Y
Yasir Rahmatallah
Associate Professor
faculty
Biomedical Informatics, College of Medicine
18 h-index
74 pubs
1,630 cited
Research Areas
Biography and Research Information
OverviewAI-generated summary
Yasir Rahmatallah is an Associate Professor in Biomedical Informatics at the University of Arkansas for Medical Sciences. His research program focuses on the application of computational methods and machine learning to analyze biological data for disease diagnosis and understanding. He has investigated the use of voice samples and spectrogram images for the identification of Parkinson’s disease, developing pre-trained convolutional neural networks for this purpose. Rahmatallah also studies gene expression patterns in plants, particularly rice, in response to beneficial bacteria and environmental stressors like salt stress, exploring how these interactions impact growth and gene regulation. His work in this area includes metagenomic analysis of soil bacteria in rice fields. In clinical research, his publications address platelet function and inflammatory dysregulation in patients with chronic kidney disease, comparing the effects of different antiplatelet medications. Rahmatallah holds an h-index of 18 with over 1,600 citations across his 73 publications.
Metrics
- h-index: 18
- Publications: 74
- Citations: 1,630
Selected Publications
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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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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)
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A machine learning method to process voice samples for identification of Parkinson’s disease (2023)
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FRI291 Musashi1 And Musashi2 Mark Distinct Pituitary Stem Cell Populations (2023)
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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 (2023)
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A Machine Learning Method to Process Voice Samples for Identification of Parkinson’s Disease (2023)
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Ticagrelor inhibits platelet aggregation and reduces inflammatory burden more than clopidogrel in patients with stages 4 or 5 chronic kidney disease (2023)
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Platelet-Dependent Inflammatory Dysregulation in Patients with Stages 4 or 5 Chronic Kidney Disease: A Mechanistic Clinical Study (2022)
Grants & Funding
- 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
Collaboration Network
Top Collaborators
Jing Jin
15 shared publications
In database
- Abstract 1923: A tract of homozygosity analysis reveals methylation-driven <i>CSMD1</i> expression in non-small cell lung cancers
- A Tracts of Homozygosity Approach Identifies Methylation-Regulated <i>CSMD1</i> Expression Targets in Non–Small Cell Lung Cancers Related to Smoking Behavior
- Supplementary Fig. S2 from A Tracts of Homozygosity Approach Identifies Methylation-Regulated <i>CSMD1</i> Expression Targets in Non–Small Cell Lung Cancers Related to Smoking Behavior
- Data from A Tracts of Homozygosity Approach Identifies Methylation-Regulated <i>CSMD1</i> Expression Targets in Non–Small Cell Lung Cancers Related to Smoking Behavior
- Supplementary Fig. S3 from A Tracts of Homozygosity Approach Identifies Methylation-Regulated <i>CSMD1</i> Expression Targets in Non–Small Cell Lung Cancers Related to Smoking Behavior
Showing 5 of 15 shared publications
Horacio Gómez-Acevedo
15 shared publications
In database
- Abstract 1923: A tract of homozygosity analysis reveals methylation-driven <i>CSMD1</i> expression in non-small cell lung cancers
- A Tracts of Homozygosity Approach Identifies Methylation-Regulated <i>CSMD1</i> Expression Targets in Non–Small Cell Lung Cancers Related to Smoking Behavior
- Supplementary Fig. S2 from A Tracts of Homozygosity Approach Identifies Methylation-Regulated <i>CSMD1</i> Expression Targets in Non–Small Cell Lung Cancers Related to Smoking Behavior
- Data from A Tracts of Homozygosity Approach Identifies Methylation-Regulated <i>CSMD1</i> Expression Targets in Non–Small Cell Lung Cancers Related to Smoking Behavior
- Supplementary Fig. S3 from A Tracts of Homozygosity Approach Identifies Methylation-Regulated <i>CSMD1</i> Expression Targets in Non–Small Cell Lung Cancers Related to Smoking Behavior
Showing 5 of 15 shared publications
Yong‐Moon Park
15 shared publications
In database
- Abstract 1923: A tract of homozygosity analysis reveals methylation-driven <i>CSMD1</i> expression in non-small cell lung cancers
- A Tracts of Homozygosity Approach Identifies Methylation-Regulated <i>CSMD1</i> Expression Targets in Non–Small Cell Lung Cancers Related to Smoking Behavior
- Supplementary Fig. S2 from A Tracts of Homozygosity Approach Identifies Methylation-Regulated <i>CSMD1</i> Expression Targets in Non–Small Cell Lung Cancers Related to Smoking Behavior
- Data from A Tracts of Homozygosity Approach Identifies Methylation-Regulated <i>CSMD1</i> Expression Targets in Non–Small Cell Lung Cancers Related to Smoking Behavior
- Supplementary Fig. S3 from A Tracts of Homozygosity Approach Identifies Methylation-Regulated <i>CSMD1</i> Expression Targets in Non–Small Cell Lung Cancers Related to Smoking Behavior
Showing 5 of 15 shared publications
David W. Ussery
15 shared publications
In database
- Abstract 1923: A tract of homozygosity analysis reveals methylation-driven <i>CSMD1</i> expression in non-small cell lung cancers
- A Tracts of Homozygosity Approach Identifies Methylation-Regulated <i>CSMD1</i> Expression Targets in Non–Small Cell Lung Cancers Related to Smoking Behavior
- Supplementary Fig. S2 from A Tracts of Homozygosity Approach Identifies Methylation-Regulated <i>CSMD1</i> Expression Targets in Non–Small Cell Lung Cancers Related to Smoking Behavior
- Data from A Tracts of Homozygosity Approach Identifies Methylation-Regulated <i>CSMD1</i> Expression Targets in Non–Small Cell Lung Cancers Related to Smoking Behavior
- Supplementary Fig. S3 from A Tracts of Homozygosity Approach Identifies Methylation-Regulated <i>CSMD1</i> Expression Targets in Non–Small Cell Lung Cancers Related to Smoking Behavior
Showing 5 of 15 shared publications
Mohammed S. Orloff
15 shared publications
In database
- Abstract 1923: A tract of homozygosity analysis reveals methylation-driven <i>CSMD1</i> expression in non-small cell lung cancers
- A Tracts of Homozygosity Approach Identifies Methylation-Regulated <i>CSMD1</i> Expression Targets in Non–Small Cell Lung Cancers Related to Smoking Behavior
- Supplementary Fig. S2 from A Tracts of Homozygosity Approach Identifies Methylation-Regulated <i>CSMD1</i> Expression Targets in Non–Small Cell Lung Cancers Related to Smoking Behavior
- Data from A Tracts of Homozygosity Approach Identifies Methylation-Regulated <i>CSMD1</i> Expression Targets in Non–Small Cell Lung Cancers Related to Smoking Behavior
- Supplementary Fig. S3 from A Tracts of Homozygosity Approach Identifies Methylation-Regulated <i>CSMD1</i> Expression Targets in Non–Small Cell Lung Cancers Related to Smoking Behavior
Showing 5 of 15 shared publications
Ebrahim Jakoet
14 shared publications
In database
- A Tracts of Homozygosity Approach Identifies Methylation-Regulated <i>CSMD1</i> Expression Targets in Non–Small Cell Lung Cancers Related to Smoking Behavior
- Supplementary Fig. S2 from A Tracts of Homozygosity Approach Identifies Methylation-Regulated <i>CSMD1</i> Expression Targets in Non–Small Cell Lung Cancers Related to Smoking Behavior
- Data from A Tracts of Homozygosity Approach Identifies Methylation-Regulated <i>CSMD1</i> Expression Targets in Non–Small Cell Lung Cancers Related to Smoking Behavior
- Supplementary Fig. S3 from A Tracts of Homozygosity Approach Identifies Methylation-Regulated <i>CSMD1</i> Expression Targets in Non–Small Cell Lung Cancers Related to Smoking Behavior
- Supplementary Fig. S4 from A Tracts of Homozygosity Approach Identifies Methylation-Regulated <i>CSMD1</i> Expression Targets in Non–Small Cell Lung Cancers Related to Smoking Behavior
Showing 5 of 14 shared publications
Galina Glazko
University of Arkansas for Medical Sciences
9 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
- 16S rRNA Gene-Based Metagenomic Analysis of Rhizosphere Soil Bacteria in Arkansas Rice Crop Fields
- 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
- 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 9 shared publications
Anu Iyer
Georgia Institute of Technology (US)
5 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
- Voice Samples for Patients with Parkinson’s Disease and Healthy Controls
- Pre-trained Convolutional Neural Networks Identify Parkinson’s Disease from Spectrogram Images of Voice Samples
Aaron S. Kemp
University of Arkansas for Medical Sciences
5 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
- Voice Samples for Patients with Parkinson’s Disease and Healthy Controls
- Pre-trained Convolutional Neural Networks Identify Parkinson’s Disease from Spectrogram Images of Voice Samples
Lakshmi Pillai
University of Arkansas for Medical Sciences
5 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
- Voice Samples for Patients with Parkinson’s Disease and Healthy Controls
- Pre-trained Convolutional Neural Networks Identify Parkinson’s Disease from Spectrogram Images of Voice Samples
Fred Prior
University of Arkansas for Medical Sciences
5 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
- Voice Samples for Patients with Parkinson’s Disease and Healthy Controls
- Pre-trained Convolutional Neural Networks Identify Parkinson’s Disease from Spectrogram Images of Voice Samples
Linda Larson‐Prior
University of Arkansas for Medical Sciences
5 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
- Voice Samples for Patients with Parkinson’s Disease and Healthy Controls
- Pre-trained Convolutional Neural Networks Identify Parkinson’s Disease from Spectrogram Images of Voice Samples
Tuhin Virmani
University of Arkansas for Medical Sciences
5 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
- Voice Samples for Patients with Parkinson’s Disease and Healthy Controls
- Pre-trained Convolutional Neural Networks Identify Parkinson’s Disease from Spectrogram Images of Voice Samples
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>
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>
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