Author

Babak Khorsand

University of California, IrvineORCID 0000-0002-1585-3909Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Bioinformatics· 3

Signature Assays, Organisms & Methods

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Research Focus:differential expression· 2 Organism:human· 2 Approach:machine learning (classical)· 2 Assay:bulk RNA-seq· 1 Assay:protein sequence· 1 Research Focus:software / tool· 1 Approach:statistical model· 1

Frequent Co-Authors

Hamidreza HouriiD· 2 shared Ali Akbar Yousefi· 1 shared Alireza Aghaahmadi· 1 shared Javad ZahiriiD· 1 shared Maedeh MohagheghiD· 1 shared Mina OwrangiD· 1 shared Mohammadjavad Kargar· 1 shared Nastaran AsriiD· 1 shared

Activity by Year

2017-2025 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 16 mutual co-author links

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2 shared papersBioinformatics

Hamidreza HouriORCID

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3 papers

Recent·Most cited

Comprehensive Transcriptomic Analysis of Hepatocellular Carcinoma: Uncovering Shared and Unique Molecular Signatures Across Diverse Etiologies

Babak Khorsand, Nazanin Naderi, Seyedeh Sara Karimian, Maedeh Mohaghegh, Alireza Aghaahmadi, Seyedeh Negin Hadisadegh, Mina Owrang, Hamidreza Houri

This study identifies 125 consistently dysregulated genes across multiple HCC datasets, highlighting shared and unique molecular signatures associated with different etiologies.

Biochemistry and Biophysics Reports · Jul 1, 2025Peer-reviewedBioinformatics

Investigating Alzheimers Disease Biomarkers by Applying Machine Learning Models

Babak Khorsand, Shirin Salehi, Soroush Karimi, Sonia Karimipasand, Neda Fariborzi, Hamidreza Houri, Nastaran Asri

This study uses machine learning to diagnose Alzheimer's Disease by analyzing biomarkers and clinical features, finding high accuracy with KNN, SVM, RF, and DT models.

bioRxiv · Mar 19, 2025 · PreprintBioinformatics

AntAngioCOOL: An R Package for Computational Detection of Anti-Angiogenic Peptides

Javad Zahiri, Babak Khorsand, Ramin Shirali Hossein Zade, Mohammadjavad Kargar, Ali Akbar Yousefi

AntAngioCOOL is an R package that predicts anti-angiogenic peptides using machine learning, achieving 88% sensitivity and 77% specificity on an independent test set.

bioRxiv · Dec 14, 2017 · PreprintBioinformatics