Search Results for author:
Found 5 papers, 2 papers with code
Robust Bayesian optimization with reinforcement learned acquisition functions
In Bayesian optimization (BO) for expensive black-box optimization tasks, acquisition function (AF) guides sequential sampling and plays a pivotal role for efficient convergence to better optima.
Attention-aware contrastive learning for predicting T cell receptor-antigen binding specificity
The limitation can be attributed to the binding specificity of T cell receptor (TCR) to peptide-MHC complex (pMHC).
Attention-wise masked graph contrastive learning for predicting molecular property
We developed a novel molecular graph augmentation strategy, referred to as attention-wise graph mask, to generate challenging positive sample for contrastive learning.
Contrastive learning-based computational histopathology predict differential expression of cancer driver genes
We leveraged contrastive learning on large-scale unannotated WSIs to derive slide-level histopathological feature in latent space, and then transfer it to tumor diagnosis and prediction of differentially expressed cancer driver genes.
Graph2MDA: a multi-modal variational graph embedding model for predicting microbe-drug associations
We evaluated our method on three independent datasets and the experimental results showed that our proposed method outperformed six existing state-of-the-art methods.
