Algorithms and semantic infrastructure for mutation impact extraction and grounding

Laurila, Jonas Bergman; Naderi, Nona; Witte, René; Riazanov, Alexandre; Kouznetsov, Alexandre; Baker, Christopher J. O.

doi:10.1186/1471-2164-11-s4-s24

Cited by 30 publications

(29 citation statements)

References 29 publications

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“…So in [2] we explored the possibility of using semantic technologies for exporting the text mining pipeline outputs according to a domain specific knowledge representation. Currently, our system, like mSTRAP [5], delivers its results in the form of an OWL ABox, i.e., as a collection of logical statements characterising the extracted mutations, proteins and impacts.…”

Section: Introductionmentioning

confidence: 99%

Deploying mutation impact text-mining software with the SADI Semantic Web Services framework

2011

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BackgroundMutation impact extraction is an important task designed to harvest relevant annotations from scientific documents for reuse in multiple contexts. Our previous work on text mining for mutation impacts resulted in (i) the development of a GATE-based pipeline that mines texts for information about impacts of mutations on proteins, (ii) the population of this information into our OWL DL mutation impact ontology, and (iii) establishing an experimental semantic database for storing the results of text mining.ResultsThis article explores the possibility of using the SADI framework as a medium for publishing our mutation impact software and data. SADI is a set of conventions for creating web services with semantic descriptions that facilitate automatic discovery and orchestration. We describe a case study exploring and demonstrating the utility of the SADI approach in our context. We describe several SADI services we created based on our text mining API and data, and demonstrate how they can be used in a number of biologically meaningful scenarios through a SPARQL interface (SHARE) to SADI services. In all cases we pay special attention to the integration of mutation impact services with external SADI services providing information about related biological entities, such as proteins, pathways, and drugs.ConclusionWe have identified that SADI provides an effective way of exposing our mutation impact data such that it can be leveraged by a variety of stakeholders in multiple use cases. The solutions we provide for our use cases can serve as examples to potential SADI adopters trying to solve similar integration problems.

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Section: Introductionmentioning

confidence: 99%

Deploying mutation impact text-mining software with the SADI Semantic Web Services framework

2011

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“…Similar to disease variant extraction, EnzyMiner has difficulty associating variants to the correct enzyme when multiple enzymes are present in the same abstract and thus may benefit from natural language processing. Laurila et al created a method to extract variants (using MutationFinder) and impact relations from abstracts[110]. Finally, Naderi et al extended this work and developed the Open Mutation Miner[111] to extract variants and impact relations using an ontology model for variant impact relations.…”

Section: Variant Extraction From the Literaturementioning

confidence: 99%

Towards Precision Medicine: Advances in Computational Approaches for the Analysis of Human Variants

Peterson

Doughty

Kann

2013

Journal of Molecular Biology

121

122

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Variations and similarities in our individual genomes are part of our history, our heritage, and our identity. Some human genomic variants are associated with common traits such as hair and eye color, while others are associated with susceptibility to disease or response to drug treatment. Identifying the human variations producing clinically relevant phenotypic changes is critical for providing accurate and personalized diagnosis, prognosis, and treatment for diseases. Furthermore, a better understanding of the molecular underpinning of disease can lead to development of new drug targets for precision medicine. Several resources have been designed for collecting and storing human genomic variations in highly structured, easily accessible databases. Unfortunately, a vast amount of information about these genetic variants and their functional and phenotypic associations is currently buried in the literature, only accessible by manual curation or sophisticated text mining technology to extract the relevant information. In addition, the low cost of sequencing technologies coupled with increasing computational power has enabled the development of numerous computational methodologies to predict the pathogenicity of human variants. This review provides a detailed comparison of current human variant resources, including HGMD, OMIM, ClinVar, and UniProt/Swiss-Prot, followed by an overview of the computational methods and techniques used to leverage the available data to predict novel deleterious variants. We expect these resources and tools to become the foundation for understanding the molecular details of genomic variants leading to disease, which in turn will enable the promise of precision medicine.

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“…The use of information extraction techniques in pharmacogenomics resulted, for example, in the creation of tools for the extraction of pharmacogenomic concepts and relationships [24], the automatic construction of databases such as SIDER [10], the completion of pharmacokinetics pathways [25], the creation of a Pharmacogenomic Relationships Ontology (PHARE) [26], and the extraction of mutation impacts on protein properties that were used to populate the Mutation Impact Ontology [27]. One limitation of NLP approaches is the lack of available text corpora where genes, drugs, phenotypes and their relationships are manually annotated.…”

Section: Extracting Knowledge From the Pharmacogenomics Literaturementioning

confidence: 99%

Semantically Enabling Pharmacogenomic Data for the Realization of Personalized Medicine

et al. 2012

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Understanding how each individual’s genetics and physiology influences pharmaceutical response is crucial to the realization of personalized medicine and the discovery and validation of pharmacogenomic biomarkers is key to its success. However, integration of genotype and phenotype knowledge in medical information systems remains a critical challenge. The inability to easily and accurately integrate the results of biomolecular studies with patients’ medical records and clinical reports prevents us from realizing the full potential of pharmacogenomic knowledge for both drug development and clinical practice. Herein, we describe approaches using Semantic Web technologies, in which pharmacogenomic knowledge relevant to drug development and medical decision support is represented in such a way that it can be efficiently accessed both by software and human experts. We suggest that this approach increases the utility of data, and that such computational technologies will become an essential part of personalized medicine, alongside diagnostics and pharmaceutical products.

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Algorithms and semantic infrastructure for mutation impact extraction and grounding

Cited by 30 publications

References 29 publications

Deploying mutation impact text-mining software with the SADI Semantic Web Services framework

Deploying mutation impact text-mining software with the SADI Semantic Web Services framework

Towards Precision Medicine: Advances in Computational Approaches for the Analysis of Human Variants

Semantically Enabling Pharmacogenomic Data for the Realization of Personalized Medicine

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