Author

Anahita Khojandi

University of Arkansas at FayettevilleORCID 0000-0001-6818-2048Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Bioinformatics· 1 Genetic & Genomic Medicine· 1

Signature Assays, Organisms & Methods

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Research Focus:benchmarking· 2 Organism:human· 2 Assay:bulk RNA-seq· 1 Approach:deep learning· 1 Research Focus:differential expression· 1 Assay:electronic health records· 1 Approach:machine learning (classical)· 1

Frequent Co-Authors

Robert Lowell DavisiD· 2 shared Franco van WykiD· 1 shared Lokesh K. ChinthalaiD· 1 shared Nahian TahminiD· 1 shared Rishikesan KamaleswaraniD· 1 shared Tesfaye Mersha BayeiD· 1 shared

Activity by Year

2018-2026 · peak 1/yr

Co-Author Collaboration Network

6 top collaborators · 9 mutual co-author links

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2 shared papersGenetic & Genomic Medicine

Robert Lowell DavisORCID

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2 papers

Recent·Most cited

Genetic & Genomic MedicinemedRxivPreprintSep 7, 2026

X-Admix: An Interpretable Multimodal Cross-Attention Framework for Integrating Genotype, Local Ancestry, and Social Drivers of Health in Admixed African American Populations

Nahian Tahmin, Lokesh K. Chinthala, Tesfaye Mersha Baye…·Anahita Khojandi

DOI 10.64898/2026.09.03.26362179

humanbulk RNA-seq

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BioinformaticsbioRxivPreprintMay 15, 2018

Physiomarkers in Real-Time Physiological Data Streams Predict Adult Sepsis Onset Earlier Than Clinical Practice

Franco van Wyk, Anahita Khojandi, Robert Lowell Davis, Rishikesan Kamaleswaran

This study develops machine learning models to predict sepsis onset using real-time physiological data and white blood cell counts, achieving early detection up to 3.5 hours before symptoms.

4 citations

humanelectronic health records

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