In this paper, we propose a new peptide data augmentation scheme, where we train peptide language models on artificially constructed peptides that are small contiguous subsets of longer, wild-type proteins; we refer to the training peptides as "chopped proteins".
Predicting the responses of a cell under perturbations may bring important benefits to drug discovery and personalized therapeutics.
Estimating an individual's potential outcomes under counterfactual treatments is a challenging task for traditional causal inference and supervised learning approaches when the outcome is high-dimensional (e. g. gene expressions, impulse responses, human faces) and covariates are relatively limited.
Image-based characterization and disease understanding involve integrative analysis of morphological, spatial, and topological information across biological scales.
Unlike conventional graph convolution networks always assuming the same node attributes in a global graph, our approach models interdomain information fusion with bipartite graph convolution operation.
In this work, we investigate semi-supervised learning (SSL) for image classification using adversarial training.
Recently, semi-supervised learning methods based on generative adversarial networks (GANs) have received much attention.