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Found 10 papers, 2 papers with code
Sparse Covariance Modeling in High Dimensions with Gaussian Processes
This paper studies statistical relationships among components of high-dimensional observations varying across non-random covariates.
Dynamic Fusion of Eye Movement Data and Verbal Narrations in Knowledge-rich Domains
We propose to jointly analyze experts' eye movements and verbal narrations to discover important and interpretable knowledge patterns to better understand their decision-making processes.
Predicting Biomedical Interactions with Higher-Order Graph Convolutional Networks
Recently, graph neural networks have been proposed to effectively learn representations for biomedical entities and achieved state-of-the-art results in biomedical interaction prediction.
Ranked #1 on
Link Prediction
on Drug-target interactions
Interpretable Structured Learning with Sparse Gated Sequence Encoder for Protein-Protein Interaction Prediction
Although various deep learning models in Siamese architecture have been proposed to model PPIs from sequences, these methods are computationally expensive for a large number of PPIs due to the pairwise encoding process.
