Mary Qu Yang
Affiliation confirmed via AI analysis of OpenAlex, ORCID, and web sources.
Professor - STEM
Also affiliated: Oak Ridge Associated Universities (2008–2009); Oak Ridge National Laboratory (2008); United States Department of Health and Human Services (2006–2009); National Institutes of Health (2006–2010); University of Minnesota (2021); National Institute of Standards and Technology (2019); Harvard University (2007); University of Toronto (2013); George Washington University (2018); Cornell University (2021); Purdue University West Lafayette (2003–2010); University of Arkansas Medical Center (2014–2021); Harvard University Press (2009); Massachusetts General Hospital (2007); National Human Genome Research Institute (2006–2010); Information Technology Laboratory (2019); SBS CyberSecurity (United States) (2019); Conway School of Landscape Design (2023); Department of Health and Human Services (2007); Oak Ridge Institute for Science and Education (2008); Mitre (United States) (2019); Indiana University – Purdue University Indianapolis (2008–2010)
Faculty Researcher
Information Science
Research Areas
Biomedical Subjects
Biography and Research Information
OverviewAI-generated summary
Mary Qu Yang's research focuses on the application of computational methods, including artificial intelligence and machine learning, to address complex biological and medical questions. Her work investigates gene regulation, disease classification, and therapeutic response characterization. She has explored the analytical validity of circulating tumor DNA sequencing assays for precision oncology and compared machine learning approaches for Alzheimer's disease classification. Yang also examines the tumor microenvironment and its prognostic impact in breast cancer, and uses gene regulation analysis to reveal perturbations associated with autism spectrum disorder during neural system development.
Her contributions extend to developing and applying computational tools for biological data analysis. This includes work on missing value recovery for single-cell RNA sequencing data, such as the cnnImpute method. Yang has also integrated single-cell transcriptome and network analysis to characterize therapeutic responses in chronic myeloid leukemia. Her research group is actively involved in bioinformatics and genomics, as evidenced by her h-index of 30 and over 5,000 citations across nearly 200 publications. She is a member of the ARA Academy and is recognized as a highly cited researcher.
Metrics
- h-index: 30
- Publications: 197
- Citations: 5,264
Selected Publications
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Single-Cell Transcriptomic Analysis Unveils Key Regulators and Signaling Pathways in Lung Adenocarcinoma Progression (2025)
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Characterizing the Tumor Microenvironment and Its Prognostic Impact in Breast Cancer (2024)
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Novel Thienopyrimidine-Hydrazinyl Compounds Induce DRP1-Mediated Non-Apoptotic Cell Death in Triple-Negative Breast Cancer Cells (2024)
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cnnImpute: missing value recovery for single cell RNA sequencing data (2024)
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A Deep Learning-Based Model for Gene Regulatory Network Inference (2023)
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Analysis of Single-Cell RNA Sequencing Data Unveils Novel Immune Prognostic Biomarkers (2023)
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Exploring Barriers to Diversity, Equity, and Inclusion in Communication Sciences and Disorders Students (2023)
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Additional file 2 of Deep oncopanel sequencing reveals within block position-dependent quality degradation in FFPE processed samples (2022)
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Additional file 3 of Deep oncopanel sequencing reveals within block position-dependent quality degradation in FFPE processed samples (2022)
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Integrating Single-Cell Transcriptome and Network Analysis to Characterize the Therapeutic Response of Chronic Myeloid Leukemia (2022)
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Deep oncopanel sequencing reveals within block position-dependent quality degradation in FFPE processed samples (2022)
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Missing Value Recovery for Single Cell RNA Sequencing Data (2021)
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Gene Regulation Analysis Reveals Perturbations of Autism Spectrum Disorder during Neural System Development (2021)
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Brain Tumor Segmentation Using Deep Neural Networks and Survival Prediction (2021)
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Merging Deep Learning and Data Analytics for Inferring Coronavirus Human Adaptive Transmutability and Transmissibility (2021)
ARA Academy 2023 ARA Fellow
Dr. Yang established the Systems Genomics Laboratory at UALR. She holds degrees in engineering, physics, electrical and computer engineering, and a PhD in Computational Science and Physics from Purdue University. She completed postdoctoral training in Human Genomics and Bioinformatics at the National Human Genome Research Institute (NHGRI) and worked as a Research Fellow there from 2008-2012.
Policy Impact
Established the MidSouth Bioinformatics Center at UALR, building computational genomics infrastructure that supports health research and precision medicine across the state.
Growth Areas
['Population Health Innovations & Clinical Research']
Collaboration Network
Top Collaborators
- Evaluating the analytical validity of circulating tumor DNA sequencing assays for precision oncology
- Deep oncopanel sequencing reveals within block position-dependent quality degradation in FFPE processed samples
- Deep oncopanel sequencing reveals fixation time- and within block position-dependent quality degradation in FFPE processed samples
- Additional file 3 of Deep oncopanel sequencing reveals within block position-dependent quality degradation in FFPE processed samples
- Additional file 2 of Deep oncopanel sequencing reveals within block position-dependent quality degradation in FFPE processed samples
- Evaluating the analytical validity of circulating tumor DNA sequencing assays for precision oncology
- Deep oncopanel sequencing reveals within block position-dependent quality degradation in FFPE processed samples
- Deep oncopanel sequencing reveals fixation time- and within block position-dependent quality degradation in FFPE processed samples
- Additional file 3 of Deep oncopanel sequencing reveals within block position-dependent quality degradation in FFPE processed samples
- Additional file 2 of Deep oncopanel sequencing reveals within block position-dependent quality degradation in FFPE processed samples
- Evaluating the analytical validity of circulating tumor DNA sequencing assays for precision oncology
- Deep oncopanel sequencing reveals within block position-dependent quality degradation in FFPE processed samples
- Deep oncopanel sequencing reveals fixation time- and within block position-dependent quality degradation in FFPE processed samples
- Additional file 3 of Deep oncopanel sequencing reveals within block position-dependent quality degradation in FFPE processed samples
- Additional file 2 of Deep oncopanel sequencing reveals within block position-dependent quality degradation in FFPE processed samples
- Evaluating the analytical validity of circulating tumor DNA sequencing assays for precision oncology
- Deep oncopanel sequencing reveals within block position-dependent quality degradation in FFPE processed samples
- Deep oncopanel sequencing reveals fixation time- and within block position-dependent quality degradation in FFPE processed samples
- Additional file 3 of Deep oncopanel sequencing reveals within block position-dependent quality degradation in FFPE processed samples
- Additional file 2 of Deep oncopanel sequencing reveals within block position-dependent quality degradation in FFPE processed samples
- Evaluating the analytical validity of circulating tumor DNA sequencing assays for precision oncology
- Deep oncopanel sequencing reveals within block position-dependent quality degradation in FFPE processed samples
- Deep oncopanel sequencing reveals fixation time- and within block position-dependent quality degradation in FFPE processed samples
- Additional file 3 of Deep oncopanel sequencing reveals within block position-dependent quality degradation in FFPE processed samples
- Additional file 2 of Deep oncopanel sequencing reveals within block position-dependent quality degradation in FFPE processed samples
- Evaluating the analytical validity of circulating tumor DNA sequencing assays for precision oncology
- Deep oncopanel sequencing reveals within block position-dependent quality degradation in FFPE processed samples
- Deep oncopanel sequencing reveals fixation time- and within block position-dependent quality degradation in FFPE processed samples
- Additional file 3 of Deep oncopanel sequencing reveals within block position-dependent quality degradation in FFPE processed samples
- Additional file 2 of Deep oncopanel sequencing reveals within block position-dependent quality degradation in FFPE processed samples
- Evaluating the analytical validity of circulating tumor DNA sequencing assays for precision oncology
- Deep oncopanel sequencing reveals within block position-dependent quality degradation in FFPE processed samples
- Deep oncopanel sequencing reveals fixation time- and within block position-dependent quality degradation in FFPE processed samples
- Additional file 3 of Deep oncopanel sequencing reveals within block position-dependent quality degradation in FFPE processed samples
- Additional file 2 of Deep oncopanel sequencing reveals within block position-dependent quality degradation in FFPE processed samples
- Deep oncopanel sequencing reveals within block position-dependent quality degradation in FFPE processed samples
- Deep oncopanel sequencing reveals fixation time- and within block position-dependent quality degradation in FFPE processed samples
- Additional file 3 of Deep oncopanel sequencing reveals within block position-dependent quality degradation in FFPE processed samples
- Additional file 2 of Deep oncopanel sequencing reveals within block position-dependent quality degradation in FFPE processed samples
- Deep oncopanel sequencing reveals within block position-dependent quality degradation in FFPE processed samples
- Deep oncopanel sequencing reveals fixation time- and within block position-dependent quality degradation in FFPE processed samples
- Additional file 3 of Deep oncopanel sequencing reveals within block position-dependent quality degradation in FFPE processed samples
- Additional file 2 of Deep oncopanel sequencing reveals within block position-dependent quality degradation in FFPE processed samples
- Deep oncopanel sequencing reveals within block position-dependent quality degradation in FFPE processed samples
- Deep oncopanel sequencing reveals fixation time- and within block position-dependent quality degradation in FFPE processed samples
- Additional file 3 of Deep oncopanel sequencing reveals within block position-dependent quality degradation in FFPE processed samples
- Additional file 2 of Deep oncopanel sequencing reveals within block position-dependent quality degradation in FFPE processed samples
- Deep oncopanel sequencing reveals within block position-dependent quality degradation in FFPE processed samples
- Deep oncopanel sequencing reveals fixation time- and within block position-dependent quality degradation in FFPE processed samples
- Additional file 3 of Deep oncopanel sequencing reveals within block position-dependent quality degradation in FFPE processed samples
- Additional file 2 of Deep oncopanel sequencing reveals within block position-dependent quality degradation in FFPE processed samples
- Deep oncopanel sequencing reveals within block position-dependent quality degradation in FFPE processed samples
- Deep oncopanel sequencing reveals fixation time- and within block position-dependent quality degradation in FFPE processed samples
- Additional file 3 of Deep oncopanel sequencing reveals within block position-dependent quality degradation in FFPE processed samples
- Additional file 2 of Deep oncopanel sequencing reveals within block position-dependent quality degradation in FFPE processed samples
- Deep oncopanel sequencing reveals within block position-dependent quality degradation in FFPE processed samples
- Deep oncopanel sequencing reveals fixation time- and within block position-dependent quality degradation in FFPE processed samples
- Additional file 3 of Deep oncopanel sequencing reveals within block position-dependent quality degradation in FFPE processed samples
- Additional file 2 of Deep oncopanel sequencing reveals within block position-dependent quality degradation in FFPE processed samples
- Deep oncopanel sequencing reveals within block position-dependent quality degradation in FFPE processed samples
- Deep oncopanel sequencing reveals fixation time- and within block position-dependent quality degradation in FFPE processed samples
- Additional file 3 of Deep oncopanel sequencing reveals within block position-dependent quality degradation in FFPE processed samples
- Additional file 2 of Deep oncopanel sequencing reveals within block position-dependent quality degradation in FFPE processed samples
- Deep oncopanel sequencing reveals within block position-dependent quality degradation in FFPE processed samples
- Deep oncopanel sequencing reveals fixation time- and within block position-dependent quality degradation in FFPE processed samples
- Additional file 3 of Deep oncopanel sequencing reveals within block position-dependent quality degradation in FFPE processed samples
- Additional file 2 of Deep oncopanel sequencing reveals within block position-dependent quality degradation in FFPE processed samples
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