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20222026
most citedBenchmarking Transcriptomics Foundation Models for Perturbation Analysis : one PCA still rules them all

11 citations · 30 across the 12 of their papers we have counts for

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6 papers · 1 filter

cs.LG2026

Effective Biological Representation Learning by Masking Gene Expression

Kian Kenyon-Dean, Alina Selega, Ihab Bendidi +5

RNA sequencing produces rich and diverse datasets of gene expression, offering compelling insights into cellular state and function that have many applications in drug discovery. M…

cs.LG2026

XFACTORS: Disentangled Information Bottleneck via Contrastive Supervision

Alexandre Myara, Nicolas Bourriez, Thomas Boyer +3

Disentangled representation learning aims to map independent factors of variation to independent representation components. On one hand, purely unsupervised approaches have proven…

cs.LG2025

A Cross Modal Knowledge Distillation & Data Augmentation Recipe for Improving Transcriptomics Representations through Morphological Features

Ihab Bendidi, Yassir El Mesbahi, Alisandra K. Denton +4

Understanding cellular responses to stimuli is crucial for biological discovery and drug development. Transcriptomics provides interpretable, gene-level insights, while microscopy…

cs.LG2025★ 2 cited

TxPert: Leveraging Biochemical Relationships for Out-of-Distribution Transcriptomic Perturbation Prediction

Frederik Wenkel, Wilson Tu, Cassandra Masschelein +12

Accurately predicting cellular responses to genetic perturbations is essential for understanding disease mechanisms and designing effective therapies. Yet exhaustively exploring th…

cs.LG2024★ 11 cited

Benchmarking Transcriptomics Foundation Models for Perturbation Analysis : one PCA still rules them all

Ihab Bendidi, Shawn Whitfield, Kian Kenyon-Dean +4

Understanding the relationships among genes, compounds, and their interactions in living organisms remains limited due to technological constraints and the complexity of biological…

cs.LG2024★ 3 cited

ViTally Consistent: Scaling Biological Representation Learning for Cell Microscopy

Kian Kenyon-Dean, Zitong Jerry Wang, John Urbanik +10

Large-scale cell microscopy screens are used in drug discovery and molecular biology research to study the effects of millions of chemical and genetic perturbations on cells. To us…