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Predictable Dual-View Hashing
We propose a Predictable Dual-View Hashing (PDH) algorithm which embeds proximity of data samples in the original spaces. We create a cross-view hamming space with the ability to compare information
Collective Spammer Detection in Evolving Multi-Relational Social Networks
TLDR
To identify spammer accounts, the approach makes use of structural features, sequence modelling, and collective reasoning, and a statistical relational model using hinge-loss Markov random fields (HL-MRFs), a class of probabilistic graphical models which are highly scalable.
Network-Based Drug-Target Interaction Prediction with Probabilistic Soft Logic
TLDR
This work proposes a prediction framework that represents the problem using a bipartite graph of drug-target interactions augmented with drug-drug and target-target similarity measures and makes predictions using probabilistic soft logic (PSL).
HyPER: A Flexible and Extensible Probabilistic Framework for Hybrid Recommender Systems
TLDR
This paper shows how a recently introduced statistical relational learning framework can be used to develop a generic and extensible hybrid recommender system, HyPER (HYbrid Probabilistic Extensible Recommender), which incorporates and reasons over a wide range of information sources.
NSEEN: Neural Semantic Embedding for Entity Normalization
TLDR
A general, scalable solution based on a deep Siamese neural network model to embed the semantic information about the entities, as well as their syntactic variations, which is used for fast mapping of new entities to large reference sets, and empirically shows the effectiveness of the framework in challenging bio-entity normalization datasets.
Confidence in medical decision making: application in temporal lobe epilepsy data mining
TLDR
It is shown that in critical domains such as medicine, use of AUC does not provide sufficient information about the confidence of the classification and further measures are needed.
Drug-target interaction prediction for drug repurposing with probabilistic similarity logic
TLDR
A novel drug-target interaction prediction framework based on probabilistic similarity logic (PSL), where rules describing link predictions based on triads and tetrads can effectively make use of a variety of similarity measures is proposed.
Biomedical Named Entity Recognition via Reference-Set Augmented Bootstrapping
TLDR
A weakly-supervised data augmentation approach to improve Named Entity Recognition (NER) in a challenging domain: extracting biomedical entities from the scientific literature.
A probabilistic approach for collective similarity-based drug-drug interaction prediction
TLDR
A probabilistic approach for jointly inferring unknown DDIs from a network of multiple drug-based similarities and known interactions is proposed and five novel interactions validated by external sources are found among the top-ranked predictions of this model.
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