Author

Ali Poursina

University of California, San Francisco

Listed by name: these papers carry no ORCID for this author, so different people with the same name may be mixed together.

Biology Disciplines

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Bioinformatics· 3

Signature Assays, Organisms & Methods

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Assay:scRNA-seq· 3 Approach:deep learning· 2 Assay:long-read sequencing· 2 Assay:bulk RNA-seq· 1 Research Focus:differential expression· 1 Research Focus:gene regulatory network inference· 1 Organism:human· 1 Approach:statistical model· 1

Frequent Co-Authors

Ali Saberi· 3 shared Arsham Mikaeili NaminiiD· 3 shared Hamed Shateri NajafabadiiD· 3 shared Amin EmadiD· 2 shared Hani GoodarziiD· 2 shared Aiden Sababi· 1 shared Albertas NavickasiD· 1 shared Aldo Hernández-CorchadoiD· 1 shared

Activity by Year

2024-2026 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 34 mutual co-author links

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3 shared papersBioinformatics

Ali Saberi

Mutual co-authors in cluster

Author profile →

3 papers

Recent·Most cited

A latent space thermodynamic model of cell differentiation

Ali Poursina, Shayan Hajhashemi, Ali Saberi, Arsham Mikaeili Namini, Ariel Madrigal, Amin Emad, Hamed Shateri Najafabadi

This paper introduces scLSD, a thermodynamics-inspired model for cell differentiation that reconstructs differentiation trajectories and predicts fate commitment, outperforming existing methods.

bioRxiv · Mar 6, 2026 · PreprintBioinformatics

A long context RNA foundation model for predicting transcriptome architecture

Ali Saberi, Man Hung Choi, Shaopu Zhou, Mohsen Naghipourfar, Simai Wang, Aldo Hernández-Corchado, Rohan Dandage, Najia Bouaddouch, Arsham Mikaeili Namini, Ali Poursina, Vijay Ramani, Amin Emad, Hamed Shateri Najafabadi, Hani Goodarzi

The paper introduces Mach-1, an RNA foundation model that predicts transcriptome architecture from DNA sequence, validated through experimental perturbations and CRISPR editing.

bioRxiv · Aug 26, 2024 · PreprintBioinformatics

Accurate isoform quantification in bulk and single-cell RNA-seq by integrating multi-platform sequencing data and sequence-to-expression models

Michael J. Apostolides, Jichen Wang, Ali Saberi, Benedict Choi, Tabea Mittmann, Ali Poursina, Arsham Mikaeili Namini, Darian Salehi, Gabrielle Perron, Albertas Navickas, Larisa M. Soto, Aiden Sababi, Hani Goodarzi, Hamed Shateri Najafabadi

This paper introduces MPAQT, a framework that improves isoform quantification in bulk and single-cell RNA-seq by integrating short- and long-read data and using sequence-to-expression models.

bioRxiv · Jul 11, 2024 · PreprintBioinformatics