Search Results for author:
Found 6 papers, 3 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.
Question-Context Alignment and Answer-Context Dependencies for Effective Answer Sentence Selection
In this paper, we propose to improve the candidate scoring by explicitly incorporating the dependencies between question-context and answer-context into the final representation of a candidate.
Predicting Biomedical Interactions with Probabilistic Model Selection for Graph Neural Networks
Experiments on four interaction datasets show that our proposed method achieves accurate and calibrated predictions.
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.
GNE: a deep learning framework for gene network inference by aggregating biological information
However, it is still a challenging task to aggregate heterogeneous biological information such as gene expression and gene interactions to achieve more accurate inference for prediction and discovery of new gene interactions.
Ranked #1 on
Gene Interaction Prediction
on BioGRID(yeast)
(using extra training data)
