We apply genetically motivated constrained tensor factorization to group pathways in a way that reflects molecular mechanism interactions.
Graph Neural Network (GNN) aggregates the neighborhood of each node into the node embedding and shows its powerful capability for graph representation learning.
Knowledge graphs are important resources for many artificial intelligence tasks but often suffer from incompleteness.
Ranked #4 on Link Prediction on FB15k-237 (MR metric)
However, most of the existing approaches for image representation ignore the relations between images and consider each input image independently.
In this paper, with the insight that the distribution in a local sample space should be simpler than that in the whole sample space, a local probabilistic model established for a local region is expected much simpler and can relax the fundamental assumptions that may not be true in the whole sample space.
Additionally, based on the local distribution, we generate a generalized local classification form that can be effectively applied to various datasets through tuning the parameters.
We developed data-driven prediction models to estimate the risk of new AKI onset.
We used the simulation data to verify the effectiveness of this method, and then we applied it to ICU mortality risk prediction and demonstrated its superiority over other conventional supervised NMF methods.
In this paper, we recognize that novel classes should be different from each other, and propose distribution networks for open set learning that can model different novel classes based on probability distributions.
However, most of the previous deep neural network classifiers were based on deterministic architectures which are usually very noise-sensitive and are likely to aggravate the overfitting issue.
We build a single text graph for a corpus based on word co-occurrence and document word relations, then learn a Text Graph Convolutional Network (Text GCN) for the corpus.
Ranked #6 on Text Classification on R52
Our system facilitates portable phenotyping of obesity and its 15 comorbidities based on the unstructured patient discharge summaries, while achieving a performance that often ranked among the top 10 of the challenge participants.
Clinical text classification is an important problem in medical natural language processing.
Ranked #2 on Clinical Note Phenotyping on I2B2 2008: Obesity