrel in patients with stages 4 or 5 chronic kidney disease
Jerry Ware
University of Arkansas for Medical Sciences
2 shared publications
In database
- Platelet-Dependent Inflammatory Dysregulation in Patients with Stages 4 or 5 Chronic Kidney Disease: A Mechanistic Clinical Study
- Ticagrelor inhibits platelet aggregation and reduces inflammatory burden more than clopidogrel in patients with stages 4 or 5 chronic kidney disease
Junqiang Dai
University of Kansas Medical Center (US)
2 shared publications
- Platelet-Dependent Inflammatory Dysregulation in Patients with Stages 4 or 5 Chronic Kidney Disease: A Mechanistic Clinical Study
- Ticagrelor inhibits platelet aggregation and reduces inflammatory burden more than clopidogrel in patients with stages 4 or 5 chronic kidney disease
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