Collaborative Filtering on a Family of Biological Targets

By Dumitru Erhan, Pierre-Jean L'Heureux, S. Yue, Yoshua Bengio. 2006

102 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Molecular Biology

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Citations in the corpus

Modeling Industrial ADMET Data with Multitask Networks (2016, background): “Previous attempts to quantify relatedness have focused on shared compounds or correlations between labels [Erhan et al., 2006; Ramsundar et al., 2015].”
Massively Multitask Networks for Drug Discovery (2015, background): “Another closely related work proposed the use of collaborative ﬁltering for virtual screening and employed both multitask networks and kernel-based methods (Erhan et al., 2006).”
A Systematic Prediction of Multiple Drug-Target Interactions from Chemical, Genomic, and Pharmacological Data (2012, methodology): “To overcome the shortcomings of these two approaches, several statistical models have been developed to predict the interactions, such as the binary classification models [36,37] and the supervised bipartite graph inference ones [38,39].”
Drug-target interaction prediction from chemical, genomic and pharmacological data in an integrated framework (2010, unknown): “Contact: yoshihiro.yamanishi@ensmp.fr”
Protein-ligand interaction prediction: an improved chemogenomics approach (2008, background): “The classical paradigm is to predict the modulators of a given ∗To whom correspondence should be addressed. target, considering each target independently from other proteins.”
Accelerating Drugs Discovery with Deep Reinforcement Learning: An Early Approach (2018, methodology): “They have been also successfully applied in drug discovery [6, 12, 33], including application such as QSAR [32], genomics [1, 61], and VS [8, 58].”