Large-scale structural and textual similarity-based mining of knowledge graph to predict drug–drug interactions

Abdelaziz, Ibrahim; Fokoue, Achille; Hassanzadeh, Oktie; Zhang, Ping; Sadoghi, Mohammad

doi:10.1016/j.websem.2017.06.002

Cited by 88 publications

(61 citation statements)

References 26 publications

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“…Signal detection (Huang et al, 2011; Gurulingappa et al, 2012; Cheng et al, 2013; Cheng and Zhao, 2014; Iyer et al, 2014; Shang et al, 2014; Cai et al, 2015; Herrero-Zazo et al, 2015; Jiang et al, 2015; Koutkias and Jaulent, 2015, 2016; Bravo et al, 2016; Eshleman and Singh, 2016; Kawazoe et al, 2016; Lowe et al, 2016; Noor et al, 2016; Abdelaziz et al, 2017; Knowledge Base Workgroup of the Observational Health Data Sciences and Informatics (OHDSI) Collaborative, 2017; Nguyen et al, 2017)…”

Section: Resultsmentioning

confidence: 99%

“…MoA identification/analysis (Huang et al, 2011; Gottlieb et al, 2012; Xu and Wang, 2013; Cai et al, 2015; Herrero-Zazo et al, 2015; Guo et al, 2016; Noor et al, 2016; Abdelaziz et al, 2017; Personeni et al, 2017; Piñero et al, 2017)…”

Section: Resultsmentioning

confidence: 99%

“…MoA identification/analysis (Lin et al, 2010; Tari et al, 2010; Huang et al, 2011; Gottlieb et al, 2012; Cai et al, 2015; Herrero-Zazo et al, 2015; Noor et al, 2016; Zhang et al, 2016; Abdelaziz et al, 2017)…”

Section: Resultsmentioning

confidence: 99%

See 2 more Smart Citations

Computational Advances in Drug Safety: Systematic and Mapping Review of Knowledge Engineering Based Approaches

et al. 2019

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Drug Safety (DS) is a domain with significant public health and social impact. Knowledge Engineering (KE) is the Computer Science discipline elaborating on methods and tools for developing “knowledge-intensive” systems, depending on a conceptual “knowledge” schema and some kind of “reasoning” process. The present systematic and mapping review aims to investigate KE-based approaches employed for DS and highlight the introduced added value as well as trends and possible gaps in the domain. Journal articles published between 2006 and 2017 were retrieved from PubMed/MEDLINE and Web of Science® (873 in total) and filtered based on a comprehensive set of inclusion/exclusion criteria. The 80 finally selected articles were reviewed on full-text, while the mapping process relied on a set of concrete criteria (concerning specific KE and DS core activities, special DS topics, employed data sources, reference ontologies/terminologies, and computational methods, etc.). The analysis results are publicly available as online interactive analytics graphs. The review clearly depicted increased use of KE approaches for DS. The collected data illustrate the use of KE for various DS aspects, such as Adverse Drug Event (ADE) information collection, detection, and assessment. Moreover, the quantified analysis of using KE for the respective DS core activities highlighted room for intensifying research on KE for ADE monitoring, prevention and reporting. Finally, the assessed use of the various data sources for DS special topics demonstrated extensive use of dominant data sources for DS surveillance, i.e., Spontaneous Reporting Systems, but also increasing interest in the use of emerging data sources, e.g., observational healthcare databases, biochemical/genetic databases, and social media. Various exemplar applications were identified with promising results, e.g., improvement in Adverse Drug Reaction (ADR) prediction, detection of drug interactions, and novel ADE profiles related with specific mechanisms of action, etc. Nevertheless, since the reviewed studies mostly concerned proof-of-concept implementations, more intense research is required to increase the maturity level that is necessary for KE approaches to reach routine DS practice. In conclusion, we argue that efficiently addressing DS data analytics and management challenges requires the introduction of high-throughput KE-based methods for effective knowledge discovery and management, resulting ultimately, in the establishment of a continuous learning DS system.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

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Computational Advances in Drug Safety: Systematic and Mapping Review of Knowledge Engineering Based Approaches

et al. 2019

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“…In addition, several similarity-based mining techniques have been applied to solve this problem. Abdelaziz et al 3 designed a large-scale similarity-based framework (Tiresias) to predict DDIs through link prediction. Tiresias was¯rst used to perform a semantic integration of various drug-related sources, and to produce a knowledge graph describing drug attributes and relations with various related entities such as enzymes, chemical structures, and pathways.…”

Section: Introductionmentioning

confidence: 99%

Extraction of drug–drug interaction using neural embedding

Wen

Ceesay

2018

J. Bioinform. Comput. Biol.

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Information on changes in a drug’s effect when taken in combination with a second drug, known as drug–drug interaction (DDI), is relevant in the pharmaceutical industry. DDIs can delay, decrease, or enhance absorption of either drug and thus decrease or increase their action or cause adverse effects. Information Extraction (IE) can be of great benefit in allowing identification and extraction of relevant information on DDIs. We here propose an approach for the extraction of DDI from text using neural word embedding to train a machine learning system. Results show that our system is competitive against other systems for the task of extracting DDIs, and that significant improvements can be achieved by learning from word features and using a deep-learning approach. Our study demonstrates that machine learning techniques such as neural networks and deep learning methods can efficiently aid in IE from text. Our proposed approach is well suited to play a significant role in future research.

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“…Vilar et al [4] proposed the similarity-based prediction method and modeled interaction profile fingerprints to predict DDIs [1]. Fokoue et al [5] developed the similarity calculation method between two drug pairs, and built a Tiresias framework of multiple drug data sources to predict the DDIs of different sources. Zhang et al [6] used three representative methods: the matrix perturbation method, the neighbor recommender method and the random walk method to predict potential DDIs by integrating chemical, biological, phenotypic, and network data.…”

Section: Introductionmentioning

confidence: 99%

Improved Label Propagation Model to Predict Drug - drug Interactions

et al. 2018

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Drug-drug interactions (DDIs) is one of the most concerned issues in drug design. Accurate prediction of potential DDIs in clinical trials can reduce the occurrence of side effects in real life of drugs. Therefore, we propose a model to predict DDIs. The model integrates several methods that can improve label propagation algorithm. Firstly, the chi-square test (CHI) method is adopted to filter or select the features that contain a large amount of information. Secondly, the sample similarity calculation method is reconstructed by label similarity and feature similarity. Then the label initialization information of unlabeled samples is constructed. Finally, we use label propagation algorithm to estimate the labels of the unlabeled drugs. The results show that the proposed model can obtain higher the area under the receiver operating characteristic curve (AUROC) and the area under the precision-recall curve (AUPR), which provides a favorable guarantee for the discovery of DDIs in the clinical stage.

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Large-scale structural and textual similarity-based mining of knowledge graph to predict drug–drug interactions

Abstract: Large-scale structural and textual similarity-based mining of knowledge graph to predict drug-drug interactions, Web Semantics: Science, Services and Agents on the World Wide Web (2017),

Cited by 88 publications

References 26 publications

Computational Advances in Drug Safety: Systematic and Mapping Review of Knowledge Engineering Based Approaches

Computational Advances in Drug Safety: Systematic and Mapping Review of Knowledge Engineering Based Approaches

Extraction of drug–drug interaction using neural embedding

Improved Label Propagation Model to Predict Drug - drug Interactions

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