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Drug Repositioning

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Revealing new therapeutic opportunities through drug target prediction: a class imbalance-tolerant machine learning approach.

Bioinformatics (Oxford, England)
MOTIVATION: In silico drug target prediction provides valuable information for drug repurposing, understanding of side effects as well as expansion of the druggable genome. In particular, discovery of actionable drug targets is critical to developing...

deepDR: a network-based deep learning approach to in silico drug repositioning.

Bioinformatics (Oxford, England)
MOTIVATION: Traditional drug discovery and development are often time-consuming and high risk. Repurposing/repositioning of approved drugs offers a relatively low-cost and high-efficiency approach toward rapid development of efficacious treatments. T...

[Artificial Intelligence-based Drug Discovery and Drug Repositioning].

Brain and nerve = Shinkei kenkyu no shinpo
The methodologies of computational drug discovery and drug repositioning (DR) based on biomolecular profile information are reviewed systematically. For big data drug discovery and DR, 1) methods of comparing gene expression profiles of the diseased ...

Drug knowledge bases and their applications in biomedical informatics research.

Briefings in bioinformatics
Recent advances in biomedical research have generated a large volume of drug-related data. To effectively handle this flood of data, many initiatives have been taken to help researchers make good use of them. As the results of these initiatives, many...

GOF/LOF knowledge inference with tensor decomposition in support of high order link discovery for gene, mutation and disease.

Mathematical biosciences and engineering : MBE
For discovery of new usage of drugs, the function type of their target genes plays an important role, and the hypothesis of "Antagonist-GOF" and "Agonist-LOF" has laid a solid foundation for supporting drug repurposing. In this research, an active ge...

Tripartite Network-Based Repurposing Method Using Deep Learning to Compute Similarities for Drug-Target Prediction.

Methods in molecular biology (Clifton, N.J.)
The drug discovery process is conventionally regarded as resource intensive and complex. Therefore, research effort has been put into a process called drug repositioning with the use of computational methods. Similarity-based methods are common in pr...

A Drug-Target Network-Based Supervised Machine Learning Repurposing Method Allowing the Use of Multiple Heterogeneous Information Sources.

Methods in molecular biology (Clifton, N.J.)
Drug-target networks have an important role in pharmaceutical innovation, drug lead discovery, and recent drug repositioning tasks. Many different in silico approaches for the identification of new drug-target interactions have been proposed, many of...

Machine Learning Approach for Predicting New Uses of Existing Drugs and Evaluation of Their Reliabilities.

Methods in molecular biology (Clifton, N.J.)
In this chapter, a new method to evaluate the reliability of predicting new uses of existing drugs was proposed. The prediction was performed with a support vector machine (SVM) using various data. Because the reliability of prediction could not be e...

A Machine-Learning-Based Drug Repurposing Approach Using Baseline Regularization.

Methods in molecular biology (Clifton, N.J.)
We present the baseline regularization model for computational drug repurposing using electronic health records (EHRs). In EHRs, drug prescriptions of various drugs are recorded throughout time for various patients. In the same time, numeric physical...