11 citations · 13 across the 8 of their papers we have counts for
8 papers
Elucidating the Design Space of Flow Matching for Cellular Microscopy
Charles Jones, Emmanuel Noutahi, Jason Hartford +1
Flow-matching generative models are increasingly used to simulate cell responses to biological perturbations. However, the design space for building such models is large and undere…
Conditional Chemical Language Models are Versatile Tools in Drug Discovery
Lu Zhu, Emmanuel Noutahi
Generative chemical language models (CLMs) have demonstrated strong capabilities in molecular design, yet their impact in drug discovery remains limited by the absence of reliable…
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…
Graph and Simplicial Complex Prediction Gaussian Process via the Hodgelet Representations
Mathieu Alain, So Takao, Xiaowen Dong +2
Predicting the labels of graph-structured data is crucial in scientific applications and is often achieved using graph neural networks (GNNs). However, when data is scarce, GNNs su…
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…
SAFE setup for generative molecular design
Yassir El Mesbahi, Emmanuel Noutahi
SMILES-based molecular generative models have been pivotal in drug design but face challenges in fragment-constrained tasks. To address this, the Sequential Attachment-based Fragme…
Graph Classification Gaussian Processes via Hodgelet Spectral Features
Mathieu Alain, So Takao, Xiaowen Dong +2
The problem of classifying graphs is ubiquitous in machine learning. While it is standard to apply graph neural networks or graph kernel methods, Gaussian processes can be employed…