Learning effective recommendation models from sparse user interactions
r...
Retrosynthesis is the process of determining the set of reactant molecul...
Ligand-based drug design aims to identify novel drug candidates of simil...
Personalized cancer treatment requires a thorough understanding of compl...
T cells monitor the health status of cells by identifying foreign peptid...
Sequential recommendation aims to recommend the next item of users' inte...
Retrosynthesis is a procedure where a molecule is transformed into poten...
Session-based recommendation aims to generate recommendations for the ne...
Recent advances in molecular machine learning, especially deep neural
ne...
Collaborative recommendation approaches based on nearest-neighbors are s...
In drug discovery, molecule optimization is an important step in order t...
T-cell receptors can recognize foreign peptides bound to major
histocomp...
Background: Breast implants have been increasingly popular over the last...
Due to the rapid growth of information available about individual patien...
With increasing and extensive use of electronic health records, clinicia...
Next-basket recommendation considers the problem of recommending a set o...
We developed a hybrid associations model (HAM) to generate sequential
re...
Knowledge graph learning plays a critical role in integrating domain spe...
Background: The problem of predicting whether a drug combination of arbi...
Adverse drug reactions (ADRs) induced from high-order drug-drug interact...
Selecting the right drugs for the right patients is a primary goal of
pr...
The past decade has seen a growth in the development and deployment of
e...
Link prediction, or predicting the likelihood of a link in a knowledge g...