Learning Causal Representations of Single Cells via Sparse Mechanism
Shift Modeling

Learning Causal Representations of Single Cells via Sparse Mechanism Shift Modeling

7 November 2022

Natavsa Tagasovska

Papers citing "Learning Causal Representations of Single Cells via Sparse Mechanism Shift Modeling"

19 / 19 papers shown

Title
In-silico biological discovery with large perturbation models Djordje Miladinovic Tobias Hoppe Mathieu Chevalley Andreas Georgiou Lachlan Stuart Arash Mehrjou M. Bantscheff Bernhard Schölkopf Patrick Schwab 34 0 0 30 Mar 2025
Efficient Data Selection for Training Genomic Perturbation Models G. Panagopoulos J. Lutzeyer Sofiane Ennadir Michalis Vazirgiannis Jun Pang 66 0 0 18 Mar 2025
Sanity Checking Causal Representation Learning on a Simple Real-World System Juan L. Gamella Simon Bing Jakob Runge CML 50 0 0 27 Feb 2025
No Foundations without Foundations -- Why semi-mechanistic models are essential for regulatory biology Luka Kovacevic Thomas Gaudelet James Opzoomer Hagen Triendl John Whittaker Caroline Uhler Lindsay Edwards J. Taylor-King AI4CE 62 0 0 31 Jan 2025
GPO-VAE: Modeling Explainable Gene Perturbation Responses utilizing GRN-Aligned Parameter Optimization Seungheun Baek Soyon Park Y. T. Chok Mogan Gim Jaewoo Kang DRL 40 0 0 31 Jan 2025
Efficient Fine-Tuning of Single-Cell Foundation Models Enables Zero-Shot Molecular Perturbation Prediction Sepideh Maleki Jan-Christian Huetter Kangway V Chuang Gabriele Scalia Tommaso Biancalani Tommaso Biancalani AI4CE 85 2 0 18 Dec 2024
Learning Identifiable Factorized Causal Representations of Cellular Responses Haiyi Mao Romain Lopez Kai Liu Jan-Christian Huetter David Richmond Panayiotis Benos Lin Qiu CML 22 3 0 29 Oct 2024
Automated Discovery of Pairwise Interactions from Unstructured Data Zuheng Xu Moksh Jain Ali Denton Shawn Whitfield Aniket Didolkar Berton A. Earnshaw Jason S. Hartford 16 2 0 11 Sep 2024
CRADLE-VAE: Enhancing Single-Cell Gene Perturbation Modeling with Counterfactual Reasoning-based Artifact Disentanglement Seungheun Baek Soyon Park Y. T. Chok Junhyun Lee Jueon Park Mogan Gim Jaewoo Kang CML 30 1 0 09 Sep 2024
PerturBench: Benchmarking Machine Learning Models for Cellular Perturbation Analysis Yan Wu Esther Wershof Sebastian M. Schmon Marcel Nassar Błażej Osiński Ridvan Eksi Kun Zhang T. Graepel 19 6 0 20 Aug 2024
sc-OTGM: Single-Cell Perturbation Modeling by Solving Optimal Mass Transport on the Manifold of Gaussian Mixtures Andac Demir E. Solovyeva James Boylan Mei Xiao Fabrizio Serluca S. Hoersch Jeremy L Jenkins Murthy S. Devarakonda B. Kiziltan 19 0 0 06 May 2024
Toward the Identifiability of Comparative Deep Generative Models Romain Lopez Jan-Christian Huetter Ehsan Hajiramezanali Jonathan Pritchard Aviv Regev 13 2 0 29 Jan 2024
Invariance & Causal Representation Learning: Prospects and Limitations Simon Bing Jonas Wahl Urmi Ninad Jakob Runge CML OOD 23 3 0 06 Dec 2023
Learning Causal Representations from General Environments: Identifiability and Intrinsic Ambiguity Jikai Jin Vasilis Syrgkanis CML 17 5 0 21 Nov 2023
Modelling Cellular Perturbations with the Sparse Additive Mechanism Shift Variational Autoencoder Michael D. Bereket Theofanis Karaletsos DRL 14 18 0 05 Nov 2023
Conditionally Invariant Representation Learning for Disentangling Cellular Heterogeneity H. Aliee Ferdinand Kapl Soroor Hediyeh-zadeh Fabian J. Theis CML 13 6 0 02 Jul 2023
Out-of-Variable Generalization for Discriminative Models Siyuan Guo J. Wildberger Bernhard Schölkopf OOD CML 7 2 0 16 Apr 2023
Unpaired Multi-Domain Causal Representation Learning Nils Sturma C. Squires Mathias Drton Caroline Uhler OOD CML 13 20 0 02 Feb 2023
Interventional Causal Representation Learning Kartik Ahuja Divyat Mahajan Yixin Wang Yoshua Bengio CML 26 83 0 24 Sep 2022