Author

Ahmad Nasimian

Lund University

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· 2 Cancer Biology· 1

Signature Assays, Organisms & Methods

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Organism:human· 2 Approach:machine learning (classical)· 2 Research Focus:benchmarking· 1 Assay:bulk RNA-seq· 1 Research Focus:cancer & tumor microenvironment· 1 Approach:comparative / phylogenetic analysis· 1 Research Focus:drug response / drug discovery· 1 Approach:experimental assay· 1

Frequent Co-Authors

Julhash Uddin KaziiD· 3 shared Lars RönnstrandiD· 2 shared Özge TatlıiD· 2 shared Saleena YounusiD· 2 shared Anna Sandström GerdtssoniD· 1 shared Bianca Bochis· 1 shared Elna Persson· 1 shared Elsa Ernestal· 1 shared

Activity by Year

2023-2026 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 25 mutual co-author links

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3 shared papersBioinformatics

Julhash Uddin KaziORCID

Mutual co-authors in cluster

Author profile →

3 papers

Recent·Most cited

MTDH-SND1 disruption sensitizes ovarian cancer to ferroptosis and PARP inhibition

Parisa Esmaeili, Ahmad Nasimian, Elsa Ernestal, Elna Persson, Bianca Bochis, Yuan Li, Måns Zamore, Anna Sandström Gerdtsson, Julhash Uddin Kazi, Fredrik Levander

This study shows that disrupting the MTDH-SND1 complex in BRCA-deficient ovarian cancer increases sensitivity to ferroptosis and PARP inhibition, with potential immune system benefits.

bioRxiv · May 20, 2026 · PreprintCancer Biology

Understanding the characteristic behavior of the wild-type and mutant protein structure of FLT3 protein by computational methods

Saleena Younus, Özge Tatlı, Ahmad Nasimian, Julhash Uddin Kazi, Lars Rönnstrand

This study uses computational methods to analyze the structural behavior of FLT3 protein mutants in AML, identifying key structural changes and their implications for therapeutic targeting.

bioRxiv · Apr 18, 2024 · PreprintBioinformatics

A Clear, Legible, Explainable, Transparent, and Elucidative (CLETE) Binary Classification Platform for Tabular Data

Ahmad Nasimian, Saleena Younus, Özge Tatlı, Emma U. Hammarlund, Kenneth J. Pienta, Lars Rönnstrand, Julhash Uddin Kazi

The paper introduces a CLETE binary classification platform for tabular data that offers explainable, transparent, and modifiable machine learning models with various feature selection, sampling, and evaluation methods.

bioRxiv · Jun 20, 2023 · PreprintBioinformatics