Work

Systema: a framework for evaluating genetic perturbation response prediction beyond systematic variation.

Year: 2024

Type: article

Abstract: Acknowledgements: We thank F. Barkmann, J. Bues, J. Corria-Osorio, B. Deplancke, A. Dox, A. Gadetsky, J. Goumaz, M. Grcić, F. Gröger, Y. Hoshen, M. Jeon, Y. Jiang, M. Knežević, M. Kodryan, M. Künzle, L. Lorch, M. Lotfollahi, A. Oarga, A. Saadat, N. Simidjievski, S. Smarak Panigrahi, J. Sobotka, M. Voges, S. Wen and Y. Yu for valuable discussions and for their support. We thank C. Wang for reviewing our scGPT code. We gratefully acknowledge the support of the Peter und Traudl Engelhorn Foundation to R.V., the Swiss National Science Foundation (starting grant TMSGI2_226252, grant IC00I0-231922) to M.B. and the European Union (DecodeSC, 101040660) to M.N. Views and opinions expressed are those of the authors only and do not necessarily reflect those of the European Union or the European Research Council.

Source: Apollo (University of Cambridge)

Authors: Viñas Torné, Ramon, Wiatrak, Maciej, Piran, Zoe, Fan, Shuyang, Jiang, Liangze, Teichmann, Sarah A, Nitzan, Mor, Brbić, Maria

Institutions: Hebrew University of Jerusalem, École Polytechnique Fédérale de Lausanne

Language: English

Topic: Gene Regulatory Network Analysis

Subfield: Molecular Biology

Field: Biochemistry, Genetics and Molecular Biology

Domain: Life Sciences

Open Access status: green

Funders: National Science Foundation, European Commission, Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung

Awards: G4223227197, G5802765421, Learning the Language of Cells with a Generative Multi-modal Foundation Model

OpenAlex ID: W7080188142

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