Author

Yasaman Fatapour

Cedars-Sinai Medical CenterORCID 0000-0001-8660-1621Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Signature Assays, Organisms & Methods

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Organism:human· 2 Research Focus:differential expression· 1 Approach:machine learning (classical)· 1 Assay:scRNA-seq· 1 Approach:statistical model· 1 Research Focus:variant effect prediction· 1 Assay:whole-genome sequencing· 1

Frequent Co-Authors

Aditi KuchiiD· 1 shared Hongyu LiuiD· 1 shared Jacob S. BerkowitziD· 1 shared James P. BrodyiD· 1 shared Jose Miguel Acitores CortinaiD· 1 shared Nicholas P. TatonettiiD· 1 shared

Activity by Year

2025-2025 · peak 2/yr

Co-Author Collaboration Network

6 top collaborators · 10 mutual co-author links

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1 shared paperBioinformatics

Aditi KuchiORCID

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2 papers

Recent·Most cited

GenomicsBioData MiningPeer-reviewedJun 19, 2025

A compact encoding of the genome suitable for machine learning prediction of traits and genetic risk scores.

Yasaman Fatapour, James P. Brody

This paper shows that a compact genotype representation using chromosome-scale length variation can effectively predict phenotypes like sex and height using machine learning.

4 citations

humanwhole-genome sequencing

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BioinformaticsbioRxivPreprintJun 17, 2025

Cell-Type Specific Single-Cell Signatures Reveal Nephrotoxic Drug Effects

Aditi Kuchi, Jose Miguel Acitores Cortina, Hongyu Liu…·Nicholas P. Tatonetti

The paper identifies cell-type specific single-cell signatures that reveal nephrotoxic drug effects and improves prediction of drug-induced kidney injury compared to bulk RNA sequencing.

DOI 10.1101/2025.06.17.660070

humanscRNA-seq

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