Integrating genotype and phenotype information: an overview of the PharmGKB project

Klein, Teri E.; Chang, Jeffrey T.; Cho, M. K.; Easton, Katrina L.; Fergerson, Ray W.; Hewett, Micheal; Lin, Zhen; Liu, Y.; Liu, S.; Oliver, Diane E.; Rubin, Daniel L.; Shafa, F.; Stuart, Joshua M.; Altman, Russ B.

doi:10.1038/sj.tpj.6500035

Cited by 320 publications

(228 citation statements)

References 6 publications

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“…The dataset was taken from the Pharmacogenetics and Pharmacogenomics Knowledge Base (PharmGKB) 2 [11]. We observed a dramatically changing cluster structure when varying the threshold.…”

Section: Unsupervised Analysismentioning

confidence: 99%

T-Time: Threshold-Based Data Mining on Time Series

Aßfalg

Kriegel

Kröger

et al. 2008

2008 IEEE 24th International Conference on Data Engineering

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Abstract-Mining time series data is an important approach for the analysis in many application areas as diverse as biology, environmental research, medicine, or stock chart analysis. As nearly all data mining tasks on this kind of data depend on a distance function between two time series, a huge number of such functions has been developed during the last decades. The introduction of threshold-based distance functions presented a new concept of time series similarity and these functions were applied to data mining techniques on a wide spectrum of time series data. In this demonstration, we present the Java toolkit T-Time which is able to perform several data mining tasks for a complete range of threshold values in an interactive way. The results are visually presented in a very concise way so that the user can easily identify important threshold values. Combined with domain-specific knowledge, these pivotal values can yield novel insights beyond the means of the underlying data mining techniques the analysis is based on.

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“…The dataset was taken from the Pharmacogenetics and Pharmacogenomics Knowledge Base (PharmGKB) 2 [11]. We observed a dramatically changing cluster structure when varying the threshold.…”

Section: Unsupervised Analysismentioning

confidence: 99%

T-Time: Threshold-Based Data Mining on Time Series

Aßfalg

Kriegel

Kröger

et al. 2008

2008 IEEE 24th International Conference on Data Engineering

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“…Underlying many of these informatics approaches has been the availability of reference databases containing information about the relationship between genes, drugs and diseases, such as DrugBank [13], Pharmacogenomics Knowledge Base [14,15], and the Comparative Toxicogenomics Database [16]. The broader informatics approaches to drug repositioning have been recently reviewed [2].…”

Section: Introductionmentioning

confidence: 99%

Compensating for literature annotation bias when predicting novel drug-disease relationships through Medical Subject Heading Over-representation Profile (MeSHOP) similarity

2013

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BackgroundUsing annotations to the articles in MEDLINE®/PubMed®, over six thousand chemical compounds with pharmacological actions have been tracked since 1996. Medical Subject Heading Over-representation Profiles (MeSHOPs) quantitatively leverage the literature associated with biological entities such as diseases or drugs, providing the opportunity to reposition known compounds towards novel disease applications.MethodsA MeSHOP is constructed by counting the number of times each medical subject term is assigned to an entity-related research publication in the MEDLINE database and calculating the significance of the count by comparing against the count of the term in a background set of publications. Based on the expectation that drugs suitable for treatment of a disease (or disease symptom) will have similar annotation properties to the disease, we successfully predict drug-disease associations by comparing MeSHOPs of diseases and drugs.ResultsThe MeSHOP comparison approach delivers an 11% improvement over bibliometric baselines. However, novel drug-disease associations are observed to be biased towards drugs and diseases with more publications. To account for the annotation biases, a correction procedure is introduced and evaluated.ConclusionsBy explicitly accounting for the annotation bias, unexpectedly similar drug-disease pairs are highlighted as candidates for drug repositioning research. MeSHOPs are shown to provide a literature-supported perspective for discovery of new links between drugs and diseases based on pre-existing knowledge.

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“…Consider for example, that five curators at the Pharmacogenomics Knowledge Base (PharmGKB) manually browse the pharmacogenomics (PGx) literature to curate relationships relevant for storage in the PharmGKB [2]. The result of this curation process is a high quality database queried by clinicians and bioinformaticians.…”

Section: Introductionmentioning

confidence: 99%

Integration and publication of heterogeneous text-mined relationships on the Semantic Web

et al. 2011

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BackgroundAdvances in Natural Language Processing (NLP) techniques enable the extraction of fine-grained relationships mentioned in biomedical text. The variability and the complexity of natural language in expressing similar relationships causes the extracted relationships to be highly heterogeneous, which makes the construction of knowledge bases difficult and poses a challenge in using these for data mining or question answering.ResultsWe report on the semi-automatic construction of the PHARE relationship ontology (the PHArmacogenomic RElationships Ontology) consisting of 200 curated relations from over 40,000 heterogeneous relationships extracted via text-mining. These heterogeneous relations are then mapped to the PHARE ontology using synonyms, entity descriptions and hierarchies of entities and roles. Once mapped, relationships can be normalized and compared using the structure of the ontology to identify relationships that have similar semantics but different syntax. We compare and contrast the manual procedure with a fully automated approach using WordNet to quantify the degree of integration enabled by iterative curation and refinement of the PHARE ontology. The result of such integration is a repository of normalized biomedical relationships, named PHARE-KB, which can be queried using Semantic Web technologies such as SPARQL and can be visualized in the form of a biological network.ConclusionsThe PHARE ontology serves as a common semantic framework to integrate more than 40,000 relationships pertinent to pharmacogenomics. The PHARE ontology forms the foundation of a knowledge base named PHARE-KB. Once populated with relationships, PHARE-KB (i) can be visualized in the form of a biological network to guide human tasks such as database curation and (ii) can be queried programmatically to guide bioinformatics applications such as the prediction of molecular interactions. PHARE is available at http://purl.bioontology.org/ontology/PHARE.

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Integrating genotype and phenotype information: an overview of the PharmGKB project

Cited by 320 publications

References 6 publications

T-Time: Threshold-Based Data Mining on Time Series

T-Time: Threshold-Based Data Mining on Time Series

Compensating for literature annotation bias when predicting novel drug-disease relationships through Medical Subject Heading Over-representation Profile (MeSHOP) similarity

Integration and publication of heterogeneous text-mined relationships on the Semantic Web

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