Systematic identification of pharmacogenomics information from clinical trials

Li, Jiao; Lu, Zhiyong

doi:10.1016/j.jbi.2012.04.005

Cited by 27 publications

(17 citation statements)

References 30 publications

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“…In contrast, the work described here uses relatively small clinical trial data on ClinicalTrials.gov, which has been proved useful in other works to identify combination therapy (Wu et al, 2015) and pharmacogenomics information (Li & Lu, 2012). The algorithm presented here is simple and direct.…”

Section: Discussionmentioning

confidence: 99%

Systematic drug repositioning through mining adverse event data in ClinicalTrials.gov

Sanger

2017

PeerJ

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Drug repositioning (i.e., drug repurposing) is the process of discovering new uses for marketed drugs. Historically, such discoveries were serendipitous. However, the rapid growth in electronic clinical data and text mining tools makes it feasible to systematically identify drugs with the potential to be repurposed. Described here is a novel method of drug repositioning by mining ClinicalTrials.gov. The text mining tools I2E (Linguamatics) and PolyAnalyst (Megaputer) were utilized. An I2E query extracts “Serious Adverse Events” (SAE) data from randomized trials in ClinicalTrials.gov. Through a statistical algorithm, a PolyAnalyst workflow ranks the drugs where the treatment arm has fewer predefined SAEs than the control arm, indicating that potentially the drug is reducing the level of SAE. Hypotheses could then be generated for the new use of these drugs based on the predefined SAE that is indicative of disease (for example, cancer).

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Section: Discussionmentioning

confidence: 99%

Systematic drug repositioning through mining adverse event data in ClinicalTrials.gov

Sanger

2017

PeerJ

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“…The meaning of unrecognized words had to be inferred by the InfoCodex engine based only on its universal internal linguistic database. Third, the text mining algorithms used here do not use rule-based approaches [31], or analyze co-occurrences sentence by sentence [29] or section by section [32], but rather they extract knowledge from entire documents and their relations with semantically related documents.…”

Section: Discussionmentioning

confidence: 99%

Discovery of novel biomarkers and phenotypes by semantic technologies

Trugenberger¹,

Wälti²,

Peregrim

et al. 2013

BMC Bioinformatics

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BackgroundBiomarkers and target-specific phenotypes are important to targeted drug design and individualized medicine, thus constituting an important aspect of modern pharmaceutical research and development. More and more, the discovery of relevant biomarkers is aided by in silico techniques based on applying data mining and computational chemistry on large molecular databases. However, there is an even larger source of valuable information available that can potentially be tapped for such discoveries: repositories constituted by research documents.ResultsThis paper reports on a pilot experiment to discover potential novel biomarkers and phenotypes for diabetes and obesity by self-organized text mining of about 120,000 PubMed abstracts, public clinical trial summaries, and internal Merck research documents. These documents were directly analyzed by the InfoCodex semantic engine, without prior human manipulations such as parsing. Recall and precision against established, but different benchmarks lie in ranges up to 30% and 50% respectively. Retrieval of known entities missed by other traditional approaches could be demonstrated. Finally, the InfoCodex semantic engine was shown to discover new diabetes and obesity biomarkers and phenotypes. Amongst these were many interesting candidates with a high potential, although noticeable noise (uninteresting or obvious terms) was generated.ConclusionsThe reported approach of employing autonomous self-organising semantic engines to aid biomarker discovery, supplemented by appropriate manual curation processes, shows promise and has potential to impact, conservatively, a faster alternative to vocabulary processes dependent on humans having to read and analyze all the texts. More optimistically, it could impact pharmaceutical research, for example to shorten time-to-market of novel drugs, or speed up early recognition of dead ends and adverse reactions.

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“…Most machine learning-based methods take randomly generated drug-disease associations as negative samples, in which some false negatives are included and lead to biased decision boundary [7, 11]. The literature mining methods depend on term co-occurrence and sematic inference of some keywords of interest to infer new drug-disease associations [10, 12]. Due to the ambiguity in nature of natural language and limited accuracy of text mining techniques, literature mining-based methods do not obtain desirable performance.…”

Section: Introductionmentioning

confidence: 99%

Inferring new indications for approved drugs via random walk on drug-disease heterogenous networks

et al. 2016

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BackgroundSince traditional drug research and development is often time-consuming and high-risk, there is an increasing interest in establishing new medical indications for approved drugs, referred to as drug repositioning, which provides a relatively low-cost and high-efficiency approach for drug discovery. With the explosive growth of large-scale biochemical and phenotypic data, drug repositioning holds great potential for precision medicine in the post-genomic era. It is urgent to develop rational and systematic approaches to predict new indications for approved drugs on a large scale.ResultsIn this paper, we propose the two-pass random walks with restart on a heterogenous network, TP-NRWRH for short, to predict new indications for approved drugs. Rather than random walk on bipartite network, we integrated the drug-drug similarity network, disease-disease similarity network and known drug-disease association network into one heterogenous network, on which the two-pass random walks with restart is implemented. We have conducted performance evaluation on two datasets of drug-disease associations, and the results show that our method has higher performance than six existing methods. A case study on the Alzheimer’s disease showed that nine of top 10 predicted drugs have been approved or investigational for neurodegenerative diseases. The experimental results show that our method achieves state-of-the-art performance in predicting new indications for approved drugs.ConclusionsWe proposed a two-pass random walk with restart on the drug-disease heterogeneous network, referred to as TP-NRWRH, to predict new indications for approved drugs. Performance evaluation on two independent datasets showed that TP-NRWRH achieved higher performance than six existing methods on 10-fold cross validations. The case study on the Alzheimer’s disease showed that nine of top 10 predicted drugs have been approved or are investigational for neurodegenerative diseases. The results show that our method achieves state-of-the-art performance in predicting new indications for approved drugs.

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Systematic identification of pharmacogenomics information from clinical trials

Cited by 27 publications

References 30 publications

Systematic drug repositioning through mining adverse event data in ClinicalTrials.gov

Systematic drug repositioning through mining adverse event data in ClinicalTrials.gov

Discovery of novel biomarkers and phenotypes by semantic technologies

Inferring new indications for approved drugs via random walk on drug-disease heterogenous networks

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