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Predictable Dual-View Hashing
TLDR
We propose a Predictable Dual-View Hashing (PDH) algorithm which embeds proximity of data samples in the original spaces with a notion of 'predictability'. Expand
Collective Spammer Detection in Evolving Multi-Relational Social Networks
TLDR
We model a social network as a time-stamped multi-relational graph where vertices represent users and edges represent different activities between them. Expand
Network-Based Drug-Target Interaction Prediction with Probabilistic Soft Logic
TLDR
We propose 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). Expand
HyPER: A Flexible and Extensible Probabilistic Framework for Hybrid Recommender Systems
TLDR
We propose a general hybrid recommender framework, called HyPER (HYbrid Probabilistic Extensible Recommender), which leverages the flexibility of probabilistic programming in order to build adaptable and extensible Hybrid recommender systems which reason over complex data. Expand
Bias and stability of single variable classifiers for feature ranking and selection
TLDR
We study the stability and bias of SVC feature ranking methods and report problems that affect both SVC and wrapper methods in practice. Expand
Confidence in medical decision making: application in temporal lobe epilepsy data mining
TLDR
We have 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. Expand
Drug-target interaction prediction for drug repurposing with probabilistic similarity logic
TLDR
In this paper, we propose a novel drug-target interaction prediction framework based on probabilistic similarity logic (PSL) [5]. Expand
Biomedical Named Entity Recognition via Reference-Set Augmented Bootstrapping
TLDR
We present a weakly-supervised data augmentation approach to improve Named Entity Recognition (NER) in a challenging domain: extracting biomedical entities (e.g., proteins) from the scientific literature. Expand
NSEEN: Neural Semantic Embedding for Entity Normalization
TLDR
We develop a general, scalable solution based on a deep Siamese neural network model to embed entity information that can capture syntactic variations and semantic variations in a numeric vector space. Expand
A probabilistic approach for collective similarity-based drug-drug interaction prediction
TLDR
We propose a probabilistic approach for jointly inferring unknown DDIs from a network of multiple drug-based similarities and known interactions. Expand
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