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Niu D, Zhang L, Zhang B, Zhang Q, Li Z. DAS-DDI: A dual-view framework with drug association and drug structure for drug-drug interaction prediction. J Biomed Inform 2024; 156:104672. [PMID: 38857738 DOI: 10.1016/j.jbi.2024.104672] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2024] [Revised: 05/09/2024] [Accepted: 06/06/2024] [Indexed: 06/12/2024]
Abstract
In drug development and clinical application, drug-drug interaction (DDI) prediction is crucial for patient safety and therapeutic efficacy. However, traditional methods for DDI prediction often overlook the structural features of drugs and the complex interrelationships between them, which affect the accuracy and interpretability of the model. In this paper, a novel dual-view DDI prediction framework, DAS-DDI is proposed. Firstly, a drug association network is constructed based on similarity information among drugs, which could provide rich context information for DDI prediction. Subsequently, a novel drug substructure extraction method is proposed, which could update the features of nodes and chemical bonds simultaneously, improving the comprehensiveness of the feature. Furthermore, an attention mechanism is employed to fuse multiple drug embeddings from different views dynamically, enhancing the discriminative ability of the model in handling multi-view data. Comparative experiments on three public datasets demonstrate the superiority of DAS-DDI compared with other state-of-the-art models under two scenarios.
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Affiliation(s)
- Dongjiang Niu
- College of Computer Science and Technology, Qingdao University, No. 308 Ningxia Road, Qingdao, 266071, Shandong, China
| | - Lianwei Zhang
- College of Computer Science and Technology, Qingdao University, No. 308 Ningxia Road, Qingdao, 266071, Shandong, China
| | - Beiyi Zhang
- College of Computer Science and Technology, Qingdao University, No. 308 Ningxia Road, Qingdao, 266071, Shandong, China
| | - Qiang Zhang
- College of Computer Science and Technology, Qingdao University, No. 308 Ningxia Road, Qingdao, 266071, Shandong, China
| | - Zhen Li
- College of Computer Science and Technology, Qingdao University, No. 308 Ningxia Road, Qingdao, 266071, Shandong, China.
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Zhang Y, Deng Z, Xu X, Feng Y, Junliang S. Application of Artificial Intelligence in Drug-Drug Interactions Prediction: A Review. J Chem Inf Model 2024; 64:2158-2173. [PMID: 37458400 DOI: 10.1021/acs.jcim.3c00582] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/09/2024]
Abstract
Drug-drug interactions (DDI) are a critical aspect of drug research that can have adverse effects on patients and can lead to serious consequences. Predicting these events accurately can significantly improve clinicians' ability to make better decisions and establish optimal treatment regimens. However, manually detecting these interactions is time-consuming and labor-intensive. Utilizing the advancements in Artificial Intelligence (AI) is essential for achieving accurate forecasts of DDIs. In this review, DDI prediction tasks are classified into three types according to the type of DDI prediction: undirected DDI prediction, DDI events prediction, and Asymmetric DDI prediction. The paper then reviews the progress of AI for each of these three prediction tasks in DDI and provides a summary of the data sets used as well as the representative methods used in these three prediction directions. In this review, we aim to provide a comprehensive overview of drug interaction prediction. The first section introduces commonly used databases and presents an overview of current research advancements and techniques across three domains of DDI. Additionally, we introduce classical machine learning techniques for predicting undirected drug interactions and provide a timeline for the progression of the predicted drug interaction events. At last, we debate the difficulties and prospects of AI approaches at predicting DDI, emphasizing their potential for improving clinical decision-making and patient outcomes.
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Affiliation(s)
- Yuanyuan Zhang
- School of Information and Control Engineering, Qingdao University of Technology, Qingdao,266000,China
| | - Zengqian Deng
- School of Information and Control Engineering, Qingdao University of Technology, Qingdao,266000,China
| | - Xiaoyu Xu
- School of Information and Control Engineering, Qingdao University of Technology, Qingdao,266000,China
| | - Yinfei Feng
- School of Information and Control Engineering, Qingdao University of Technology, Qingdao,266000,China
| | - Shang Junliang
- School of Information Science and Engineering, Qufu Normal University, Rizhao, 276800, China
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3
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Wang NN, Zhu B, Li XL, Liu S, Shi JY, Cao DS. Comprehensive Review of Drug-Drug Interaction Prediction Based on Machine Learning: Current Status, Challenges, and Opportunities. J Chem Inf Model 2024; 64:96-109. [PMID: 38132638 DOI: 10.1021/acs.jcim.3c01304] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2023]
Abstract
Detecting drug-drug interactions (DDIs) is an essential step in drug development and drug administration. Given the shortcomings of current experimental methods, the machine learning (ML) approach has become a reliable alternative, attracting extensive attention from the academic and industrial fields. With the rapid development of computational science and the growing popularity of cross-disciplinary research, a large number of DDI prediction studies based on ML methods have been published in recent years. To give an insight into the current situation and future direction of DDI prediction research, we systemically review these studies from three aspects: (1) the classic DDI databases, mainly including databases of drugs, side effects, and DDI information; (2) commonly used drug attributes, which focus on chemical, biological, and phenotypic attributes for representing drugs; (3) popular ML approaches, such as shallow learning-based, deep learning-based, recommender system-based, and knowledge graph-based methods for DDI detection. For each section, related studies are described, summarized, and compared, respectively. In the end, we conclude the research status of DDI prediction based on ML methods and point out the existing issues, future challenges, potential opportunities, and subsequent research direction.
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Affiliation(s)
- Ning-Ning Wang
- Department of Pharmacy, Xiangya Hospital, Central South University, Changsha 410008, Hunan, P.R. China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha 410008, Hunan, P.R. China
| | - Bei Zhu
- School of Life Sciences, Northwestern Polytechnical University, Xi'an 710072, Shanxi, P.R. China
| | - Xin-Liang Li
- Department of Pharmacy, Xiangya Hospital, Central South University, Changsha 410008, Hunan, P.R. China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha 410008, Hunan, P.R. China
| | - Shao Liu
- Department of Pharmacy, Xiangya Hospital, Central South University, Changsha 410008, Hunan, P.R. China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha 410008, Hunan, P.R. China
| | - Jian-Yu Shi
- School of Life Sciences, Northwestern Polytechnical University, Xi'an 710072, Shanxi, P.R. China
| | - Dong-Sheng Cao
- Department of Pharmacy, Xiangya Hospital, Central South University, Changsha 410008, Hunan, P.R. China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha 410008, Hunan, P.R. China
- Xiangya School of Pharmaceutical Sciences, Central South University, Changsha 410013, Hunan, P.R. China
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Masumshah R, Eslahchi C. DPSP: a multimodal deep learning framework for polypharmacy side effects prediction. BIOINFORMATICS ADVANCES 2023; 3:vbad110. [PMID: 37701676 PMCID: PMC10493180 DOI: 10.1093/bioadv/vbad110] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/08/2023] [Revised: 08/01/2023] [Accepted: 08/15/2023] [Indexed: 09/14/2023]
Abstract
Motivation Because unanticipated drug-drug interactions (DDIs) can result in severe bodily harm, identifying the adverse effects of polypharmacy is one of the most important tasks in human health. Over the past few decades, computational methods for predicting the adverse effects of polypharmacy have been developed. Results This article presents DPSP, a framework for predicting polypharmacy side effects based on the construction of novel drug features and the application of a deep neural network to predict DDIs. In the first step, a variety of drug information is evaluated, and a feature extraction method and the Jaccard similarity are used to determine similarities between two drugs. By combining these similarities, a novel feature vector is generated for each drug. In the second step, the method predicts DDIs for specific DDI events using a multimodal framework and drug feature vectors. On three benchmark datasets, the performance of DPSP is measured by comparing its results to those of several well-known methods, such as GNN-DDI, MSTE, MDF-SA-DDI, NNPS, DDIMDL, DNN, DeepDDI, KNN, LR, and RF. DPSP outperforms these classification methods based on a variety of classification metrics. The results indicate that the use of diverse drug information is effective and efficient for identifying DDI adverse effects. Availability and implementation The source code and datasets are available at https://github.com/raziyehmasumshah/DPSP.
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Affiliation(s)
- Raziyeh Masumshah
- Department of Computer and Data Sciences, Faculty of Mathematical Sciences, Shahid Beheshti University, Tehran 1983969411, Iran
| | - Changiz Eslahchi
- Department of Computer and Data Sciences, Faculty of Mathematical Sciences, Shahid Beheshti University, Tehran 1983969411, Iran
- School of Biological Sciences, Institute for Research in Fundamental Sciences (IPM), Tehran 193955746, Iran
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5
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Espinosa K, Georgiadis P, Christopoulou F, Ju M, Miwa M, Ananiadou S. Comparing neural models for nested and overlapping biomedical event detection. BMC Bioinformatics 2022; 23:211. [PMID: 35655127 PMCID: PMC9161617 DOI: 10.1186/s12859-022-04746-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2021] [Accepted: 05/05/2022] [Indexed: 11/16/2022] Open
Abstract
Background Nested and overlapping events are particularly frequent and informative structures in biomedical event extraction. However, state-of-the-art neural models either neglect those structures during learning or use syntactic features and external tools to detect them. To overcome these limitations, this paper presents and compares two neural models: a novel EXhaustive Neural Network (EXNN) and a Search-Based Neural Network (SBNN) for detection of nested and overlapping events. Results We evaluate the proposed models as an event detection component in isolation and within a pipeline setting. Evaluation in several annotated biomedical event extraction datasets shows that both EXNN and SBNN achieve higher performance in detecting nested and overlapping events, compared to the state-of-the-art model Turku Event Extraction System (TEES). Conclusions The experimental results reveal that both EXNN and SBNN are effective for biomedical event extraction. Furthermore, results on a pipeline setting indicate that our models improve detection of events compared to models that use either gold or predicted named entities.
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Li Y, Wei L, Wang C, Zhao J, Han S, Zhang Y, Du W. LPInsider: a webserver for lncRNA–protein interaction extraction from the literature. BMC Bioinformatics 2022; 23:135. [PMID: 35428172 PMCID: PMC9013167 DOI: 10.1186/s12859-022-04665-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2021] [Accepted: 04/04/2022] [Indexed: 11/24/2022] Open
Abstract
Background Long non-coding RNA (LncRNA) plays important roles in physiological and pathological processes. Identifying LncRNA–protein interactions (LPIs) is essential to understand the molecular mechanism and infer the functions of lncRNAs. With the overwhelming size of the biomedical literature, extracting LPIs directly from the biomedical literature is essential, promising and challenging. However, there is no webserver of LPIs relationship extraction from literature. Results LPInsider is developed as the first webserver for extracting LPIs from biomedical literature texts based on multiple text features (semantic word vectors, syntactic structure vectors, distance vectors, and part of speech vectors) and logistic regression. LPInsider allows researchers to extract LPIs by uploading PMID, PMCID, PMID List, or biomedical text. A manually filtered and highly reliable LPI corpus is integrated in LPInsider. The performance of LPInsider is optimal by comprehensive experiment on different combinations of different feature and machine learning models. Conclusions LPInsider is an efficient analytical tool for LPIs that helps researchers to enhance their comprehension of lncRNAs from text mining, and also saving their time. In addition, LPInsider is freely accessible from http://www.csbg-jlu.info/LPInsider/ with no login requirement. The source code and LPIs corpus can be downloaded from https://github.com/qiufengdiewu/LPInsider. Supplementary Information The online version contains supplementary material available at 10.1186/s12859-022-04665-3.
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Wang NN, Wang XG, Xiong GL, Yang ZY, Lu AP, Chen X, Liu S, Hou TJ, Cao DS. Machine learning to predict metabolic drug interactions related to cytochrome P450 isozymes. J Cheminform 2022; 14:23. [PMID: 35428354 PMCID: PMC9013037 DOI: 10.1186/s13321-022-00602-x] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2021] [Accepted: 03/26/2022] [Indexed: 11/28/2022] Open
Abstract
Drug–drug interaction (DDI) often causes serious adverse reactions and thus results in inestimable economic and social loss. Currently, comprehensive DDI evaluation has become a major challenge in pharmaceutical research due to the time-consuming and costly process of the experimental assessment and it is of high necessity to develop effective in silico methods to predict and evaluate DDIs accurately and efficiently. In this study, based on a large number of substrates and inhibitors related to five important CYP450 isozymes (CYP1A2, CYP2C9, CYP2C19, CYP2D6 and CYP3A4), a series of high-performance predictive models for metabolic DDIs were constructed by two machine learning methods (random forest and XGBoost) and 4 different types of descriptors (MOE_2D, CATS, ECFP4 and MACCS). To reduce the uncertainty of individual models, the consensus method was applied to yield more reliable predictions. A series of evaluations illustrated that the consensus models were more reliable and robust for the DDI predictions of new drug combination. For the internal validation, the whole prediction accuracy and AUC value of the DDI models were around 0.8 and 0.9, respectively. When it was applied to the external datasets, the model accuracy was 0.793 and 0.795 for multi-level validation and external validation, respectively. Furthermore, we also compared our model with some recently published tools and then applied the final model to predict FDA-approved drugs and proposed 54,013 possible drug pairs with potential DDIs. In summary, we developed a powerful DDI predictive model from the perspective of the CYP450 enzyme family and it will help a lot in the future drug development and clinical pharmacy research.
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Zhang C, Lu Y, Zang T. CNN-DDI: a learning-based method for predicting drug-drug interactions using convolution neural networks. BMC Bioinformatics 2022; 23:88. [PMID: 35255808 PMCID: PMC8902704 DOI: 10.1186/s12859-022-04612-2] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2022] [Accepted: 02/14/2022] [Indexed: 01/07/2023] Open
Abstract
Background Drug–drug interactions (DDIs) are the reactions between drugs. They are compartmentalized into three types: synergistic, antagonistic and no reaction. As a rapidly developing technology, predicting DDIs-associated events is getting more and more attention and application in drug development and disease diagnosis fields. In this work, we study not only whether the two drugs interact, but also specific interaction types. And we propose a learning-based method using convolution neural networks to learn feature representations and predict DDIs. Results In this paper, we proposed a novel algorithm using a CNN architecture, named CNN-DDI, to predict drug–drug interactions. First, we extract feature interactions from drug categories, targets, pathways and enzymes as feature vectors and employ the Jaccard similarity as the measurement of drugs similarity. Then, based on the representation of features, we build a new convolution neural network as the DDIs’ predictor. Conclusion The experimental results indicate that drug categories is effective as a new feature type applied to CNN-DDI method. And using multiple features is more informative and more effective than single feature. It can be concluded that CNN-DDI has more superiority than other existing algorithms on task of predicting DDIs.
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Affiliation(s)
- Chengcheng Zhang
- Department of Computer Science and Technology, Harbin Institute of Technology, Harbin, China
| | - Yao Lu
- General Hospital of Heilongjiang Province Land Reclamation Bureau, Harbin, China
| | - Tianyi Zang
- Department of Computer Science and Technology, Harbin Institute of Technology, Harbin, China.
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Han K, Cao P, Wang Y, Xie F, Ma J, Yu M, Wang J, Xu Y, Zhang Y, Wan J. A Review of Approaches for Predicting Drug–Drug Interactions Based on Machine Learning. Front Pharmacol 2022; 12:814858. [PMID: 35153767 PMCID: PMC8835726 DOI: 10.3389/fphar.2021.814858] [Citation(s) in RCA: 30] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2021] [Accepted: 12/20/2021] [Indexed: 01/01/2023] Open
Abstract
Drug–drug interactions play a vital role in drug research. However, they may also cause adverse reactions in patients, with serious consequences. Manual detection of drug–drug interactions is time-consuming and expensive, so it is urgent to use computer methods to solve the problem. There are two ways for computers to identify drug interactions: one is to identify known drug interactions, and the other is to predict unknown drug interactions. In this paper, we review the research progress of machine learning in predicting unknown drug interactions. Among these methods, the literature-based method is special because it combines the extraction method of DDI and the prediction method of DDI. We first introduce the common databases, then briefly describe each method, and summarize the advantages and disadvantages of some prediction models. Finally, we discuss the challenges and prospects of machine learning methods in predicting drug interactions. This review aims to provide useful guidance for interested researchers to further promote bioinformatics algorithms to predict DDI.
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Affiliation(s)
- Ke Han
- Heilongjiang Provincial Key Laboratory of Electronic Commerce and Information Processing, School of Computer and Information Engineering, Harbin University of Commerce, Harbin, China
- College of Pharmacy, Harbin University of Commerce, Harbin, China
- *Correspondence: Ke Han, ; Jie Wan,
| | - Peigang Cao
- Beidahuang Industry Group General Hospital, Harbin, China
| | - Yu Wang
- Heilongjiang Provincial Key Laboratory of Electronic Commerce and Information Processing, School of Computer and Information Engineering, Harbin University of Commerce, Harbin, China
| | - Fang Xie
- Heilongjiang Provincial Key Laboratory of Electronic Commerce and Information Processing, School of Computer and Information Engineering, Harbin University of Commerce, Harbin, China
| | - Jiaqi Ma
- Heilongjiang Provincial Key Laboratory of Electronic Commerce and Information Processing, School of Computer and Information Engineering, Harbin University of Commerce, Harbin, China
| | - Mengyao Yu
- Heilongjiang Provincial Key Laboratory of Electronic Commerce and Information Processing, School of Computer and Information Engineering, Harbin University of Commerce, Harbin, China
| | - Jianchun Wang
- Heilongjiang Provincial Key Laboratory of Electronic Commerce and Information Processing, School of Computer and Information Engineering, Harbin University of Commerce, Harbin, China
| | - Yaoqun Xu
- Heilongjiang Provincial Key Laboratory of Electronic Commerce and Information Processing, School of Computer and Information Engineering, Harbin University of Commerce, Harbin, China
| | - Yu Zhang
- Heilongjiang Provincial Key Laboratory of Electronic Commerce and Information Processing, School of Computer and Information Engineering, Harbin University of Commerce, Harbin, China
| | - Jie Wan
- Laboratory for Space Environment and Physical Sciences, Harbin Institute of Technology, Harbin, China
- *Correspondence: Ke Han, ; Jie Wan,
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Allahgholi M, Rahmani H, Javdani D, Sadeghi-Adl Z, Bender A, Módos D, Weiss G. DDREL: From drug-drug relationships to drug repurposing. INTELL DATA ANAL 2022. [DOI: 10.3233/ida-215745] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
Analyzing the relationships among various drugs is an essential issue in the field of computational biology. Different kinds of informative knowledge, such as drug repurposing, can be extracted from drug-drug relationships. Scientific literature represents a rich source for the retrieval of knowledge about the relationships between biological concepts, mainly drug-drug, disease-disease, and drug-disease relationships. In this paper, we propose DDREL as a general-purpose method that applies deep learning on scientific literature to automatically extract the graph of syntactic and semantic relationships among drugs. DDREL remarkably outperforms the existing human drug network method and a random network respected to average similarities of drugs’ anatomical therapeutic chemical (ATC) codes. DDREL is able to shed light on the existing deficiency of the ATC codes in various drug groups. From the DDREL graph, the history of drug discovery became visible. In addition, drugs that had repurposing score 1 (diflunisal, pargyline, fenofibrate, guanfacine, chlorzoxazone, doxazosin, oxymetholone, azathioprine, drotaverine, demecarium, omifensine, yohimbine) were already used in additional indication. The proposed DDREL method justifies the predictive power of textual data in PubMed abstracts. DDREL shows that such data can be used to 1- Predict repurposing drugs with high accuracy, and 2- Reveal existing deficiencies of the ATC codes in various drug groups.
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Affiliation(s)
- Milad Allahgholi
- School of Computer Engineering, Iran University of Science and Technology, Tehran, Iran
| | - Hossein Rahmani
- School of Computer Engineering, Iran University of Science and Technology, Tehran, Iran
| | - Delaram Javdani
- School of Computer Engineering, Iran University of Science and Technology, Tehran, Iran
| | - Zahra Sadeghi-Adl
- School of Computer Engineering, Iran University of Science and Technology, Tehran, Iran
| | - Andreas Bender
- Centre for Molecular Informatics, Department of Chemistry, University of Cambridge, Cambridge, UK
| | - Dezsö Módos
- Quadram Institute Bioscience, Norwich Research Park, Norwich, Norfolk, UK
- Earlham Institute, Norwich Research Park, Norwich, Norfolk, UK
| | - Gerhard Weiss
- Department of Data Science and Knowledge Engineering (DKE), Maastricht University, Maastricht, The Netherlands
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11
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Yan C, Duan G, Zhang Y, Wu FX, Pan Y, Wang J. Predicting Drug-Drug Interactions Based on Integrated Similarity and Semi-Supervised Learning. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2022; 19:168-179. [PMID: 32310779 DOI: 10.1109/tcbb.2020.2988018] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
A drug-drug interaction (DDI) is defined as an association between two drugs where the pharmacological effects of a drug are influenced by another drug. Positive DDIs can usually improve the therapeutic effects of patients, but negative DDIs cause the major cause of adverse drug reactions and even result in the drug withdrawal from the market and the patient death. Therefore, identifying DDIs has become a key component of the drug development and disease treatment. In this study, we propose a novel method to predict DDIs based on the integrated similarity and semi-supervised learning (DDI-IS-SL). DDI-IS-SL integrates the drug chemical, biological and phenotype data to calculate the feature similarity of drugs with the cosine similarity method. The Gaussian Interaction Profile kernel similarity of drugs is also calculated based on known DDIs. A semi-supervised learning method (the Regularized Least Squares classifier) is used to calculate the interaction possibility scores of drug-drug pairs. In terms of the 5-fold cross validation, 10-fold cross validation and de novo drug validation, DDI-IS-SL can achieve the better prediction performance than other comparative methods. In addition, the average computation time of DDI-IS-SL is shorter than that of other comparative methods. Finally, case studies further demonstrate the performance of DDI-IS-SL in practical applications.
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Wong M, Previde P, Cole J, Thomas B, Laxmeshwar N, Mallory E, Lever J, Petkovic D, Altman RB, Kulkarni A. Search and visualization of gene-drug-disease interactions for pharmacogenomics and precision medicine research using GeneDive. J Biomed Inform 2021; 117:103732. [PMID: 33737208 DOI: 10.1016/j.jbi.2021.103732] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2020] [Revised: 12/10/2020] [Accepted: 02/28/2021] [Indexed: 10/21/2022]
Abstract
BACKGROUND Understanding the relationships between genes, drugs, and disease states is at the core of pharmacogenomics. Two leading approaches for identifying these relationships in medical literature are: human expert led manual curation efforts, and modern data mining based automated approaches. The former generates small amounts of high-quality data, and the latter offers large volumes of mixed quality data. The algorithmically extracted relationships are often accompanied by supporting evidence, such as, confidence scores, source articles, and surrounding contexts (excerpts) from the articles, that can be used as data quality indicators. Tools that can leverage these quality indicators to help the user gain access to larger and high-quality data are needed. APPROACH We introduce GeneDive, a web application for pharmacogenomics researchers and precision medicine practitioners that makes gene, disease, and drug interactions data easily accessible and usable. GeneDive is designed to meet three key objectives: (1) provide functionality to manage information-overload problem and facilitate easy assimilation of supporting evidence, (2) support longitudinal and exploratory research investigations, and (3) offer integration of user-provided interactions data without requiring data sharing. RESULTS GeneDive offers multiple search modalities, visualizations, and other features that guide the user efficiently to the information of their interest. To facilitate exploratory research, GeneDive makes the supporting evidence and context for each interaction readily available and allows the data quality threshold to be controlled by the user as per their risk tolerance level. The interactive search-visualization loop enables relationship discoveries between diseases, genes, and drugs that might not be explicitly described in literature but are emergent from the source medical corpus and deductive reasoning. The ability to utilize user's data either in combination with the GeneDive native datasets or in isolation promotes richer data-driven exploration and discovery. These functionalities along with GeneDive's applicability for precision medicine, bringing the knowledge contained in biomedical literature to bear on particular clinical situations and improving patient care, are illustrated through detailed use cases. CONCLUSION GeneDive is a comprehensive, broad-use biological interactions browser. The GeneDive application and information about its underlying system architecture are available at http://www.genedive.net. GeneDive Docker image is also available for download at this URL, allowing users to (1) import their own interaction data securely and privately; and (2) generate and test hypotheses across their own and other datasets.
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Affiliation(s)
- Mike Wong
- COSE Computing for Life Sciences, San Francisco State University, San Francisco, CA, United States
| | - Paul Previde
- Department of Computer Science, San Francisco State University, San Francisco, CA, United States
| | - Jack Cole
- Department of Computer Science, San Francisco State University, San Francisco, CA, United States
| | - Brook Thomas
- Department of Computer Science, San Francisco State University, San Francisco, CA, United States
| | - Nayana Laxmeshwar
- Department of Computer Science, San Francisco State University, San Francisco, CA, United States
| | - Emily Mallory
- Biomedical Informatics Training Program, Stanford University, Palo Alto, CA, United States
| | - Jake Lever
- Postdoctoral Scholar, Stanford University, Palo Alto, CA, United States
| | - Dragutin Petkovic
- Department of Computer Science, San Francisco State University, San Francisco, CA, United States; COSE Computing for Life Sciences, San Francisco State University, San Francisco, CA, United States
| | - Russ B Altman
- Department of Bioengineering, Department of Genetics, and School of Medicine, Stanford University, Palo Alto, CA, United States
| | - Anagha Kulkarni
- Department of Computer Science, San Francisco State University, San Francisco, CA, United States.
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13
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Assiri A, Noor A. A computational approach to predict multi-pathway drug-drug interactions: A case study of irinotecan, a colon cancer medication. Saudi Pharm J 2020; 28:1507-1513. [PMID: 33424244 PMCID: PMC7783232 DOI: 10.1016/j.jsps.2020.09.017] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2020] [Accepted: 09/18/2020] [Indexed: 01/03/2023] Open
Abstract
Drug-drug interactions (DDIs) are a potentially distressing corollary of drug interventions, and may result in discomfort, debilitating illness, or even death. Existing research predominantly considers only a single level of interaction; however, serious health complications may result from multi-pathway DDIs, and so new methods are needed to enable predicting and preventing complex DDIs. This article introduces a novel method for the prediction of DDIs at two pharmacological levels (metabolic and transporter interactions) by means of a rule-based model implemented with Semantic Web technologies. The chemotherapy agent irinotecan is used as a case study for demonstrating the validity of this approach. Mechanistic and interaction data were mined from available sources and then used to predict interactors of irinotecan, including potential DDIs mediated by previously unidentified mechanisms. The findings also draw attention to the profound variation between DDI resources, indicating that clinical practice would see significant value from the development of an evidence-based resource to support DDI identification.
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Affiliation(s)
- Abdullah Assiri
- Department of Clinical Pharmacy, College of Pharmacy, King Khalid University, Abha 62529, Saudi Arabia
| | - Adeeb Noor
- Department of Information Technology, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 80221, Saudi Arabia
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14
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ADDI: Recommending alternatives for drug-drug interactions with negative health effects. Comput Biol Med 2020; 125:103969. [PMID: 32836102 DOI: 10.1016/j.compbiomed.2020.103969] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2020] [Revised: 08/09/2020] [Accepted: 08/09/2020] [Indexed: 11/21/2022]
Abstract
Investigating the interactions among various drugs is an indispensable issue in the field of computational biology. Scientific literature represents a rich source for the retrieval of knowledge about the interactions between drugs. Predicting drug-drug interaction (DDI) types will help biologists to evade hazardous drug interactions and support them in discovering potential alternatives that increase therapeutic efficacy and reduce toxicity. In this paper, we propose a general-purpose method called ADDI (standing for Alternative Drug-Drug Interaction) that applies deep learning on PubMed abstracts to predict interaction types among drugs. As an application, ADDI recommends alternatives for drug-drug interactions (DDIs) which have Negative Health Effects Types (NHETs). ADDI clearly outperforms state-of-the-art methods, on average by 13%, with respect to accuracy by using only the textual content of the online PubMed papers. Additionally, manual evaluation of ADDI indicates high precision in recommending alternatives for DDIs with NHETs.
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15
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Abstract
Rapid increases in data volume and variety pose a challenge to safe drug prescription for health professionals like doctors and dentists. This is addressed by our study, which presents innovative approaches in mining data from drug corpus and extracting feature vectors to combine this knowledge with individual patient medical profiles. Within our three-tiered framework-the prediction layer, the knowledge layer and the presentation layer-we describe multiple approaches in computing similarity ratios from the feature vectors, illustrated with an example of applying the framework in a typical medical clinic. Experimental evaluation shows that the word embedding model performs better than the adverse network model, with a F score of 0.75. The F score is a common metrics used for evaluating the performance of classification algorithms. Similarity to a drug the patient is allergic to or is taking are important considerations for the suitability of a drug for prescription. Hence, such an approach, when integrated within the clinical work-flow, will reduce prescription errors thereby increasing patient health outcomes.
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16
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Deng J, Wang F. An Informatics-based Approach to Identify Key Pharmacological Components in Drug-Drug Interactions. AMIA JOINT SUMMITS ON TRANSLATIONAL SCIENCE PROCEEDINGS. AMIA JOINT SUMMITS ON TRANSLATIONAL SCIENCE 2020; 2020:142-151. [PMID: 32477633 PMCID: PMC7233048] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
Drug-drug interactions (DDI) can cause severe adverse drug reactions and pose a major challenge to medication therapy. Recently, informatics-based approaches are emerging for DDI studies. In this paper, we aim to identify key pharmacological components in DDI based on large-scale data from DrugBank, a comprehensive DDI database. With pharmacological components as features, logistic regression is used to perform DDI classification with a focus on searching for most predictive features, a process of identifying key pharmacological components. Using univariate feature selection with chi-squared statistic as the ranking criteria, our study reveals that top 10% features can achieve comparable classification performance compared to that using all features. The top 10% features are identified to be key pharmacological components. Furthermore, their importance is quantified by feature coefficients in the classifier, which measures the DDI potential and provides a novel perspective to evaluate pharmacological components.
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Affiliation(s)
- Jianyuan Deng
- Department of Biomedical Informatics, Stony Brook University
| | - Fusheng Wang
- Department of Biomedical Informatics, Stony Brook University
- Department of Computer Science, Stony Brook University
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17
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Dere S, Ayvaz S. Prediction of Drug-Drug Interactions by Using Profile Fingerprint Vectors and Protein Similarities. Healthc Inform Res 2020; 26:42-49. [PMID: 32082699 PMCID: PMC7010946 DOI: 10.4258/hir.2020.26.1.42] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2019] [Revised: 12/24/2019] [Accepted: 12/25/2019] [Indexed: 12/21/2022] Open
Abstract
Objectives Drug-drug interaction (DDI) is a vital problem that threatens people's health. However, the prediction of DDIs through in-vivo experiments is not only extremely costly but also difficult as many serious side effects are hard to detect in in-vivo and in-vitro settings. The aim of this study was to assess the effectiveness of similarity-based in-silico computational DDI prediction approaches and to provide a cost effective and scalable solution to predict potential DDIs. Methods In this study, widely known similarity-based computational DDI prediction methods were utilized to discover novel potential DDIs. More specifically, known interactions, drug targets, adverse effects, and protein similarities of drug pairs were used to construct drug fingerprints for the prediction of DDIs. Results Using the drug interaction profile, our approach achieved an area under the curve (AUC) of 0.975 in the prediction of a potential DDI. The drug adverse effect profile and protein profile similarity-based methods resulted in AUC values of 0.685 and 0.895, respectively, in the prediction of DDIs. Conclusions In this study, we developed a computational approach to the prediction of potential drug interactions. The performance of the similarity-based computational methods was comparatively evaluated using a comprehensive real-world DDI dataset. The evaluations showed that the drug interaction profile information is a better predictor of DDIs compared to drug adverse effects and protein similarities among DDI pairs.
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Affiliation(s)
- Selma Dere
- Department of Computer Engineering, Bahcesehir University, Besiktas, Istanbul, Turkey
| | - Serkan Ayvaz
- Department of Software Engineering, Bahcesehir University, Besiktas, Istanbul, Turkey
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18
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Wu HY, Shendre A, Zhang S, Zhang P, Wang L, Zeruesenay D, Rocha LM, Shatkay H, Quinney SK, Ning X, Li L. Translational Knowledge Discovery Between Drug Interactions and Pharmacogenetics. Clin Pharmacol Ther 2020; 107:886-902. [PMID: 31863452 DOI: 10.1002/cpt.1745] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2019] [Accepted: 11/25/2019] [Indexed: 12/12/2022]
Abstract
Clinical translation of drug-drug interaction (DDI) studies is limited, and knowledge gaps across different types of DDI evidence make it difficult to consolidate and link them to clinical consequences. Consequently, we developed information retrieval (IR) models to retrieve DDI and drug-gene interaction (DGI) evidence from 25 million PubMed abstracts and distinguish DDI evidence into in vitro pharmacokinetic (PK), clinical PK, and clinical pharmacodynamic (PD) studies for US Food and Drug Administration (FDA) approved and withdrawn drugs. Additionally, information extraction models were developed to extract DDI-pairs and DGI-pairs from the IR-retrieved abstracts. An overlapping analysis identified 986 unique DDI-pairs between all 3 types of evidence. Another 2,157 and 13,012 DDI-pairs and 3,173 DGI-pairs were identified from known clinical PK/PD DDI, clinical PD DDI, and DGI evidence, respectively. By integrating DDI and DGI evidence, we discovered 119 and 18 new pharmacogenetic hypotheses associated with CYP3A and CYP2D6, respectively. Some of these DGI evidence can also aid us in understanding DDI mechanisms.
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Affiliation(s)
- Heng-Yi Wu
- Genentech Inc., San Francisco, California, USA
| | - Aditi Shendre
- Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, Ohio, USA
| | - Shijun Zhang
- Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, Ohio, USA
| | - Pengyue Zhang
- Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, Ohio, USA
| | - Lei Wang
- Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, Ohio, USA
| | - Desta Zeruesenay
- Division of Clinical Pharmacology, Department of Medicine, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Luis M Rocha
- School of Informatics, Computing & Engineering, Indiana University, Bloomington, Indiana, USA.,Instituto Gulbenkian de Ciência, Oeiras, Portugal
| | - Hagit Shatkay
- Department of Computer and Information Sciences, University of Delaware, Newark, Delaware, USA
| | - Sara K Quinney
- Division of Clinical Pharmacology, Department of Medicine, Indiana University School of Medicine, Indianapolis, Indiana, USA.,Department of Obstetrics and Gynecology, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Xia Ning
- Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, Ohio, USA.,Department of Computer Science and Engineering, The Ohio State University, Columbus, Ohio, USA
| | - Lang Li
- Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, Ohio, USA
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19
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Yan C, Duan G, Pan Y, Wu FX, Wang J. DDIGIP: predicting drug-drug interactions based on Gaussian interaction profile kernels. BMC Bioinformatics 2019; 20:538. [PMID: 31874609 PMCID: PMC6929542 DOI: 10.1186/s12859-019-3093-x] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2019] [Accepted: 09/10/2019] [Indexed: 12/23/2022] Open
Abstract
BACKGROUND A drug-drug interaction (DDI) is defined as a drug effect modified by another drug, which is very common in treating complex diseases such as cancer. Many studies have evidenced that some DDIs could be an increase or a decrease of the drug effect. However, the adverse DDIs maybe result in severe morbidity and even morality of patients, which also cause some drugs to withdraw from the market. As the multi-drug treatment becomes more and more common, identifying the potential DDIs has become the key issue in drug development and disease treatment. However, traditional biological experimental methods, including in vitro and vivo, are very time-consuming and expensive to validate new DDIs. With the development of high-throughput sequencing technology, many pharmaceutical studies and various bioinformatics data provide unprecedented opportunities to study DDIs. RESULT In this study, we propose a method to predict new DDIs, namely DDIGIP, which is based on Gaussian Interaction Profile (GIP) kernel on the drug-drug interaction profiles and the Regularized Least Squares (RLS) classifier. In addition, we also use the k-nearest neighbors (KNN) to calculate the initial relational score in the presence of new drugs via the chemical, biological, phenotypic data of drugs. We compare the prediction performance of DDIGIP with other competing methods via the 5-fold cross validation, 10-cross validation and de novo drug validation. CONLUSION In 5-fold cross validation and 10-cross validation, DDRGIP method achieves the area under the ROC curve (AUC) of 0.9600 and 0.9636 which are better than state-of-the-art method (L1 Classifier ensemble method) of 0.9570 and 0.9599. Furthermore, for new drugs, the AUC value of DDIGIP in de novo drug validation reaches 0.9262 which also outperforms the other state-of-the-art method (Weighted average ensemble method) of 0.9073. Case studies and these results demonstrate that DDRGIP is an effective method to predict DDIs while being beneficial to drug development and disease treatment.
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Affiliation(s)
- Cheng Yan
- School of Computer Science and Engineering, Central South University, 932 South Lushan Rd, ChangSha, 410083 China
- School of Computer and Information,Qiannan Normal University for Nationalities, Longshan Road, DuYun, 558000 China
| | - Guihua Duan
- School of Computer Science and Engineering, Central South University, 932 South Lushan Rd, ChangSha, 410083 China
| | - Yi Pan
- Department of Computer Science, Georgia State University, Atlanta, GA30302 USA
| | - Fang-Xiang Wu
- Biomedical Engineering and Department of Mechanical Engineering, University of Saskatchewan, Saskatoon, SKS7N5A9 Canada
| | - Jianxin Wang
- School of Computer Science and Engineering, Central South University, 932 South Lushan Rd, ChangSha, 410083 China
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20
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Pichler M, Boreux V, Klein A, Schleuning M, Hartig F. Machine learning algorithms to infer trait‐matching and predict species interactions in ecological networks. Methods Ecol Evol 2019. [DOI: 10.1111/2041-210x.13329] [Citation(s) in RCA: 54] [Impact Index Per Article: 10.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Affiliation(s)
| | - Virginie Boreux
- Nature Conservation and Landscape Ecology University of Freiburg Freiburg Germany
| | | | - Matthias Schleuning
- Senckenberg Biodiversity and Climate Research Centre (SBiK‐F) Frankfurt (Main) Germany
| | - Florian Hartig
- Theoretical Ecology University of Regensburg Regensburg Germany
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21
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Zhang W, Jing K, Huang F, Chen Y, Li B, Li J, Gong J. SFLLN: A sparse feature learning ensemble method with linear neighborhood regularization for predicting drug–drug interactions. Inf Sci (N Y) 2019. [DOI: 10.1016/j.ins.2019.05.017] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
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22
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Cañada A, Capella-Gutierrez S, Rabal O, Oyarzabal J, Valencia A, Krallinger M. LimTox: a web tool for applied text mining of adverse event and toxicity associations of compounds, drugs and genes. Nucleic Acids Res 2019; 45:W484-W489. [PMID: 28531339 PMCID: PMC5570141 DOI: 10.1093/nar/gkx462] [Citation(s) in RCA: 33] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2017] [Accepted: 05/16/2017] [Indexed: 01/03/2023] Open
Abstract
A considerable effort has been devoted to retrieve systematically information for genes and proteins as well as relationships between them. Despite the importance of chemical compounds and drugs as a central bio-entity in pharmacological and biological research, only a limited number of freely available chemical text-mining/search engine technologies are currently accessible. Here we present LimTox (Literature Mining for Toxicology), a web-based online biomedical search tool with special focus on adverse hepatobiliary reactions. It integrates a range of text mining, named entity recognition and information extraction components. LimTox relies on machine-learning, rule-based, pattern-based and term lookup strategies. This system processes scientific abstracts, a set of full text articles and medical agency assessment reports. Although the main focus of LimTox is on adverse liver events, it enables also basic searches for other organ level toxicity associations (nephrotoxicity, cardiotoxicity, thyrotoxicity and phospholipidosis). This tool supports specialized search queries for: chemical compounds/drugs, genes (with additional emphasis on key enzymes in drug metabolism, namely P450 cytochromes—CYPs) and biochemical liver markers. The LimTox website is free and open to all users and there is no login requirement. LimTox can be accessed at: http://limtox.bioinfo.cnio.es
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Affiliation(s)
- Andres Cañada
- Spanish National Bioinformatics Institute Unit, Structural Biology and BioComputing Programme, Spanish National Cancer Research Centre (CNIO), Madrid 28029, Spain
| | - Salvador Capella-Gutierrez
- Spanish National Bioinformatics Institute Unit, Structural Biology and BioComputing Programme, Spanish National Cancer Research Centre (CNIO), Madrid 28029, Spain
| | - Obdulia Rabal
- Small Molecule Discovery Platform, Molecular Therapeutics Program, Center for Applied Medical Research (CIMA), University of Navarra, Pamplona 31008, Spain
| | - Julen Oyarzabal
- Small Molecule Discovery Platform, Molecular Therapeutics Program, Center for Applied Medical Research (CIMA), University of Navarra, Pamplona 31008, Spain
| | - Alfonso Valencia
- Barcelona Supercomputing Center (BSC), Joint BSC-CRG-IRB, Research Program in Computational Biology, BSC-CRG-IRB, Barcelona 08028, Spain.,Life Science Department, Barcelona Supercomputing Centre (BSC-CNS), 08034 Barcelona, Spain.,Institució Catalana de Recerca i Estudis Avançats (ICREA), 08010 Barcelona, Spain
| | - Martin Krallinger
- Biological Text Mining Unit, Structural Biology and BioComputing Programme, Spanish National Cancer Research Centre (CNIO), Madrid 28029, Spain
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23
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Natsiavas P, Malousi A, Bousquet C, Jaulent MC, Koutkias V. Computational Advances in Drug Safety: Systematic and Mapping Review of Knowledge Engineering Based Approaches. Front Pharmacol 2019; 10:415. [PMID: 31156424 PMCID: PMC6533857 DOI: 10.3389/fphar.2019.00415] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2018] [Accepted: 04/02/2019] [Indexed: 12/12/2022] Open
Abstract
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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Affiliation(s)
- Pantelis Natsiavas
- Institute of Applied Biosciences, Centre for Research and Technology Hellas, Thessaloniki, Greece.,Sorbonne Université, INSERM, Univ Paris 13, Laboratoire d'Informatique Médicale et d'Ingénierie des Connaissances pour la e-Santé, LIMICS, Paris, France
| | - Andigoni Malousi
- Laboratory of Biological Chemistry, Department of Medicine, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Cédric Bousquet
- Sorbonne Université, INSERM, Univ Paris 13, Laboratoire d'Informatique Médicale et d'Ingénierie des Connaissances pour la e-Santé, LIMICS, Paris, France.,Public Health and Medical Information Unit, University Hospital of Saint-Etienne, Saint-Étienne, France
| | - Marie-Christine Jaulent
- Sorbonne Université, INSERM, Univ Paris 13, Laboratoire d'Informatique Médicale et d'Ingénierie des Connaissances pour la e-Santé, LIMICS, Paris, France
| | - Vassilis Koutkias
- Institute of Applied Biosciences, Centre for Research and Technology Hellas, Thessaloniki, Greece
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24
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Saqi M, Lysenko A, Guo YK, Tsunoda T, Auffray C. Navigating the disease landscape: knowledge representations for contextualizing molecular signatures. Brief Bioinform 2019; 20:609-623. [PMID: 29684165 PMCID: PMC6556902 DOI: 10.1093/bib/bby025] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2017] [Revised: 02/05/2018] [Indexed: 12/14/2022] Open
Abstract
Large amounts of data emerging from experiments in molecular medicine are leading to the identification of molecular signatures associated with disease subtypes. The contextualization of these patterns is important for obtaining mechanistic insight into the aberrant processes associated with a disease, and this typically involves the integration of multiple heterogeneous types of data. In this review, we discuss knowledge representations that can be useful to explore the biological context of molecular signatures, in particular three main approaches, namely, pathway mapping approaches, molecular network centric approaches and approaches that represent biological statements as knowledge graphs. We discuss the utility of each of these paradigms, illustrate how they can be leveraged with selected practical examples and identify ongoing challenges for this field of research.
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Affiliation(s)
- Mansoor Saqi
- Mansoor Saqi Data Science Institute, Imperial College London, UK
| | - Artem Lysenko
- Artem Lysenko Laboratory for Medical Science Mathematics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - Yi-Ke Guo
- Yi-Ke Guo Data Science Institute, Imperial College London, UK
| | - Tatsuhiko Tsunoda
- Tatsuhiko Tsunoda Laboratory for Medical Science Mathematics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan CREST, JST, Tokyo, Japan Department of Medical Science Mathematics, Medical Research Institute, Tokyo Medical and Dental University, Tokyo, Japan
| | - Charles Auffray
- Charles Auffray European Institute for Systems Biology and Medicine, Lyon, France
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25
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CuDDI: A CUDA-Based Application for Extracting Drug-Drug Interaction Related Substance Terms from PubMed Literature. Molecules 2019; 24:molecules24061081. [PMID: 30893816 PMCID: PMC6470591 DOI: 10.3390/molecules24061081] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2019] [Revised: 03/12/2019] [Accepted: 03/16/2019] [Indexed: 11/30/2022] Open
Abstract
Drug-drug interaction (DDI) is becoming a serious issue in clinical pharmacy as the use of multiple medications is more common. The PubMed database is one of the biggest literature resources for DDI studies. It contains over 150,000 journal articles related to DDI and is still expanding at a rapid pace. The extraction of DDI-related information, including compounds and proteins from PubMed, is an essential step for DDI research. In this paper, we introduce a tool, CuDDI (compute unified device architecture-based DDI searching), for identification of DDI-related terms (including compounds and proteins) from PubMed. There are three modules in this application, including the automatic retrieval of substances from PubMed, the identification of DDI-related terms, and the display of relationship of DDI-related terms. For DDI term identification, a speedup of 30–105 times was observed for the compute unified device architecture (CUDA)-based version compared with the implementation with a CPU-based Python version. CuDDI can be used to discover DDI-related terms and relationships of these terms, which has the potential to help clinicians and pharmacists better understand the mechanism of DDIs. CuDDI is available at: https://github.com/chengusf/CuDDI.
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26
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Drug prescription support in dental clinics through drug corpus mining. INTERNATIONAL JOURNAL OF DATA SCIENCE AND ANALYTICS 2018. [DOI: 10.1007/s41060-018-0149-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/28/2022]
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27
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Vilar S, Friedman C, Hripcsak G. Detection of drug-drug interactions through data mining studies using clinical sources, scientific literature and social media. Brief Bioinform 2018; 19:863-877. [PMID: 28334070 PMCID: PMC6454455 DOI: 10.1093/bib/bbx010] [Citation(s) in RCA: 82] [Impact Index Per Article: 13.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2016] [Revised: 12/28/2016] [Indexed: 11/13/2022] Open
Abstract
Drug-drug interactions (DDIs) constitute an important concern in drug development and postmarketing pharmacovigilance. They are considered the cause of many adverse drug effects exposing patients to higher risks and increasing public health system costs. Methods to follow-up and discover possible DDIs causing harm to the population are a primary aim of drug safety researchers. Here, we review different methodologies and recent advances using data mining to detect DDIs with impact on patients. We focus on data mining of different pharmacovigilance sources, such as the US Food and Drug Administration Adverse Event Reporting System and electronic health records from medical institutions, as well as on the diverse data mining studies that use narrative text available in the scientific biomedical literature and social media. We pay attention to the strengths but also further explain challenges related to these methods. Data mining has important applications in the analysis of DDIs showing the impact of the interactions as a cause of adverse effects, extracting interactions to create knowledge data sets and gold standards and in the discovery of novel and dangerous DDIs.
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Affiliation(s)
- Santiago Vilar
- Department of Biomedical Informatics, Columbia University, New York, USA
- Department of Organic Chemistry, University of Santiago de Compostela, Spain
| | - Carol Friedman
- Department of Biomedical Informatics, Columbia University, New York, USA
| | - George Hripcsak
- Department of Biomedical Informatics, Columbia University, New York, USA
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28
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29
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Deepika SS, Geetha TV. A meta-learning framework using representation learning to predict drug-drug interaction. J Biomed Inform 2018; 84:136-147. [DOI: 10.1016/j.jbi.2018.06.015] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2018] [Revised: 06/22/2018] [Accepted: 06/25/2018] [Indexed: 01/24/2023]
Affiliation(s)
- S S Deepika
- Department of Computer Science, Anna University, Chennai, Tamil Nadu, India.
| | - T V Geetha
- Department of Computer Science, Anna University, Chennai, Tamil Nadu, India
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30
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Kastrin A, Ferk P, Leskošek B. Predicting potential drug-drug interactions on topological and semantic similarity features using statistical learning. PLoS One 2018; 13:e0196865. [PMID: 29738537 PMCID: PMC5940181 DOI: 10.1371/journal.pone.0196865] [Citation(s) in RCA: 54] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2017] [Accepted: 04/20/2018] [Indexed: 01/03/2023] Open
Abstract
Drug-drug interaction (DDI) is a change in the effect of a drug when patient takes another drug. Characterizing DDIs is extremely important to avoid potential adverse drug reactions. We represent DDIs as a complex network in which nodes refer to drugs and links refer to their potential interactions. Recently, the problem of link prediction has attracted much consideration in scientific community. We represent the process of link prediction as a binary classification task on networks of potential DDIs. We use link prediction techniques for predicting unknown interactions between drugs in five arbitrary chosen large-scale DDI databases, namely DrugBank, KEGG, NDF-RT, SemMedDB, and Twosides. We estimated the performance of link prediction using a series of experiments on DDI networks. We performed link prediction using unsupervised and supervised approach including classification tree, k-nearest neighbors, support vector machine, random forest, and gradient boosting machine classifiers based on topological and semantic similarity features. Supervised approach clearly outperforms unsupervised approach. The Twosides network gained the best prediction performance regarding the area under the precision-recall curve (0.93 for both random forests and gradient boosting machine). The applied methodology can be used as a tool to help researchers to identify potential DDIs. The supervised link prediction approach proved to be promising for potential DDIs prediction and may facilitate the identification of potential DDIs in clinical research.
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Affiliation(s)
- Andrej Kastrin
- Institute of Biostatistics and Medical Informatics, Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia
| | - Polonca Ferk
- Institute of Biostatistics and Medical Informatics, Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia
| | - Brane Leskošek
- Institute of Biostatistics and Medical Informatics, Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia
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31
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Deep learning improves prediction of drug-drug and drug-food interactions. Proc Natl Acad Sci U S A 2018; 115:E4304-E4311. [PMID: 29666228 DOI: 10.1073/pnas.1803294115] [Citation(s) in RCA: 206] [Impact Index Per Article: 34.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
Drug interactions, including drug-drug interactions (DDIs) and drug-food constituent interactions (DFIs), can trigger unexpected pharmacological effects, including adverse drug events (ADEs), with causal mechanisms often unknown. Several computational methods have been developed to better understand drug interactions, especially for DDIs. However, these methods do not provide sufficient details beyond the chance of DDI occurrence, or require detailed drug information often unavailable for DDI prediction. Here, we report development of a computational framework DeepDDI that uses names of drug-drug or drug-food constituent pairs and their structural information as inputs to accurately generate 86 important DDI types as outputs of human-readable sentences. DeepDDI uses deep neural network with its optimized prediction performance and predicts 86 DDI types with a mean accuracy of 92.4% using the DrugBank gold standard DDI dataset covering 192,284 DDIs contributed by 191,878 drug pairs. DeepDDI is used to suggest potential causal mechanisms for the reported ADEs of 9,284 drug pairs, and also predict alternative drug candidates for 62,707 drug pairs having negative health effects. Furthermore, DeepDDI is applied to 3,288,157 drug-food constituent pairs (2,159 approved drugs and 1,523 well-characterized food constituents) to predict DFIs. The effects of 256 food constituents on pharmacological effects of interacting drugs and bioactivities of 149 food constituents are predicted. These results suggest that DeepDDI can provide important information on drug prescription and even dietary suggestions while taking certain drugs and also guidelines during drug development.
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Previde P, Thomas B, Wong M, Mallory EK, Petkovic D, Altman RB, Kulkarni A. GeneDive: A gene interaction search and visualization tool to facilitate precision medicine. PACIFIC SYMPOSIUM ON BIOCOMPUTING. PACIFIC SYMPOSIUM ON BIOCOMPUTING 2018; 23:590-601. [PMID: 29218917 PMCID: PMC5807065] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/02/2022]
Abstract
Obtaining relevant information about gene interactions is critical for understanding disease processes and treatment. With the rise in text mining approaches, the volume of such biomedical data is rapidly increasing, thereby creating a new problem for the users of this data: information overload. A tool for efficient querying and visualization of biomedical data that helps researchers understand the underlying biological mechanisms for diseases and drug responses, and ultimately helps patients, is sorely needed. To this end we have developed GeneDive, a web-based information retrieval, filtering, and visualization tool for large volumes of gene interaction data. GeneDive offers various features and modalities that guide the user through the search process to efficiently reach the information of their interest. GeneDive currently processes over three million gene-gene interactions with response times within a few seconds. For over half of the curated gene sets sourced from four prominent databases, more than 80% of the gene set members are recovered by GeneDive. In the near future, GeneDive will seamlessly accommodate other interaction types, such as gene-drug and gene-disease interactions, thus enabling full exploration of topics such as precision medicine. The GeneDive application and information about its underlying system architecture are available at http://www.genedive.net.
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Affiliation(s)
- Paul Previde
- Department of Computer Science, San Francisco State University, San Francisco, California 94132, U.S.A
| | - Brook Thomas
- Department of Computer Science, San Francisco State University, San Francisco, California 94132, U.S.A
| | - Mike Wong
- Center for Computing for Life Sciences, San Francisco State University, San Francisco, California 94132, U.S.A
| | - Emily K Mallory
- Biomedical Informatics Training Program, Stanford University, Stanford, California 94305, U.S.A
| | - Dragutin Petkovic
- Department of Computer Science, San Francisco State University, San Francisco, California 94132, U.S.A,Center for Computing for Life Sciences, San Francisco State University, San Francisco, California 94132, U.S.A
| | - Russ B Altman
- Department of Bioengineering, Stanford University, Stanford, California 94305, U.S.A,Department of Genetics, Stanford University, Stanford, California 94305, U.S.A,School of Medicine, Stanford University, Stanford, California 94305, U.S.A
| | - Anagha Kulkarni
- Department of Computer Science, San Francisco State University, San Francisco, California 94132, U.S.A,To whom correspondence should be addressed
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Zhang P, Wu H, Chiang C, Wang L, Binkheder S, Wang X, Zeng D, Quinney SK, Li L. Translational Biomedical Informatics and Pharmacometrics Approaches in the Drug Interactions Research. CPT-PHARMACOMETRICS & SYSTEMS PHARMACOLOGY 2017; 7:90-102. [PMID: 29193890 PMCID: PMC5824109 DOI: 10.1002/psp4.12267] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 11/07/2017] [Accepted: 11/08/2017] [Indexed: 12/18/2022]
Abstract
Drug interaction is a leading cause of adverse drug events and a major obstacle for current clinical practice. Pharmacovigilance data mining, pharmacokinetic modeling, and text mining are computation and informatic tools on integrating drug interaction knowledge and generating drug interaction hypothesis. We provide a comprehensive overview of these translational biomedical informatics methodologies with related databases. We hope this review illustrates the complementary nature of these informatic approaches and facilitates the translational drug interaction research.
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Affiliation(s)
- Pengyue Zhang
- Department of Biomedical InformaticsCollege of Medicine, the Ohio State UniversityColumbusOhioUSA
| | - Heng‐Yi Wu
- Department of Biomedical InformaticsCollege of Medicine, the Ohio State UniversityColumbusOhioUSA
| | - Chien‐Wei Chiang
- Department of Biomedical InformaticsCollege of Medicine, the Ohio State UniversityColumbusOhioUSA
| | - Lei Wang
- Department of Biomedical InformaticsCollege of Medicine, the Ohio State UniversityColumbusOhioUSA
- Intelligent Systems and Bioinformatics Institute, College of Automation, Harbin Engineering UniversityHarbinHeilongjiangChina
| | - Samar Binkheder
- Department of Biohealth InformaticsIndiana University School of Informatics and ComputingIndianapolisIndianaUSA
- Medical Informatics Unit, College of Medicine, King Saud UniversityRiyadhSaudi Arabia
| | - Xueying Wang
- Intelligent Systems and Bioinformatics Institute, College of Automation, Harbin Engineering UniversityHarbinHeilongjiangChina
| | - Donglin Zeng
- Department of BiostatisticsUniversity of North Carolina at Chapel HillChapel HillNorth CarolinaUSA
| | - Sara K. Quinney
- Department of Obstetrics and GynecologyIndiana UniversityIndianapolisIndianaUSA
| | - Lang Li
- Department of Biomedical InformaticsCollege of Medicine, the Ohio State UniversityColumbusOhioUSA
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Noor A, Assiri A, Ayvaz S, Clark C, Dumontier M. Drug-drug interaction discovery and demystification using Semantic Web technologies. J Am Med Inform Assoc 2017; 24:556-564. [PMID: 28031284 DOI: 10.1093/jamia/ocw128] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2016] [Accepted: 07/25/2016] [Indexed: 12/27/2022] Open
Abstract
Objective To develop a novel pharmacovigilance inferential framework to infer mechanistic explanations for asserted drug-drug interactions (DDIs) and deduce potential DDIs. Materials and Methods A mechanism-based DDI knowledge base was constructed by integrating knowledge from several existing sources at the pharmacokinetic, pharmacodynamic, pharmacogenetic, and multipathway interaction levels. A query-based framework was then created to utilize this integrated knowledge base in conjunction with 9 inference rules to infer mechanistic explanations for asserted DDIs and deduce potential DDIs. Results The drug-drug interactions discovery and demystification (D3) system achieved an overall 85% recall rate in terms of inferring mechanistic explanations for the DDIs integrated into its knowledge base, while demonstrating a 61% precision rate in terms of the inference or lack of inference of mechanistic explanations for a balanced, randomly selected collection of interacting and noninteracting drug pairs. Discussion The successful demonstration of the D3 system's ability to confirm interactions involving well-studied drugs enhances confidence in its ability to deduce interactions involving less-studied drugs. In its demonstration, the D3 system infers putative explanations for most of its integrated DDIs. Further enhancements to this work in the future might include ranking interaction mechanisms based on likelihood of applicability, determining the likelihood of deduced DDIs, and making the framework publicly available. Conclusion The D3 system provides an early-warning framework for augmenting knowledge of known DDIs and deducing unknown DDIs. It shows promise in suggesting interaction pathways of research and evaluation interest and aiding clinicians in evaluating and adjusting courses of drug therapy.
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Affiliation(s)
- Adeeb Noor
- Faculty of Computing and Information Technology, King Abdul Aziz University, Jeddah, KSA
| | - Abdullah Assiri
- School of Pharmacy, Purdue University, West Lafayette, Indiana, USA.,School of Pharmacy, King Khalid University, Abha, KSA
| | - Serkan Ayvaz
- Department of Computer Engineering, Bahcesehir University, Besiktas, Istanbul 34353, Turkey
| | - Connor Clark
- Unaffiliated Researcher, Mountain View, California, USA
| | - Michel Dumontier
- Stanford Center for Biomedical Informatics Research, Stanford, California
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Taewijit S, Theeramunkong T, Ikeda M. Distant Supervision with Transductive Learning for Adverse Drug Reaction Identification from Electronic Medical Records. JOURNAL OF HEALTHCARE ENGINEERING 2017; 2017:7575280. [PMID: 29090077 PMCID: PMC5635478 DOI: 10.1155/2017/7575280] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/05/2017] [Accepted: 07/19/2017] [Indexed: 11/17/2022]
Abstract
Information extraction and knowledge discovery regarding adverse drug reaction (ADR) from large-scale clinical texts are very useful and needy processes. Two major difficulties of this task are the lack of domain experts for labeling examples and intractable processing of unstructured clinical texts. Even though most previous works have been conducted on these issues by applying semisupervised learning for the former and a word-based approach for the latter, they face with complexity in an acquisition of initial labeled data and ignorance of structured sequence of natural language. In this study, we propose automatic data labeling by distant supervision where knowledge bases are exploited to assign an entity-level relation label for each drug-event pair in texts, and then, we use patterns for characterizing ADR relation. The multiple-instance learning with expectation-maximization method is employed to estimate model parameters. The method applies transductive learning to iteratively reassign a probability of unknown drug-event pair at the training time. By investigating experiments with 50,998 discharge summaries, we evaluate our method by varying large number of parameters, that is, pattern types, pattern-weighting models, and initial and iterative weightings of relations for unlabeled data. Based on evaluations, our proposed method outperforms the word-based feature for NB-EM (iEM), MILR, and TSVM with F1 score of 11.3%, 9.3%, and 6.5% improvement, respectively.
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Affiliation(s)
- Siriwon Taewijit
- The School of Information, Communication and Computer Technologies, Sirindhorn International Institute of Technology, Thammasat University, Pathum Thani 12120, Thailand
- The School of Knowledge Science, Japan Advanced Institute of Science and Technology, Nomi 923-1292, Japan
| | - Thanaruk Theeramunkong
- The School of Information, Communication and Computer Technologies, Sirindhorn International Institute of Technology, Thammasat University, Pathum Thani 12120, Thailand
| | - Mitsuru Ikeda
- The School of Knowledge Science, Japan Advanced Institute of Science and Technology, Nomi 923-1292, Japan
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Renganathan V. Text Mining in Biomedical Domain with Emphasis on Document Clustering. Healthc Inform Res 2017; 23:141-146. [PMID: 28875048 PMCID: PMC5572517 DOI: 10.4258/hir.2017.23.3.141] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2017] [Revised: 07/16/2017] [Accepted: 07/17/2017] [Indexed: 12/19/2022] Open
Abstract
Objectives With the exponential increase in the number of articles published every year in the biomedical domain, there is a need to build automated systems to extract unknown information from the articles published. Text mining techniques enable the extraction of unknown knowledge from unstructured documents. Methods This paper reviews text mining processes in detail and the software tools available to carry out text mining. It also reviews the roles and applications of text mining in the biomedical domain. Results Text mining processes, such as search and retrieval of documents, pre-processing of documents, natural language processing, methods for text clustering, and methods for text classification are described in detail. Conclusions Text mining techniques can facilitate the mining of vast amounts of knowledge on a given topic from published biomedical research articles and draw meaningful conclusions that are not possible otherwise.
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Ferdousi R, Safdari R, Omidi Y. Computational prediction of drug-drug interactions based on drugs functional similarities. J Biomed Inform 2017; 70:54-64. [DOI: 10.1016/j.jbi.2017.04.021] [Citation(s) in RCA: 57] [Impact Index Per Article: 8.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2016] [Revised: 03/18/2017] [Accepted: 04/28/2017] [Indexed: 10/19/2022]
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Krallinger M, Rabal O, Lourenço A, Oyarzabal J, Valencia A. Information Retrieval and Text Mining Technologies for Chemistry. Chem Rev 2017; 117:7673-7761. [PMID: 28475312 DOI: 10.1021/acs.chemrev.6b00851] [Citation(s) in RCA: 111] [Impact Index Per Article: 15.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
Abstract
Efficient access to chemical information contained in scientific literature, patents, technical reports, or the web is a pressing need shared by researchers and patent attorneys from different chemical disciplines. Retrieval of important chemical information in most cases starts with finding relevant documents for a particular chemical compound or family. Targeted retrieval of chemical documents is closely connected to the automatic recognition of chemical entities in the text, which commonly involves the extraction of the entire list of chemicals mentioned in a document, including any associated information. In this Review, we provide a comprehensive and in-depth description of fundamental concepts, technical implementations, and current technologies for meeting these information demands. A strong focus is placed on community challenges addressing systems performance, more particularly CHEMDNER and CHEMDNER patents tasks of BioCreative IV and V, respectively. Considering the growing interest in the construction of automatically annotated chemical knowledge bases that integrate chemical information and biological data, cheminformatics approaches for mapping the extracted chemical names into chemical structures and their subsequent annotation together with text mining applications for linking chemistry with biological information are also presented. Finally, future trends and current challenges are highlighted as a roadmap proposal for research in this emerging field.
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Affiliation(s)
- Martin Krallinger
- Structural Computational Biology Group, Structural Biology and BioComputing Programme, Spanish National Cancer Research Centre , C/Melchor Fernández Almagro 3, Madrid E-28029, Spain
| | - Obdulia Rabal
- Small Molecule Discovery Platform, Molecular Therapeutics Program, Center for Applied Medical Research (CIMA), University of Navarra , Avenida Pio XII 55, Pamplona E-31008, Spain
| | - Anália Lourenço
- ESEI - Department of Computer Science, University of Vigo , Edificio Politécnico, Campus Universitario As Lagoas s/n, Ourense E-32004, Spain.,Centro de Investigaciones Biomédicas (Centro Singular de Investigación de Galicia) , Campus Universitario Lagoas-Marcosende, Vigo E-36310, Spain.,CEB-Centre of Biological Engineering, University of Minho , Campus de Gualtar, Braga 4710-057, Portugal
| | - Julen Oyarzabal
- Small Molecule Discovery Platform, Molecular Therapeutics Program, Center for Applied Medical Research (CIMA), University of Navarra , Avenida Pio XII 55, Pamplona E-31008, Spain
| | - Alfonso Valencia
- Life Science Department, Barcelona Supercomputing Centre (BSC-CNS) , C/Jordi Girona, 29-31, Barcelona E-08034, Spain.,Joint BSC-IRB-CRG Program in Computational Biology, Parc Científic de Barcelona , C/ Baldiri Reixac 10, Barcelona E-08028, Spain.,Institució Catalana de Recerca i Estudis Avançats (ICREA) , Passeig de Lluís Companys 23, Barcelona E-08010, Spain
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Characterization of the mechanism of drug-drug interactions from PubMed using MeSH terms. PLoS One 2017; 12:e0173548. [PMID: 28422961 PMCID: PMC5396881 DOI: 10.1371/journal.pone.0173548] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2016] [Accepted: 02/23/2017] [Indexed: 11/30/2022] Open
Abstract
Identifying drug-drug interaction (DDI) is an important topic for the development of safe pharmaceutical drugs and for the optimization of multidrug regimens for complex diseases such as cancer and HIV. There have been about 150,000 publications on DDIs in PubMed, which is a great resource for DDI studies. In this paper, we introduced an automatic computational method for the systematic analysis of the mechanism of DDIs using MeSH (Medical Subject Headings) terms from PubMed literature. MeSH term is a controlled vocabulary thesaurus developed by the National Library of Medicine for indexing and annotating articles. Our method can effectively identify DDI-relevant MeSH terms such as drugs, proteins and phenomena with high accuracy. The connections among these MeSH terms were investigated by using co-occurrence heatmaps and social network analysis. Our approach can be used to visualize relationships of DDI terms, which has the potential to help users better understand DDIs. As the volume of PubMed records increases, our method for automatic analysis of DDIs from the PubMed database will become more accurate.
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Hameed PN, Verspoor K, Kusljic S, Halgamuge S. Positive-Unlabeled Learning for inferring drug interactions based on heterogeneous attributes. BMC Bioinformatics 2017; 18:140. [PMID: 28249566 PMCID: PMC5333429 DOI: 10.1186/s12859-017-1546-7] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2016] [Accepted: 02/13/2017] [Indexed: 12/16/2022] Open
Abstract
BACKGROUND Investigating and understanding drug-drug interactions (DDIs) is important in improving the effectiveness of clinical care. DDIs can occur when two or more drugs are administered together. Experimentally based DDI detection methods require a large cost and time. Hence, there is a great interest in developing efficient and useful computational methods for inferring potential DDIs. Standard binary classifiers require both positives and negatives for training. In a DDI context, drug pairs that are known to interact can serve as positives for predictive methods. But, the negatives or drug pairs that have been confirmed to have no interaction are scarce. To address this lack of negatives, we introduce a Positive-Unlabeled Learning method for inferring potential DDIs. RESULTS The proposed method consists of three steps: i) application of Growing Self Organizing Maps to infer negatives from the unlabeled dataset; ii) using a pairwise similarity function to quantify the overlap between individual features of drugs and iii) using support vector machine classifier for inferring DDIs. We obtained 6036 DDIs from DrugBank database. Using the proposed approach, we inferred 589 drug pairs that are likely to not interact with each other; these drug pairs are used as representative data for the negative class in binary classification for DDI prediction. Moreover, we classify the predicted DDIs as Cytochrome P450 (CYP) enzyme-Dependent and CYP-Independent interactions invoking their locations on the Growing Self Organizing Map, due to the particular importance of these enzymes in clinically significant interaction effects. Further, we provide a case study on three predicted CYP-Dependent DDIs to evaluate the clinical relevance of this study. CONCLUSION Our proposed approach showed an absolute improvement in F1-score of 14 and 38% in comparison to the method that randomly selects unlabeled data points as likely negatives, depending on the choice of similarity function. We inferred 5300 possible CYP-Dependent DDIs and 592 CYP-Independent DDIs with the highest posterior probabilities. Our discoveries can be used to improve clinical care as well as the research outcomes of drug development.
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Affiliation(s)
- Pathima Nusrath Hameed
- Department of Mechanical Engineering, University of Melbourne, Parkville, Melbourne, 3010, Australia. .,Data61, Victoria Research Lab, West Melbourne, 3003, Australia. .,Department of Computer Science, University of Ruhuna, Matara, 81000, Sri Lanka.
| | - Karin Verspoor
- Department of Computing and Information Systems, University of Melbourne, Parkville, Melbourne, 3010, Australia
| | - Snezana Kusljic
- Department of Nursing, University of Melbourne, Parkville, Melbourne, 3010, Australia.,The Florey Institute of Neuroscience and Mental Health, University of Melbourne, Parkville, Melbourne, 3010, Australia
| | - Saman Halgamuge
- Research School of Engineering, College of Engineering and Computer Science, The Australian National University, Canberra, 2601, ACT, Australia
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Yi Z, Li S, Yu J, Tan Y, Wu Q, Yuan H, Wang T. Drug-Drug Interaction Extraction via Recurrent Neural Network with Multiple Attention Layers. ADVANCED DATA MINING AND APPLICATIONS 2017. [DOI: 10.1007/978-3-319-69179-4_39] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
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Sernadela P, Oliveira JL. A semantic-based workflow for biomedical literature annotation. Database (Oxford) 2017; 2017:4635750. [PMID: 29220478 PMCID: PMC5691355 DOI: 10.1093/database/bax088] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2017] [Revised: 10/02/2017] [Accepted: 10/30/2017] [Indexed: 11/12/2022]
Abstract
Computational annotation of textual information has taken on an important role in knowledge extraction from the biomedical literature, since most of the relevant information from scientific findings is still maintained in text format. In this endeavour, annotation tools can assist in the identification of biomedical concepts and their relationships, providing faster reading and curation processes, with reduced costs. However, the separate usage of distinct annotation systems results in highly heterogeneous data, as it is difficult to efficiently combine and exchange this valuable asset. Moreover, despite the existence of several annotation formats, there is no unified way to integrate miscellaneous annotation outcomes into a reusable, sharable and searchable structure. Taking up this challenge, we present a modular architecture for textual information integration using semantic web features and services. The solution described allows the migration of curation data into a common model, providing a suitable transition process in which multiple annotation data can be integrated and enriched, with the possibility of being shared, compared and reused across semantic knowledge bases.
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Affiliation(s)
- Pedro Sernadela
- University of Aveiro, DETI/IEETA, University of Aveiro, Campus Universitário de Santiago, 3810-193 Aveiro, Portugal
| | - José Luís Oliveira
- University of Aveiro, DETI/IEETA, University of Aveiro, Campus Universitário de Santiago, 3810-193 Aveiro, Portugal
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Döring K, Grüning BA, Telukunta KK, Thomas P, Günther S. PubMedPortable: A Framework for Supporting the Development of Text Mining Applications. PLoS One 2016; 11:e0163794. [PMID: 27706202 PMCID: PMC5051953 DOI: 10.1371/journal.pone.0163794] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2016] [Accepted: 09/14/2016] [Indexed: 11/18/2022] Open
Abstract
Information extraction from biomedical literature is continuously growing in scope and importance. Many tools exist that perform named entity recognition, e.g. of proteins, chemical compounds, and diseases. Furthermore, several approaches deal with the extraction of relations between identified entities. The BioCreative community supports these developments with yearly open challenges, which led to a standardised XML text annotation format called BioC. PubMed provides access to the largest open biomedical literature repository, but there is no unified way of connecting its data to natural language processing tools. Therefore, an appropriate data environment is needed as a basis to combine different software solutions and to develop customised text mining applications. PubMedPortable builds a relational database and a full text index on PubMed citations. It can be applied either to the complete PubMed data set or an arbitrary subset of downloaded PubMed XML files. The software provides the infrastructure to combine stand-alone applications by exporting different data formats, e.g. BioC. The presented workflows show how to use PubMedPortable to retrieve, store, and analyse a disease-specific data set. The provided use cases are well documented in the PubMedPortable wiki. The open-source software library is small, easy to use, and scalable to the user's system requirements. It is freely available for Linux on the web at https://github.com/KerstenDoering/PubMedPortable and for other operating systems as a virtual container. The approach was tested extensively and applied successfully in several projects.
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Affiliation(s)
- Kersten Döring
- Pharmaceutical Bioinformatics, Institute of Pharmaceutical Sciences, Albert-Ludwigs University, 79104 Freiburg, Germany
| | - Björn A. Grüning
- Bioinformatics, Institute of Computer Science, Albert-Ludwigs University, 79110 Freiburg, Germany
| | - Kiran K. Telukunta
- Bioinformatics, Institute of Computer Science, Albert-Ludwigs University, 79110 Freiburg, Germany
| | - Philippe Thomas
- Language Technology Lab, German Research Center for Artificial Intelligence, DFKI GmbH, 10559 Berlin, Germany
| | - Stefan Günther
- Pharmaceutical Bioinformatics, Institute of Pharmaceutical Sciences, Albert-Ludwigs University, 79104 Freiburg, Germany
- * E-mail:
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Zhang Y, Wu HY, Xu J, Wang J, Soysal E, Li L, Xu H. Leveraging syntactic and semantic graph kernels to extract pharmacokinetic drug drug interactions from biomedical literature. BMC SYSTEMS BIOLOGY 2016; 10 Suppl 3:67. [PMID: 27585838 PMCID: PMC5009562 DOI: 10.1186/s12918-016-0311-2] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/13/2022]
Abstract
Background Information about drug–drug interactions (DDIs) supported by scientific evidence is crucial for establishing computational knowledge bases for applications like pharmacovigilance. Since new reports of DDIs are rapidly accumulating in the scientific literature, text-mining techniques for automatic DDI extraction are critical. We propose a novel approach for automated pharmacokinetic (PK) DDI detection that incorporates syntactic and semantic information into graph kernels, to address the problem of sparseness associated with syntactic-structural approaches. First, we used a novel all-path graph kernel using shallow semantic representation of sentences. Next, we statistically integrated fine-granular semantic classes into the dependency and shallow semantic graphs. Results When evaluated on the PK DDI corpus, our approach significantly outperformed the original all-path graph kernel that is based on dependency structure. Our system that combined dependency graph kernel with semantic classes achieved the best F-scores of 81.94 % for in vivo PK DDIs and 69.34 % for in vitro PK DDIs, respectively. Further, combining shallow semantic graph kernel with semantic classes achieved the highest precisions of 84.88 % for in vivo PK DDIs and 74.83 % for in vitro PK DDIs, respectively. Conclusions We presented a graph kernel based approach to combine syntactic and semantic information for extracting pharmacokinetic DDIs from Biomedical Literature. Experimental results showed that our proposed approach could extract PK DDIs from literature effectively, which significantly enhanced the performance of the original all-path graph kernel based on dependency structure.
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Affiliation(s)
- Yaoyun Zhang
- School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, TX, 77030, USA
| | - Heng-Yi Wu
- School of Medicine, Indiana University, Indianapolis, IN, 46202, USA
| | - Jun Xu
- School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, TX, 77030, USA
| | - Jingqi Wang
- School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, TX, 77030, USA
| | - Ergin Soysal
- School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, TX, 77030, USA
| | - Lang Li
- School of Medicine, Indiana University, Indianapolis, IN, 46202, USA.
| | - Hua Xu
- School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, TX, 77030, USA.
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Generalized enrichment analysis improves the detection of adverse drug events from the biomedical literature. BMC Bioinformatics 2016; 17:250. [PMID: 27333889 PMCID: PMC4918084 DOI: 10.1186/s12859-016-1080-z] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2015] [Accepted: 05/11/2016] [Indexed: 01/12/2023] Open
Abstract
Background Identification of associations between marketed drugs and adverse events from the biomedical literature assists drug safety monitoring efforts. Assessing the significance of such literature-derived associations and determining the granularity at which they should be captured remains a challenge. Here, we assess how defining a selection of adverse event terms from MeSH, based on information content, can improve the detection of adverse events for drugs and drug classes. Results We analyze a set of 105,354 candidate drug adverse event pairs extracted from article indexes in MEDLINE. First, we harmonize extracted adverse event terms by aggregating them into higher-level MeSH terms based on the terms’ information content. Then, we determine statistical enrichment of adverse events associated with drug and drug classes using a conditional hypergeometric test that adjusts for dependencies among associated terms. We compare our results with methods based on disproportionality analysis (proportional reporting ratio, PRR) and quantify the improvement in signal detection with our generalized enrichment analysis (GEA) approach using a gold standard of drug-adverse event associations spanning 174 drugs and four events. For single drugs, the best GEA method (Precision: .92/Recall: .71/F1-measure: .80) outperforms the best PRR based method (.69/.69/.69) on all four adverse event outcomes in our gold standard. For drug classes, our GEA performs similarly (.85/.69/.74) when increasing the level of abstraction for adverse event terms. Finally, on examining the 1609 individual drugs in our MEDLINE set, which map to chemical substances in ATC, we find signals for 1379 drugs (10,122 unique adverse event associations) on applying GEA with p < 0.005. Conclusions We present an approach based on generalized enrichment analysis that can be used to detect associations between drugs, drug classes and adverse events at a given level of granularity, at the same time correcting for known dependencies among events. Our study demonstrates the use of GEA, and the importance of choosing appropriate abstraction levels to complement current drug safety methods. We provide an R package for exploration of alternative abstraction levels of adverse event terms based on information content. Electronic supplementary material The online version of this article (doi:10.1186/s12859-016-1080-z) contains supplementary material, which is available to authorized users.
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Lim KMK, Li C, Chng KR, Nagarajan N. @MInter: automated text-mining of microbial interactions. Bioinformatics 2016; 32:2981-7. [PMID: 27312413 DOI: 10.1093/bioinformatics/btw357] [Citation(s) in RCA: 28] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2016] [Accepted: 05/31/2016] [Indexed: 11/13/2022] Open
Abstract
MOTIVATION Microbial consortia are frequently defined by numerous interactions within the community that are key to understanding their function. While microbial interactions have been extensively studied experimentally, information regarding them is dispersed in the scientific literature. As manual collation is an infeasible option, automated data processing tools are needed to make this information easily accessible. RESULTS We present @MInter, an automated information extraction system based on Support Vector Machines to analyze paper abstracts and infer microbial interactions. @MInter was trained and tested on a manually curated gold standard dataset of 735 species interactions and 3917 annotated abstracts, constructed as part of this study. Cross-validation analysis showed that @MInter was able to detect abstracts pertaining to one or more microbial interactions with high specificity (specificity = 95%, AUC = 0.97). Despite challenges in identifying specific microbial interactions in an abstract (interaction level recall = 95%, precision = 25%), @MInter was shown to reduce annotator workload 13-fold compared to alternate approaches. Applying @MInter to 175 bacterial species abundant on human skin, we identified a network of 357 literature-reported microbial interactions, demonstrating its utility for the study of microbial communities. AVAILABILITY AND IMPLEMENTATION @MInter is freely available at https://github.com/CSB5/atminter CONTACT nagarajann@gis.a-star.edu.sg SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Kun Ming Kenneth Lim
- Computational and Systems Biology, Genome Institute of Singapore, Singapore 138672, Singapore Computational Biology Program, Faculty of Science
| | - Chenhao Li
- Computational and Systems Biology, Genome Institute of Singapore, Singapore 138672, Singapore Department of Computer Science, National University of Singapore, Singapore, Singapore
| | - Kern Rei Chng
- Computational and Systems Biology, Genome Institute of Singapore, Singapore 138672, Singapore
| | - Niranjan Nagarajan
- Computational and Systems Biology, Genome Institute of Singapore, Singapore 138672, Singapore Department of Computer Science, National University of Singapore, Singapore, Singapore
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Zhang Y, Wu HY, Du J, Xu J, Wang J, Tao C, Li L, Xu H. Extracting drug-enzyme relation from literature as evidence for drug drug interaction. J Biomed Semantics 2016; 7:11. [PMID: 26955465 PMCID: PMC4780188 DOI: 10.1186/s13326-016-0052-6] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2015] [Accepted: 02/11/2016] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Information about drug-drug interactions (DDIs) is crucial for computational applications such as pharmacovigilance and drug repurposing. However, existing sources of DDIs have the problems of low coverage, low accuracy and low agreement. One common type of DDIs is related to the mechanism of drug metabolism: a DDI relation may be caused by different interactions (e.g., substrate, inhibit) between drugs and enzymes in the drug metabolism process. Thus, information from drug enzyme interactions (DEIs) serves as important supportive evidence for DDIs. Further, potential DDIs present implicitly could be detected by inference and reasoning based on DEIs. METHODS In this article, we propose a hybrid approach to combining machine learning algorithm with trigger words and syntactic patterns, for DEI relation extraction from biomedical literature. The extracted DEI relations are used for reasoning to infer potential DDI relations, based on a defined drug-enzyme ontology incorporating biological knowledge. RESULTS Evaluation results demonstrate that the performance of DEI relation extraction is promising, with an F-measure of 84.97% on the in vivo dataset and 65.58% on the in vitro dataset. Further, the inferred DDIs achieved a precision of 83.19% on the in vivo dataset and 70.94% on the in vitro dataset, respectively. A further examination showed that the overlaps between our inferred DDIs and those present in DrugBank were 42.02% on the in vivo dataset and 19.23 % on the in vitro dataset, respectively. CONCLUSIONS This paper proposed an effective approach to extract DEI relations from biomedical literature. Potential DDIs not present in existing knowledge bases were then inferred based on the extracted DEIs, demonstrating the capability of the proposed approach to detect DDIs with scientific evidence for pharmacovigilance and drug repurposing applications.
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Affiliation(s)
- Yaoyun Zhang
- School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, TX USA
| | - Heng-Yi Wu
- School of Medicine, Indiana University, Indianapolis, IN USA
| | - Jingcheng Du
- School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, TX USA
| | - Jun Xu
- School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, TX USA
| | - Jingqi Wang
- School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, TX USA
| | - Cui Tao
- School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, TX USA
| | - Lang Li
- School of Medicine, Indiana University, Indianapolis, IN USA
| | - Hua Xu
- School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, TX USA
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Zakharov AV, Varlamova EV, Lagunin AA, Dmitriev AV, Muratov EN, Fourches D, Kuz'min VE, Poroikov VV, Tropsha A, Nicklaus MC. QSAR Modeling and Prediction of Drug-Drug Interactions. Mol Pharm 2016; 13:545-56. [PMID: 26669717 DOI: 10.1021/acs.molpharmaceut.5b00762] [Citation(s) in RCA: 43] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/26/2023]
Abstract
Severe adverse drug reactions (ADRs) are the fourth leading cause of fatality in the U.S. with more than 100,000 deaths per year. As up to 30% of all ADRs are believed to be caused by drug-drug interactions (DDIs), typically mediated by cytochrome P450s, possibilities to predict DDIs from existing knowledge are important. We collected data from public sources on 1485, 2628, 4371, and 27,966 possible DDIs mediated by four cytochrome P450 isoforms 1A2, 2C9, 2D6, and 3A4 for 55, 73, 94, and 237 drugs, respectively. For each of these data sets, we developed and validated QSAR models for the prediction of DDIs. As a unique feature of our approach, the interacting drug pairs were represented as binary chemical mixtures in a 1:1 ratio. We used two types of chemical descriptors: quantitative neighborhoods of atoms (QNA) and simplex descriptors. Radial basis functions with self-consistent regression (RBF-SCR) and random forest (RF) were utilized to build QSAR models predicting the likelihood of DDIs for any pair of drug molecules. Our models showed balanced accuracy of 72-79% for the external test sets with a coverage of 81.36-100% when a conservative threshold for the model's applicability domain was applied. We generated virtually all possible binary combinations of marketed drugs and employed our models to identify drug pairs predicted to be instances of DDI. More than 4500 of these predicted DDIs that were not found in our training sets were confirmed by data from the DrugBank database.
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Affiliation(s)
- Alexey V Zakharov
- Computer-Aided Drug Design Group, Chemical Biology Laboratory, Center for Cancer Research, National Cancer Institute, National Institutes of Health, DHHS, NCI-Frederick , 376 Boyles Street, Frederick, Maryland 21702, United States
| | - Ekaterina V Varlamova
- Department of Molecular Structure and Cheminformatics, A.V. Bogatsky Physical Chemical Institute, National Academy of Sciences of Ukraine , Lustdorfskaya Doroga 86, Odessa 65080, Ukraine.,Chemical-Technological Department, Odessa National Polytechnic University , 1 Shevchenko Ave, Odessa 65000, Ukraine
| | - Alexey A Lagunin
- Institute of Biochemical Chemistry , 10/8, Pogodinskaya street, 119121 Moscow, Russia.,Medico-Biological Department, Pirogov Russian National Research Medical University , Ostrovitianov str. 1, Moscow 117997, Russia
| | - Alexander V Dmitriev
- Institute of Biochemical Chemistry , 10/8, Pogodinskaya street, 119121 Moscow, Russia
| | - Eugene N Muratov
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina , Beard Hall 301, CB#7568, Chapel Hill, North Carolina 27599, United States
| | - Denis Fourches
- Department of Chemistry, Bioinformatics Research Center, North Carolina State University , Raleigh, North Carolina 27695, United States
| | - Victor E Kuz'min
- Department of Molecular Structure and Cheminformatics, A.V. Bogatsky Physical Chemical Institute, National Academy of Sciences of Ukraine , Lustdorfskaya Doroga 86, Odessa 65080, Ukraine
| | - Vladimir V Poroikov
- Institute of Biochemical Chemistry , 10/8, Pogodinskaya street, 119121 Moscow, Russia
| | - Alexander Tropsha
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina , Beard Hall 301, CB#7568, Chapel Hill, North Carolina 27599, United States
| | - Marc C Nicklaus
- Computer-Aided Drug Design Group, Chemical Biology Laboratory, Center for Cancer Research, National Cancer Institute, National Institutes of Health, DHHS, NCI-Frederick , 376 Boyles Street, Frederick, Maryland 21702, United States
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Lu Y, Shen D, Pietsch M, Nagar C, Fadli Z, Huang H, Tu YC, Cheng F. A novel algorithm for analyzing drug-drug interactions from MEDLINE literature. Sci Rep 2015; 5:17357. [PMID: 26612138 PMCID: PMC4661569 DOI: 10.1038/srep17357] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2015] [Accepted: 10/14/2015] [Indexed: 12/21/2022] Open
Abstract
Drug–drug interaction (DDI) is becoming a serious clinical safety issue as the use of multiple medications becomes more common. Searching the MEDLINE database for journal articles related to DDI produces over 330,000 results. It is impossible to read and summarize these references manually. As the volume of biomedical reference in the MEDLINE database continues to expand at a rapid pace, automatic identification of DDIs from literature is becoming increasingly important. In this article, we present a random-sampling-based statistical algorithm to identify possible DDIs and the underlying mechanism from the substances field of MEDLINE records. The substances terms are essentially carriers of compound (including protein) information in a MEDLINE record. Four case studies on warfarin, ibuprofen, furosemide and sertraline implied that our method was able to rank possible DDIs with high accuracy (90.0% for warfarin, 83.3% for ibuprofen, 70.0% for furosemide and 100% for sertraline in the top 10% of a list of compounds ranked by p-value). A social network analysis of substance terms was also performed to construct networks between proteins and drug pairs to elucidate how the two drugs could interact.
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Affiliation(s)
- Yin Lu
- Department of Pharmaceutical Science, College of Pharmacy, University of South Florida, Tampa, FL, 33612, USA
| | - Dan Shen
- Department of Mathematics &Statistics, University of South Florida, Tampa, FL, 33612, USA
| | - Maxwell Pietsch
- Department of Computer Science and Engineering, University of South Florida, Tampa, FL, 33612, USA
| | - Chetan Nagar
- Department of Pharmaceutical Science, College of Pharmacy, University of South Florida, Tampa, FL, 33612, USA
| | - Zayd Fadli
- College of Medicine, Syrian private university, Damascus, 0100, Syria
| | - Hong Huang
- School of Information, University of South Florida, Tampa, FL, 33612, USA
| | - Yi-Cheng Tu
- Department of Computer Science and Engineering, University of South Florida, Tampa, FL, 33612, USA
| | - Feng Cheng
- Department of Pharmaceutical Science, College of Pharmacy, University of South Florida, Tampa, FL, 33612, USA.,Department of Epidemiology and Biostatistics, College of Public Health, University of South Florida, Tampa 33612, USA
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Zhao M, Chen Y, Qu D, Qu H. METSP: a maximum-entropy classifier based text mining tool for transporter-substrate identification with semistructured text. BIOMED RESEARCH INTERNATIONAL 2015; 2015:254838. [PMID: 26495291 PMCID: PMC4606149 DOI: 10.1155/2015/254838] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/13/2015] [Accepted: 06/21/2015] [Indexed: 01/16/2023]
Abstract
The substrates of a transporter are not only useful for inferring function of the transporter, but also important to discover compound-compound interaction and to reconstruct metabolic pathway. Though plenty of data has been accumulated with the developing of new technologies such as in vitro transporter assays, the search for substrates of transporters is far from complete. In this article, we introduce METSP, a maximum-entropy classifier devoted to retrieve transporter-substrate pairs (TSPs) from semistructured text. Based on the high quality annotation from UniProt, METSP achieves high precision and recall in cross-validation experiments. When METSP is applied to 182,829 human transporter annotation sentences in UniProt, it identifies 3942 sentences with transporter and compound information. Finally, 1547 confidential human TSPs are identified for further manual curation, among which 58.37% pairs with novel substrates not annotated in public transporter databases. METSP is the first efficient tool to extract TSPs from semistructured annotation text in UniProt. This tool can help to determine the precise substrates and drugs of transporters, thus facilitating drug-target prediction, metabolic network reconstruction, and literature classification.
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Affiliation(s)
- Min Zhao
- School of Engineering, Faculty of Science, Health, Education and Engineering, University of the Sunshine Coast, Maroochydore DC, QLD 4558, Australia
| | - Yanming Chen
- School of Computer Science & Technology, Beijing Institute of Technology, Beijing 100081, China
| | - Dacheng Qu
- School of Computer Science & Technology, Beijing Institute of Technology, Beijing 100081, China
| | - Hong Qu
- Center for Bioinformatics, State Key Laboratory of Protein and Plant Gene Research, College of Life Sciences, Peking University, Beijing 100871, China
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