310 research outputs found

    Evaluation of linear classifiers on articles containing pharmacokinetic evidence of drug-drug interactions

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    Background. Drug-drug interaction (DDI) is a major cause of morbidity and mortality. [...] Biomedical literature mining can aid DDI research by extracting relevant DDI signals from either the published literature or large clinical databases. However, though drug interaction is an ideal area for translational research, the inclusion of literature mining methodologies in DDI workflows is still very preliminary. One area that can benefit from literature mining is the automatic identification of a large number of potential DDIs, whose pharmacological mechanisms and clinical significance can then be studied via in vitro pharmacology and in populo pharmaco-epidemiology. Experiments. We implemented a set of classifiers for identifying published articles relevant to experimental pharmacokinetic DDI evidence. These documents are important for identifying causal mechanisms behind putative drug-drug interactions, an important step in the extraction of large numbers of potential DDIs. We evaluate performance of several linear classifiers on PubMed abstracts, under different feature transformation and dimensionality reduction methods. In addition, we investigate the performance benefits of including various publicly-available named entity recognition features, as well as a set of internally-developed pharmacokinetic dictionaries. Results. We found that several classifiers performed well in distinguishing relevant and irrelevant abstracts. We found that the combination of unigram and bigram textual features gave better performance than unigram features alone, and also that normalization transforms that adjusted for feature frequency and document length improved classification. For some classifiers, such as linear discriminant analysis (LDA), proper dimensionality reduction had a large impact on performance. Finally, the inclusion of NER features and dictionaries was found not to help classification.Comment: Pacific Symposium on Biocomputing, 201

    Evaluation of linear classifiers on articles containing pharmacokinetic evidence of drug-drug interactions

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    Background. Drug-drug interaction (DDI) is a major cause of morbidity and mortality. DDI research includes the study of different aspects of drug interactions, from in vitro pharmacology, which deals with drug interaction mechanisms, to pharmaco-epidemiology, which investigates the effects of DDI on drug efficacy and adverse drug reactions. Biomedical literature mining can aid both kinds of approaches by extracting relevant DDI signals from either the published literature or large clinical databases. However, though drug interaction is an ideal area for translational research, the inclusion of literature mining methodologies in DDI workflows is still very preliminary. One area that can benefit from literature mining is the automatic identification of a large number of potential DDIs, whose pharmacological mechanisms and clinical significance can then be studied via in vitro pharmacology and in populo pharmaco-epidemiology. Experiments. We implemented a set of classifiers for identifying published articles relevant to experimental pharmacokinetic DDI evidence. These documents are important for identifying causal mechanisms behind putative drug-drug interactions, an important step in the extraction of large numbers of potential DDIs. We evaluate performance of several linear classifiers on PubMed abstracts, under different feature transformation and dimensionality reduction methods. In addition, we investigate the performance benefits of including various publicly-available named entity recognition features, as well as a set of internally-developed pharmacokinetic dictionaries. Results. We found that several classifiers performed well in distinguishing relevant and irrelevant abstracts. We found that the combination of unigram and bigram textual features gave better performance than unigram features alone, and also that normalization transforms that adjusted for feature frequency and document length improved classification. For some classifiers, such as linear discriminant analysis (LDA), proper dimensionality reduction had a large impact on performance. Finally, the inclusion of NER features and dictionaries was found not to help classification.IU -Indiana Universit

    Extraction of pharmacokinetic evidence of drug-drug interactions from the literature

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    Drug-drug interaction (DDI) is a major cause of morbidity and mortality and a subject of intense scientific interest. Biomedical literature mining can aid DDI research by extracting evidence for large numbers of potential interactions from published literature and clinical databases. Though DDI is investigated in domains ranging in scale from intracellular biochemistry to human populations, literature mining has not been used to extract specific types of experimental evidence, which are reported differently for distinct experimental goals. We focus on pharmacokinetic evidence for DDI, essential for identifying causal mechanisms of putative interactions and as input for further pharmacological and pharmacoepidemiology investigations. We used manually curated corpora of PubMed abstracts and annotated sentences to evaluate the efficacy of literature mining on two tasks: first, identifying PubMed abstracts containing pharmacokinetic evidence of DDIs; second, extracting sentences containing such evidence from abstracts. We implemented a text mining pipeline and evaluated it using several linear classifiers and a variety of feature transforms. The most important textual features in the abstract and sentence classification tasks were analyzed. We also investigated the performance benefits of using features derived from PubMed metadata fields, various publicly available named entity recognizers, and pharmacokinetic dictionaries. Several classifiers performed very well in distinguishing relevant and irrelevant abstracts (reaching F10.93, MCC0.74, iAUC0.99) and sentences (F10.76, MCC0.65, iAUC0.83). We found that word bigram features were important for achieving optimal classifier performance and that features derived from Medical Subject Headings (MeSH) terms significantly improved abstract classification. We also found that some drug-related named entity recognition tools and dictionaries led to slight but significant improvements, especially in classification of evidence sentences. Based on our thorough analysis of classifiers and feature transforms and the high classification performance achieved, we demonstrate that literature mining can aid DDI discovery by supporting automatic extraction of specific types of experimental evidence.National Institutes of Health, National Library of Medicine Program, grant 01LM011945-01 "BLR: Evidence-based Drug-Interaction Discovery: In-Vivo, In-Vitro and Clinical," a grant from the Indiana University Collaborative Research Program 2013, "Drug-Drug Interaction Prediction from Large-scale Mining of Literature and Patient Records," as well as a grant from the joint program between the Fundação Luso-Americana para o Desenvolvimento (Portugal) and National Science Foundation (USA), 2012-2014, "Network Mining For Gene Regulation And Biochemical Signaling.

    BICEPP: an example-based statistical text mining method for predicting the binary characteristics of drugs

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    <p>Abstract</p> <p>Background</p> <p>The identification of drug characteristics is a clinically important task, but it requires much expert knowledge and consumes substantial resources. We have developed a statistical text-mining approach (BInary Characteristics Extractor and biomedical Properties Predictor: BICEPP) to help experts screen drugs that may have important clinical characteristics of interest.</p> <p>Results</p> <p>BICEPP first retrieves MEDLINE abstracts containing drug names, then selects tokens that best predict the list of drugs which represents the characteristic of interest. Machine learning is then used to classify drugs using a document frequency-based measure. Evaluation experiments were performed to validate BICEPP's performance on 484 characteristics of 857 drugs, identified from the Australian Medicines Handbook (AMH) and the PharmacoKinetic Interaction Screening (PKIS) database. Stratified cross-validations revealed that BICEPP was able to classify drugs into all 20 major therapeutic classes (100%) and 157 (of 197) minor drug classes (80%) with areas under the receiver operating characteristic curve (AUC) > 0.80. Similarly, AUC > 0.80 could be obtained in the classification of 173 (of 238) adverse events (73%), up to 12 (of 15) groups of clinically significant cytochrome P450 enzyme (CYP) inducers or inhibitors (80%), and up to 11 (of 14) groups of narrow therapeutic index drugs (79%). Interestingly, it was observed that the keywords used to describe a drug characteristic were not necessarily the most predictive ones for the classification task.</p> <p>Conclusions</p> <p>BICEPP has sufficient classification power to automatically distinguish a wide range of clinical properties of drugs. This may be used in pharmacovigilance applications to assist with rapid screening of large drug databases to identify important characteristics for further evaluation.</p

    Extraction and Classification of Drug-Drug Interaction from Biomedical Text Using a Two-Stage Classifier

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    One of the critical causes of medical errors is Drug-Drug interaction (DDI), which occurs when one drug increases or decreases the effect of another drug. We propose a machine learning system to extract and classify drug-drug interactions from the biomedical literature, using the annotated corpus from the DDIExtraction-2013 shared task challenge. Our approach applies a two-stage classifier to handle the highly unbalanced class distribution in the corpus. The first stage is designed for binary classification of drug pairs as interacting or non-interacting, and the second stage for further classification of interacting pairs into one of four interacting types: advise, effect, mechanism, and int. To find the set of best features for classification, we explored many features, including stemmed words, bigrams, part of speech tags, verb lists, parse tree information, mutual information, and similarity measures, among others. As the system faced two different classification tasks, binary and multi-class, we also explored various classifiers in each stage. Our results show that the best performing classifier in both stages was Support Vector Machines, and the best performing features were 1000 top informative words and part of speech tags between two main drugs. We obtained an F-Measure of 0.64, showing a 12% improvement over our submitted system to the DDIExtraction 2013 competition

    Structuring the Unstructured: Unlocking pharmacokinetic data from journals with Natural Language Processing

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    The development of a new drug is an increasingly expensive and inefficient process. Many drug candidates are discarded due to pharmacokinetic (PK) complications detected at clinical phases. It is critical to accurately estimate the PK parameters of new drugs before being tested in humans since they will determine their efficacy and safety outcomes. Preclinical predictions of PK parameters are largely based on prior knowledge from other compounds, but much of this potentially valuable data is currently locked in the format of scientific papers. With an ever-increasing amount of scientific literature, automated systems are essential to exploit this resource efficiently. Developing text mining systems that can structure PK literature is critical to improving the drug development pipeline. This thesis studied the development and application of text mining resources to accelerate the curation of PK databases. Specifically, the development of novel corpora and suitable natural language processing architectures in the PK domain were addressed. The work presented focused on machine learning approaches that can model the high diversity of PK studies, parameter mentions, numerical measurements, units, and contextual information reported across the literature. Additionally, architectures and training approaches that could efficiently deal with the scarcity of annotated examples were explored. The chapters of this thesis tackle the development of suitable models and corpora to (1) retrieve PK documents, (2) recognise PK parameter mentions, (3) link PK entities to a knowledge base and (4) extract relations between parameter mentions, estimated measurements, units and other contextual information. Finally, the last chapter of this thesis studied the feasibility of the whole extraction pipeline to accelerate tasks in drug development research. The results from this thesis exhibited the potential of text mining approaches to automatically generate PK databases that can aid researchers in the field and ultimately accelerate the drug development pipeline. Additionally, the thesis presented contributions to biomedical natural language processing by developing suitable architectures and corpora for multiple tasks, tackling novel entities and relations within the PK domain

    Translational drug interaction study using text mining technology

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    Indiana University-Purdue University Indianapolis (IUPUI)Drug-Drug Interaction (DDI) is one of the major causes of adverse drug reaction (ADR) and has been demonstrated to threat public health. It causes an estimated 195,000 hospitalizations and 74,000 emergency room visits each year in the USA alone. Current DDI research aims to investigate different scopes of drug interactions: molecular level of pharmacogenetics interaction (PG), pharmacokinetics interaction (PK), and clinical pharmacodynamics consequences (PD). All three types of experiments are important, but they are playing different roles for DDI research. As diverse disciplines and varied studies are involved, interaction evidence is often not available cross all three types of evidence, which create knowledge gaps and these gaps hinder both DDI and pharmacogenetics research. In this dissertation, we proposed to distinguish the three types of DDI evidence (in vitro PK, in vivo PK, and clinical PD studies) and identify all knowledge gaps in experimental evidence for them. This is a collective intelligence effort, whereby a text mining tool will be developed for the large-scale mining and analysis of drug-interaction information such that it can be applied to retrieve, categorize, and extract the information of DDI from published literature available on PubMed. To this end, three tasks will be done in this research work: First, the needed lexica, ontology, and corpora for distinguishing three different types of studies were prepared. Despite the lexica prepared in this work, a comprehensive dictionary for drug metabolites or reaction, which is critical to in vitro PK study, is still lacking in pubic databases. Thus, second, a name entity recognition tool will be proposed to identify drug metabolites and reaction in free text. Third, text mining tools for retrieving DDI articles and extracting DDI evidence are developed. In this work, the knowledge gaps cross all three types of DDI evidence can be identified and the gaps between knowledge of molecular mechanisms underlying DDI and their clinical consequences can be closed with the result of DDI prediction using the retrieved drug gene interaction information such that we can exemplify how the tools and methods can advance DDI pharmacogenetics research.2 year

    Construction of antimicrobial peptide-drug combination networks from scientific literature based on a semi-automated curation workflow

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    Considerable research efforts are being invested in the development of novel antimicrobial therapies effective against the growing number of multi-drug resistant (MDR) pathogens. Notably, the combination of different agents is increasingly explored as means to exploit and improve individual agent actions while minimising microorganism resistance. Although there are several databases on antimicrobial agents, scientific literature is the primary source of information on experimental antimicrobial combination testing. This work presents a semi-automated database curation workflow that supports the mining of scientific literature and enables the reconstruction of recently documented antimicrobial combinations. Currently, the database contains data on antimicrobial combinations that have been experimentally tested against Pseudomonas aeruginosa, Staphylococcus aureus, Escherichia coli, Listeria monocytogenes and Candida albicans, which are prominent pathogenic organisms and are well-known for their wide and growing resistance to conventional antimicrobials. Researchers are able to explore the experimental results for a single organism or across organisms. Likewise, researchers may look into indirect network associations and identify new potential combinations to be tested. The database is available without charges. Database URL: http://sing.ei.uvigo.es/antimicrobialCombination/This study was supported by the Portuguese Foundation for Science and Technology (FCT) under the scope of the strategic funding of UID/BIO/04469/2013 unit and COMPETE 2020 (POCI-01-0145FEDER-006684), and support by FCT and the European Community fund FEDER, through the Programme COMPETE, under the scope of the Projects AntiPep PTDC/SAU-SAP/113196/2009 (FCOMP-01-0124-FEDER-016012) and RECI/BBB-EBI/0179/2012 (FCOMP-01-0124-FEDER-027462). Authors acknowledge the PhD Grant of Paula Jorge, funded by FCT Ref. SFRH/BD/ 88192/2012, and the PhD grants of Martin Pérez-Pérez and Gael Pe´rez-Rodriguez, funded by the University of Vigo. Finally, this study was partially funded by the [15VI013] Contract-Programme from the University of Vigo and the Agrupamento INBIOMED from DXPCTSUG-FEDER unha maneira de facer Europa (2012/273). This document reflects only the authors views and the European Union is not liable for any use that may be made of the information contained herein

    Doctor of Philosophy

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    dissertationNanoinformatics is a relatively young field of study that is important due to its implications in the field of nanomedicine, specifically toward the development of nanoparticle drug delivery systems. As more structural, biochemical, and physiochemical data become available regarding nanoparticles, the greater the knowledge-gain from using nanoinformatics methods will become. While there are challenges that exist with nanoparticle data, including heterogeneity of data and complexity of the particles, nanoinformatics will be at the forefront of processing these data and aid in the design of nanoparticles for biomedical applications. In this dissertation, a review of data mining and machine learning studies performed in the field of nanomedicine is presented. Next, the use of natural language processing methods to extract numeric values of biomedical property terms of poly(amido amine) (PAMAM) dendrimers from nanomedicine literature is demonstrated, along with successful extraction results. Following this is an implementation and its results of data mining techniques used for the development of predictive models of cytotoxicity of PAMAM dendrimers using their chemical and structural properties. Finally, a method and its results for using molecular dynamics simulations to test the ability of EDTA, as a gold standard, and generation 3.5 (G3.5) PAMAM dendrimers to chelate calcium
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