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Items 1 to 7 of about 7
1. Wiegers TC, Davis AP, Cohen KB, Hirschman L, Mattingly CJ: Text mining and manual curation of chemical-gene-disease networks for the comparative toxicogenomics database (CTD). BMC Bioinformatics; 2009 Oct 08;10:326
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  • [Source] The source of this record is MEDLINE®, a database of the U.S. National Library of Medicine.
  • [Title] Text mining and manual curation of chemical-gene-disease networks for the comparative toxicogenomics database (CTD).
  • BACKGROUND: The Comparative Toxicogenomics Database (CTD) is a publicly available resource that promotes understanding about the etiology of environmental diseases.
  • It provides manually curated chemical-gene/protein interactions and chemical- and gene-disease relationships from the peer-reviewed, published literature.
  • The goals of the research reported here were to establish a baseline analysis of current CTD curation, develop a text-mining prototype from readily available open source components, and evaluate its potential value in augmenting curation efficiency and increasing data coverage.
  • RESULTS: Prototype text-mining applications were developed and evaluated using a CTD data set consisting of manually curated molecular interactions and relationships from 1,600 documents.
  • Preliminary results indicated that the prototype found 80% of the gene, chemical, and disease terms appearing in curated interactions.
  • These terms were used to re-rank documents for curation, resulting in increases in mean average precision (63% for the baseline vs. 73% for a rule-based re-ranking), and in the correlation coefficient of rank vs. number of curatable interactions per document (baseline 0.14 vs. 0.38 for the rule-based re-ranking).
  • CONCLUSION: This text-mining project is unique in its integration of existing tools into a single workflow with direct application to CTD.
  • We performed a baseline assessment of the inter-curator consistency and coverage in CTD, which allowed us to measure the potential of these integrated tools to improve prioritization of journal articles for manual curation.
  • Our study presents a feasible and cost-effective approach for developing a text mining solution to enhance manual curation throughput and efficiency.

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  • (PMID = 19814812.001).
  • [ISSN] 1471-2105
  • [Journal-full-title] BMC bioinformatics
  • [ISO-abbreviation] BMC Bioinformatics
  • [Language] ENG
  • [Grant] United States / NCRR NIH HHS / RR / P20 RR016463; United States / NIEHS NIH HHS / ES / R01 ES014065; United States / NCRR NIH HHS / RR / P20 RR-016463
  • [Publication-type] Comparative Study; Journal Article; Research Support, N.I.H., Extramural
  • [Publication-country] England
  • [Other-IDs] NLM/ PMC2768719
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2. Thorn CF, Klein TE, Altman RB: Pharmacogenomics and bioinformatics: PharmGKB. Pharmacogenomics; 2010 Apr;11(4):501-5
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  • [Source] The source of this record is MEDLINE®, a database of the U.S. National Library of Medicine.
  • [Title] Pharmacogenomics and bioinformatics: PharmGKB.
  • The NIH initiated the PharmGKB in April 2000.
  • The primary mission was to create a repository of primary data, tools to track associations between genes and drugs, and to catalog the location and frequency of genetic variations known to impact drug response.
  • Over the past 10 years, new technologies have shifted research from candidate gene pharmacogenetics to phenotype-based pharmacogenomics with a consequent explosion of data.
  • PharmGKB has refocused on curating knowledge rather than housing primary genotype and phenotype data, and now, captures more complex relationships between genes, variants, drugs, diseases and pathways.
  • Going forward, the challenges are to provide the tools and knowledge to plan and interpret genome-wide pharmacogenomics studies, predict gene-drug relationships based on shared mechanisms and support data-sharing consortia investigating clinical applications of pharmacogenomics.

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  • (PMID = 20350130.001).
  • [ISSN] 1744-8042
  • [Journal-full-title] Pharmacogenomics
  • [ISO-abbreviation] Pharmacogenomics
  • [Language] ENG
  • [Grant] United States / NIGMS NIH HHS / GM / GM061374-10; United States / NIGMS NIH HHS / GM / U01 GM061374; United States / NIGMS NIH HHS / GM / U01 GM061374-10; United States / NIGMS NIH HHS / GM / U01GM61374
  • [Publication-type] Journal Article; Research Support, N.I.H., Extramural
  • [Publication-country] England
  • [Chemical-registry-number] 0 / Pharmaceutical Preparations
  • [Other-IDs] NLM/ NIHMS287269; NLM/ PMC3098752
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3. Li J, Zhu X, Chen JY: Building disease-specific drug-protein connectivity maps from molecular interaction networks and PubMed abstracts. PLoS Comput Biol; 2009 Jul;5(7):e1000450
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  • [Title] Building disease-specific drug-protein connectivity maps from molecular interaction networks and PubMed abstracts.
  • The recently proposed concept of molecular connectivity maps enables researchers to integrate experimental measurements of genes, proteins, metabolites, and drug compounds under similar biological conditions.
  • The study of these maps provides opportunities for future toxicogenomics and drug discovery applications.
  • We developed a computational framework to build disease-specific drug-protein connectivity maps.
  • We integrated gene/protein and drug connectivity information based on protein interaction networks and literature mining, without requiring gene expression profile information derived from drug perturbation experiments on disease samples.
  • We described the development and application of this computational framework using Alzheimer's Disease (AD) as a primary example in three steps.
  • First, molecular interaction networks were incorporated to reduce bias and improve relevance of AD seed proteins.
  • Second, PubMed abstracts were used to retrieve enriched drug terms that are indirectly associated with AD through molecular mechanistic studies.
  • Third and lastly, a comprehensive AD connectivity map was created by relating enriched drugs and related proteins in literature.
  • We showed that this molecular connectivity map development approach outperformed both curated drug target databases and conventional information retrieval systems.
  • Our initial explorations of the AD connectivity map yielded a new hypothesis that diltiazem and quinidine may be investigated as candidate drugs for AD treatment.
  • Molecular connectivity maps derived computationally can help study molecular signature differences between different classes of drugs in specific disease contexts.
  • To achieve overall good data coverage and quality, a series of statistical methods have been developed to overcome high levels of data noise in biological networks and literature mining results.
  • Further development of computational molecular connectivity maps to cover major disease areas will likely set up a new model for drug development, in which therapeutic/toxicological profiles of candidate drugs can be checked computationally before costly clinical trials begin.
  • [MeSH-major] Databases, Protein. Genomics / methods. Pharmaceutical Preparations / chemistry. Pharmacology. Proteins / chemistry. PubMed. Systems Biology / methods
  • [MeSH-minor] Algorithms. Alzheimer Disease / drug therapy. Animals. Cluster Analysis. Drug Therapy. Genes / drug effects. Humans. ROC Curve. Reproducibility of Results. Sensitivity and Specificity


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4. Yang X, Huang Y, Crowson M, Li J, Maitland ML, Lussier YA: Kinase inhibition-related adverse events predicted from in vitro kinome and clinical trial data. J Biomed Inform; 2010 Jun;43(3):376-84
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  • [Title] Kinase inhibition-related adverse events predicted from in vitro kinome and clinical trial data.
  • BACKGROUND: Kinase inhibition is an increasingly popular strategy for pharmacotherapy of human diseases.
  • Although many of these agents have been described as "targeted therapy", they will typically inhibit multiple kinases with varying potency.
  • Pre-clinical model testing has not predicted the numerous significant toxicities identified during clinical development.
  • The purpose of this study was to develop a bioinformatics-based method to predict specific adverse events (AEs) in humans associated with the inhibition of particular kinase targets (KTs).
  • METHODS: The AE frequencies of protein kinase inhibitors (PKIs) were curated from three sources (PubMed, Thompson Physician Desk Reference and PharmGKB), and affinities of 38 PKIs for 317 kinases, representing >50% of the predicted human kinome, were collected from published in vitro assay results.
  • A novel quantitative computational method was developed to predict associations between KTs and AEs that included a whole panel of 71 AEs and 20 PKIs targeting 266 distinct kinases with K(d)<10microM.
  • The method calculated an unbiased, kinome-wide association score via linear algebra on (i) the normalized frequencies of AEs associated with 20 PKIs and (ii) the negative log-transformed dissociation constant of kinases targeted by these PKIs.
  • Finally, a reference standard was calculated by applying Fisher's exact test to the co-occurrence of indexed Pubmed terms (p0.05, and manually verified) for AE and associated kinase targets (AE-KT) pairs from standard literature search techniques.
  • We also evaluated the enrichment of predictions between the quantitative method and the literature search by Fisher's exact testing.
  • RESULTS: We identified significant associations among already empirically well established pairs of AEs (e.g. diarrhea and rash) and KTs (e.g. EGFR).
  • The following less well recognized AE-KT pairs had similar association scores: diarrhea-(DDR1;ERBB4), rash-ERBB4, and fatigue-(CSF1R;KIT).
  • With no filtering, the association score identified 41 prioritized associations involving 7 AEs and 19 KTs.
  • Among them, eight associations were reported in the literature review.
  • There were only 78 out of a total of 4522 AE-KT pairs meeting the evaluation threshold, indicating a strong association between the predicted and the text mined AE-KT pairs (p=3x10(-7)).
  • As many of these drugs remain in development, a larger volume of more detailed data on AE-PKI associations is accessible only through non-public databases.
  • These prediction models will be refined with these data and validated through dedicated prospective human studies.
  • CONCLUSION AND FUTURE DIRECTIONS: Our in silico method can predict associations between kinase targets and AE frequencies in human patients.
  • Refining this method should lead to improved clinical development of protein kinase inhibitors, a large new class of therapeutics. http://www.lussierlab.org/publication/PAS/.

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  • (PMID = 20434586.001).
  • [ISSN] 1532-0480
  • [Journal-full-title] Journal of biomedical informatics
  • [ISO-abbreviation] J Biomed Inform
  • [Language] ENG
  • [Grant] United States / NCRR NIH HHS / RR / 1U54 RR023560-01A1; United States / NCI NIH HHS / CA / K23 CA124802-04; United States / NLM NIH HHS / LM / LM008308-05; United States / NCRR NIH HHS / RR / UL1 RR024999; United States / NCRR NIH HHS / RR / RR024999-03; United States / NCRR NIH HHS / RR / U54 RR023560; United States / NCRR NIH HHS / RR / UL1 RR024999-04; United States / NLM NIH HHS / LM / K22 LM008308-05; United States / NCRR NIH HHS / RR / UL1 RR024999-03; United States / NCI NIH HHS / CA / K23 CA124802-03; United States / NCI NIH HHS / CA / U54 CA121852; United States / NCRR NIH HHS / RR / RR024999-04; United States / NCI NIH HHS / CA / K23CA124802; United States / NCI NIH HHS / CA / U54 CA121852-05; United States / NLM NIH HHS / LM / K22 LM008308; United States / NCI NIH HHS / CA / 1U54CA121852; United States / NCRR NIH HHS / RR / UL1 RR024999-03S5; United States / NCRR NIH HHS / RR / RR024999-03S5; United States / NCI NIH HHS / CA / K23 CA124802
  • [Publication-type] Journal Article; Research Support, N.I.H., Extramural; Research Support, Non-U.S. Gov't
  • [Publication-country] United States
  • [Chemical-registry-number] 0 / Protein Kinase Inhibitors; EC 2.7.- / Protein Kinases
  • [Other-IDs] NLM/ NIHMS202308; NLM/ PMC2893391
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5. Garten Y, Tatonetti NP, Altman RB: Improving the prediction of pharmacogenes using text-derived drug-gene relationships. Pac Symp Biocomput; 2010;:305-14
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  • [Source] The source of this record is MEDLINE®, a database of the U.S. National Library of Medicine.
  • [Title] Improving the prediction of pharmacogenes using text-derived drug-gene relationships.
  • A critical goal of pharmacogenomics research is to identify genes that can explain variation in drug response.
  • We have previously reported a method that creates a genome-scale ranking of genes likely to interact with a drug.
  • The algorithm uses information about drug structure and indications of use to rank the genes.
  • Although the algorithm has good performance, its performance depends on a curated set of drug-gene relationships that is expensive to create and difficult to maintain.
  • In this work, we assess the utility of text mining in extracting a network of drug-gene relationships automatically.
  • This provides a valuable aggregate source of knowledge, subsequently used as input into the algorithm that ranks potential pharmacogenes.
  • Using a drug-gene network created from sentence-level co-occurrence in the full text of scientific articles, we compared the performance to that of a network created by manual curation of those articles.
  • Under a wide range of conditions, we show that a knowledge base derived from text-mining the literature performs as well as, and sometimes better than, a high-quality, manually curated knowledge base.
  • We conclude that we can use relationships mined automatically from the literature as a knowledgebase for pharmacogenomics relationships.
  • Additionally, when relationships are missed by text mining, our system can accurately extrapolate new relationships with 77.4% precision.

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  • (PMID = 19908383.001).
  • [ISSN] 2335-6936
  • [Journal-full-title] Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
  • [ISO-abbreviation] Pac Symp Biocomput
  • [Language] ENG
  • [Grant] United States / NLM NIH HHS / LM / R01 LM005652; United States / NIGMS NIH HHS / GM / U01 GM061374-10; United States / NIGMS NIH HHS / GM / GM061374-10; United States / NIGMS NIH HHS / GM / U01GM61374; United States / NLM NIH HHS / LM / T15 LM007033; United States / NIGMS NIH HHS / GM / U01 GM061374
  • [Publication-type] Journal Article; Research Support, N.I.H., Extramural; Validation Studies
  • [Publication-country] United States
  • [Other-IDs] NLM/ NIHMS287270; NLM/ PMC3092476
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6. Garten Y, Coulet A, Altman RB: Recent progress in automatically extracting information from the pharmacogenomic literature. Pharmacogenomics; 2010 Oct;11(10):1467-89
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  • [Source] The source of this record is MEDLINE®, a database of the U.S. National Library of Medicine.
  • [Title] Recent progress in automatically extracting information from the pharmacogenomic literature.
  • The biomedical literature holds our understanding of pharmacogenomics, but it is dispersed across many journals.
  • In order to integrate our knowledge, connect important facts across publications and generate new hypotheses we must organize and encode the contents of the literature.
  • By creating databases of structured pharmocogenomic knowledge, we can make the value of the literature much greater than the sum of the individual reports.
  • We can, for example, generate candidate gene lists or interpret surprising hits in genome-wide association studies.
  • Text mining automatically adds structure to the unstructured knowledge embedded in millions of publications, and recent years have seen a surge in work on biomedical text mining, some specific to pharmacogenomics literature.
  • These methods enable extraction of specific types of information and can also provide answers to general, systemic queries.
  • In this article, we describe the main tasks of text mining in the context of pharmacogenomics, summarize recent applications and anticipate the next phase of text mining applications.

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  • (PMID = 21047206.001).
  • [ISSN] 1744-8042
  • [Journal-full-title] Pharmacogenomics
  • [ISO-abbreviation] Pharmacogenomics
  • [Language] ENG
  • [Grant] United States / NIGMS NIH HHS / GM / R24 GM061374; United States / NLM NIH HHS / LM / LM07033; United States / NLM NIH HHS / LM / R01 LM005652; United States / NHGRI NIH HHS / HG / U54 HG004028; United States / NLM NIH HHS / LM / LM05652; United States / NIGMS NIH HHS / GM / GM061374-10S1; United States / NIGMS NIH HHS / GM / U01 GM061374-10S1; United States / NIGMS NIH HHS / GM / GM61374; United States / NLM NIH HHS / LM / T15 LM007033; United States / NIGMS NIH HHS / GM / U01 GM061374
  • [Publication-type] Journal Article; Research Support, N.I.H., Extramural; Review
  • [Publication-country] England
  • [Other-IDs] NLM/ NIHMS268321; NLM/ PMC3035632
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7. Iossifov I, Rodriguez-Esteban R, Mayzus I, Millen KJ, Rzhetsky A: Looking at cerebellar malformations through text-mined interactomes of mice and humans. PLoS Comput Biol; 2009 Nov;5(11):e1000559
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  • [Source] The source of this record is MEDLINE®, a database of the U.S. National Library of Medicine.
  • [Title] Looking at cerebellar malformations through text-mined interactomes of mice and humans.
  • We have generated and made publicly available two very large networks of molecular interactions: 49,493 mouse-specific and 52,518 human-specific interactions.
  • These networks were generated through automated analysis of 368,331 full-text research articles and 8,039,972 article abstracts from the PubMed database, using the GeneWays system.
  • Our networks cover a wide spectrum of molecular interactions, such as bind, phosphorylate, glycosylate, and activate; 207 of these interaction types occur more than 1,000 times in our unfiltered, multi-species data set.
  • Because mouse and human genes are linked through an orthological relationship, human and mouse networks are amenable to straightforward, joint computational analysis.
  • Using our newly generated networks and known associations between mouse genes and cerebellar malformation phenotypes, we predicted a number of new associations between genes and five cerebellar phenotypes (small cerebellum, absent cerebellum, cerebellar degeneration, abnormal foliation, and abnormal vermis).
  • Using a battery of statistical tests, we showed that genes that are associated with cerebellar phenotypes tend to form compact network clusters.
  • Further, we observed that cerebellar malformation phenotypes tend to be associated with highly connected genes.
  • This tendency was stronger for developmental phenotypes and weaker for cerebellar degeneration.

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  • (PMID = 19893633.001).
  • [ISSN] 1553-7358
  • [Journal-full-title] PLoS computational biology
  • [ISO-abbreviation] PLoS Comput. Biol.
  • [Language] ENG
  • [Grant] United States / NIGMS NIH HHS / GM / R01 GM061372; United States / NCI NIH HHS / CA / U54 CA121852; United States / NIGMS NIH HHS / GM / GM61372; United States / NCI NIH HHS / CA / U54 CA121852-01A1
  • [Publication-type] Journal Article; Research Support, N.I.H., Extramural; Research Support, Non-U.S. Gov't; Research Support, U.S. Gov't, Non-P.H.S.
  • [Publication-country] United States
  • [Chemical-registry-number] 0 / Nerve Tissue Proteins
  • [Other-IDs] NLM/ PMC2767227
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