Statistical relational learning with soft quantifiers

Quantification in statistical relational learning (SRL) is either existential or universal, however humans might be more inclined to express knowledge using soft quantifiers, such as ``most'' and ``a few''. In this paper, we define the syntax and semantics of PSL^Q, a new SRL framework that supports reasoning with soft quantifiers, and present its most probable explanation (MPE) inference algorithm. To the best of our knowledge, PSL^Q is the first SRL framework that combines soft quantifiers with first-order logic rules for modelling uncertain relational data. Our experimental results for link prediction in social trust networks demonstrate that the use of soft quantifiers not only allows for a natural and intuitive formulation of domain knowledge, but also improves the accuracy of inferred results

Predication of Drug Target Interaction using Reliable Multicast Routing in Wireless Ad hoc Molecular Network

Wireless Ad hoc Molecular network consists of atoms in terms of nodes in the absence of administrative point. In this connection, there is a need to adopt molecular analogy to define the architecture. Prediction of Drug Target Interaction (DTI) is a major impact in molecular ad hoc network. Efforts were made to combine such information with data to define DTI and to construct biological space. The concept of Conditional Random Field (CRF) is used in our proposed reliable multicast routing to integrate genomic, chemical and functional data to predict DTI. Reliability of links was also maintained to increase the network performance. Based on the extensive simulation results, the proposed reliable routing protocol achieves better performance than existing schemes

Learning to Make Predictions on Graphs with Autoencoders

We examine two fundamental tasks associated with graph representation learning: link prediction and semi-supervised node classification. We present a novel autoencoder architecture capable of learning a joint representation of both local graph structure and available node features for the multi-task learning of link prediction and node classification. Our autoencoder architecture is efficiently trained end-to-end in a single learning stage to simultaneously perform link prediction and node classification, whereas previous related methods require multiple training steps that are difficult to optimize. We provide a comprehensive empirical evaluation of our models on nine benchmark graph-structured datasets and demonstrate significant improvement over related methods for graph representation learning. Reference code and data are available at https://github.com/vuptran/graph-representation-learningComment: Published as a conference paper at IEEE DSAA 201

Soft quantification in statistical relational learning

We present a new statistical relational learning (SRL) framework that supports reasoning with soft quantifiers, such as "most" and "a few." We define the syntax and the semantics of this language, which we call , and present a most probable explanation inference algorithm for it. To the best of our knowledge, is the first SRL framework that combines soft quantifiers with first-order logic rules for modelling uncertain relational data. Our experimental results for two real-world applications, link prediction in social trust networks and user profiling in social networks, demonstrate that the use of soft quantifiers not only allows for a natural and intuitive formulation of domain knowledge, but also improves inference accuracy

A collective, probabilistic approach to schema mapping using diverse noisy evidence

We propose a probabilistic approach to the problem of schema mapping. Our approach is declarative, scalable, and extensible. It builds upon recent results in both schema mapping and probabilistic reasoning and contributes novel techniques in both fields. We introduce the problem of schema mapping selection, that is, choosing the best mapping from a space of potential mappings, given both metadata constraints and a data example. As selection has to reason holistically about the inputs and the dependencies between the chosen mappings, we define a new schema mapping optimization problem which captures interactions between mappings as well as inconsistencies and incompleteness in the input. We then introduce Collective Mapping Discovery (CMD), our solution to this problem using state-of-the-art probabilistic reasoning techniques. Our evaluation on a wide range of integration scenarios, including several real-world domains, demonstrates that CMD effectively combines data and metadata information to infer highly accurate mappings even with significant levels of noise

Repurposing Market Drugs to Target Epigenetic Enzymes in Human Diseases

Drug discovery is an exciting yet highly costly endeavor. In the United States, developing a new prescription medicine that gains marketing approval takes near a decade and costs drugmakers for near 3 billion. More challengingly, the success rate of a compound entering phase I trials is just slightly under 10%. Because of these mounting hurdles, repurposing market approved drugs to new clinical indications has been a new trend on the rise. Another merit to this approach is the already confirmed toxicity profiles of the drugs and their possession of drug-like features. Thus, repurposed drugs can reach the market approved stage in a much faster, cheaper, and more efficient way. Notably, epigenetic enzymes play a critical role in the etiology and progression of different diseases. Researchers are now assessing the possibilities of using market approved drugs to target epigenetic enzymes as a novel strategy to curtail disease progression. Thus, in this book chapter, we will provide an outlook on repurposing market drugs to target epigenetic enzymes in various diseases. Consequently, this book chapter will not only provide the readers with current knowledge in this specific field, but also will shed light on the pathway forward for repurposing market drugs to target epigenetic enzymes in human diseases