2014
DOI: 10.1186/1471-2105-15-267
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A phenome-guided drug repositioning through a latent variable model

Abstract: BackgroundThe phenome represents a distinct set of information in the human population. It has been explored particularly in its relationship with the genome to identify correlations for diseases. The phenome has been also explored for drug repositioning with efforts focusing on the search space for the most similar candidate drugs. For a comprehensive analysis of the phenome, we assumed that all phenotypes (indications and side effects) were inter-connected with a probabilistic distribution and this character… Show more

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Cited by 38 publications
(23 citation statements)
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“…Many studies have analyzed either molecular-level states induced by disease or drugs, or phenotypic profiling from human individuals with the goal of drug repositioning. [9][10][11] For example, the Connectivity Map elucidates relationships between small molecule drugs and diseases. 12 In previous works, we proposed a method based on a guilt-byassociation approach to predict new ones 13 and integrated clinical phenotypes from electronic medical records.…”
Section: Study Highlightsmentioning
confidence: 99%
See 1 more Smart Citation
“…Many studies have analyzed either molecular-level states induced by disease or drugs, or phenotypic profiling from human individuals with the goal of drug repositioning. [9][10][11] For example, the Connectivity Map elucidates relationships between small molecule drugs and diseases. 12 In previous works, we proposed a method based on a guilt-byassociation approach to predict new ones 13 and integrated clinical phenotypes from electronic medical records.…”
Section: Study Highlightsmentioning
confidence: 99%
“…Many studies have analyzed either molecular‐level states induced by disease or drugs, or phenotypic profiling from human individuals with the goal of drug repositioning . For example, the Connectivity Map elucidates relationships between small molecule drugs and diseases .…”
mentioning
confidence: 99%
“…In recent years, different approaches were exploited for repurposing drugs, including network, text mining, machine learning and semantic inference based approaches. Recently, networkbased approach attracted more attention and was widely used in computational drug repositioning due to the capability of using ever-increasing large scale biological datasets such as genetic, pharmacogenomics, clinical and chemical data [2,5,[7][8][9][10][11][12][13][14].…”
Section: Introductionmentioning
confidence: 99%
“…We use Latent Dirichlet Allocation (LDA) [15], an unsupervised probabilistic method of learning topics associated with text. In the biomedical space, LDA has been used to analyze electronic health records [16,17], drug labels [1], and adverse event data [18].…”
Section: Introductionmentioning
confidence: 99%