14 citations · 31 across the 6 of their papers we have counts for
6 papers
CytoImageNet: A large-scale pretraining dataset for bioimage transfer learning
Stanley Bryan Z. Hua, Alex X. Lu, Alan M. Moses
Motivation: In recent years, image-based biological assays have steadily become high-throughput, sparking a need for fast automated methods to extract biologically-meaningful infor…
Random Embeddings and Linear Regression can Predict Protein Function
Tianyu Lu, Alex X. Lu, Alan M. Moses
Large self-supervised models pretrained on millions of protein sequences have recently gained popularity in generating embeddings of protein sequences for protein function predicti…
Evolution Is All You Need: Phylogenetic Augmentation for Contrastive Learning
Amy X. Lu, Alex X. Lu, Alan Moses
Self-supervised representation learning of biological sequence embeddings alleviates computational resource constraints on downstream tasks while circumventing expensive experiment…
W-Cell-Net: Multi-frame Interpolation of Cellular Microscopy Videos
Rohit Saha, Abenezer Teklemariam, Ian Hsu +1
Deep Neural Networks are increasingly used in video frame interpolation tasks such as frame rate changes as well as generating fake face videos. Our project aims to apply recent ad…
The Cells Out of Sample (COOS) dataset and benchmarks for measuring out-of-sample generalization of image classifiers
Alex X. Lu, Amy X. Lu, Wiebke Schormann +3
Understanding if classifiers generalize to out-of-sample datasets is a central problem in machine learning. Microscopy images provide a standardized way to measure the generalizati…
Towards a theoretical understanding of false positives in DNA motif finding
Amin Zia, Alan M. Moses
Detection of false-positive motifs is one of the main causes of low performance in motif finding methods. It is generally assumed that false-positives are mostly due to algorithmic…