36 citations · 76 across the 13 of their papers we have counts for
14 papers
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…
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…
RxRx3-core: Benchmarking drug-target interactions in High-Content Microscopy
Oren Kraus, Federico Comitani, John Urbanik +6
High Content Screening (HCS) microscopy datasets have transformed the ability to profile cellular responses to genetic and chemical perturbations, enabling cell-based inference of…
Towards scientific discovery with dictionary learning: Extracting biological concepts from microscopy foundation models
Konstantin Donhauser, Kristina Ulicna, Gemma Elyse Moran +4
Sparse dictionary learning (DL) has emerged as a powerful approach to extract semantically meaningful concepts from the internals of large language models (LLMs) trained mainly in…
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…
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…
Masked Autoencoders for Microscopy are Scalable Learners of Cellular Biology
Oren Kraus, Kian Kenyon-Dean, Saber Saberian +14
Featurizing microscopy images for use in biological research remains a significant challenge, especially for large-scale experiments spanning millions of images. This work explores…
Masked Autoencoders are Scalable Learners of Cellular Morphology
Oren Kraus, Kian Kenyon-Dean, Saber Saberian +13
Inferring biological relationships from cellular phenotypes in high-content microscopy screens provides significant opportunity and challenge in biological research. Prior results…
Deconstructing word embedding algorithms
Kian Kenyon-Dean, Edward Newell, Jackie Chi Kit Cheung
Word embeddings are reliable feature representations of words used to obtain high quality results for various NLP applications. Uncontextualized word embeddings are used in many NL…
Learning Efficient Task-Specific Meta-Embeddings with Word Prisms
Jingyi He, KC Tsiolis, Kian Kenyon-Dean +1
Word embeddings are trained to predict word cooccurrence statistics, which leads them to possess different lexical properties (syntactic, semantic, etc.) depending on the notion of…
Deconstructing and reconstructing word embedding algorithms
Edward Newell, Kian Kenyon-Dean, Jackie Chi Kit Cheung
Uncontextualized word embeddings are reliable feature representations of words used to obtain high quality results for various NLP applications. Given the historical success of wor…
Word Embedding Algorithms as Generalized Low Rank Models and their Canonical Form
Kian Kenyon-Dean
Word embedding algorithms produce very reliable feature representations of words that are used by neural network models across a constantly growing multitude of NLP tasks. As such,…
Clustering-Oriented Representation Learning with Attractive-Repulsive Loss
Kian Kenyon-Dean, Andre Cianflone, Lucas Page-Caccia +3
The standard loss function used to train neural network classifiers, categorical cross-entropy (CCE), seeks to maximize accuracy on the training data; building useful representatio…
Resolving Event Coreference with Supervised Representation Learning and Clustering-Oriented Regularization
Kian Kenyon-Dean, Jackie Chi Kit Cheung, Doina Precup
We present an approach to event coreference resolution by developing a general framework for clustering that uses supervised representation learning. We propose a neural network ar…