6 papers · 1 filter
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…
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…
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…
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…
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…
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…