Author

Sina Khanmohammadi

University of OklahomaORCID 0000-0003-4023-9078Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Signature Assays, Organisms & Methods

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Research Focus:benchmarking· 2 Approach:deep learning· 2 Research Focus:phylogenetics / evolution· 2 Approach:statistical model· 2 Assay:DNA methylation· 1 Approach:machine learning (classical)· 1 Organism:multiple species· 1

Frequent Co-Authors

Ulises Rosas‐PuchuriiD· 3 shared Claudia Solís‐LemusiD· 2 shared Ricardo Betancur‐RiD· 2 shared Aintzane SantaquiteriaiD· 1 shared Betancur-R. Ricardo· 1 shared Dahiana ArcilaiD· 1 shared Emanuell Duarte-RibeiroiD· 1 shared Guillermo OrtíiD· 1 shared

Activity by Year

2023-2025 · peak 1/yr

Co-Author Collaboration Network

9 top collaborators · 20 mutual co-author links

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3 shared papersEvolutionary Biology

Ulises Rosas‐PuchuriORCID

Mutual co-authors in cluster

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3 papers

Recent·Most cited

Evolutionary BiologybioRxivPreprintNov 16, 2025

Sparse learning for scalable phylogenetic network inference

Ulises Rosas‐Puchuri, Nathan Kolbow, Claudia Solís‐Lemus…·Betancur-R. Ricardo

This paper introduces Qsin, a sparse learning method that reduces the size of concordance factors tables for phylogenetic network inference while maintaining accuracy and reducing computational time.

DOI 10.1101/2025.11.16.688704

deep learningstatistical model

𝕏 PostBluesky

Evolutionary BiologyMethods in Ecology and EvolutionPeer-reviewedAug 5, 2024

Non-linear phylogenetic regression using regularized kernels

Ulises Rosas‐Puchuri, Aintzane Santaquiteria, Sina Khanmohammadi…·Ricardo Betancur‐R

This paper introduces a non-linear phylogenetic regression method using regularized kernels, showing improved error reduction in both biological and simulated datasets.

6 citations

deep learningstatistical model

𝕏 PostBluesky

Evolutionary BiologybioRxivPreprintSep 20, 2023

Dissecting Factors Underlying Phylogenetic Uncertainty Using Machine Learning Models

Ulises Rosas‐Puchuri, Emanuell Duarte-Ribeiro, Sina Khanmohammadi…·Ricardo Betancur‐R

This study uses machine learning to analyze phylogenetic datasets, finding that non-linear models outperform linear ones in capturing complex relationships affecting phylogenomic inference.

DOI 10.1101/2023.09.20.558635

multiple speciesDNA methylation

𝕏 PostBluesky