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Found 7 papers, 3 papers with code
Systemic Biases in Sign Language AI Research: A Deaf-Led Call to Reevaluate Research Agendas
Growing research in sign language recognition, generation, and translation AI has been accompanied by calls for ethical development of such technologies.
Protein structure generation via folding diffusion
The ability to computationally generate novel yet physically foldable protein structures could lead to new biological discoveries and new treatments targeting yet incurable diseases.
CytoImageNet: A large-scale pretraining dataset for bioimage transfer learning
Motivation: In recent years, image-based biological assays have steadily become high-throughput, sparking a need for fast automated methods to extract biologically-meaningful information from hundreds of thousands of images.
Ranked #1 on
BBBC021 NSC Accuracy
on CytoImageNet
Mol2Image: Improved Conditional Flow Models for Molecule to Image Synthesis
In this paper, we aim to synthesize cell microscopy images under different molecular interventions, motivated by practical applications to drug development.
Random Embeddings and Linear Regression can Predict Protein Function
Large self-supervised models pretrained on millions of protein sequences have recently gained popularity in generating embeddings of protein sequences for protein function prediction.
Evolution Is All You Need: Phylogenetic Augmentation for Contrastive Learning
Self-supervised representation learning of biological sequence embeddings alleviates computational resource constraints on downstream tasks while circumventing expensive experimental label acquisition.
The Cells Out of Sample (COOS) dataset and benchmarks for measuring out-of-sample generalization of image classifiers
Understanding if classifiers generalize to out-of-sample datasets is a central problem in machine learning.
