How to learn about gene function: text-mining or ontologies?

Soldatos, Theodoros; Perdigão, Nelson; Brown, Nigel P.; Sabir, Kenneth; O’Donoghue, Seán I.

doi:10.1016/j.ymeth.2014.07.004

Cited by 26 publications

(23 citation statements)

References 64 publications

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“…Thus, the functional characterization of putatively selected loci becomes more and more crucial as we delve into deciphering the underlying genetic basis of multifactorial traits (Soldatos et al . ). Further, rich annotation offers a new avenue to investigate the source of predictive ability for each annotated genomic region via a genome partitioning approach (Morota et al .…”

Section: Discussionmentioning

confidence: 97%

An application of Me SH enrichment analysis in livestock

et al. 2015

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SummaryAn integral part of functional genomics studies is to assess the enrichment of specific biological terms in lists of genes found to be playing an important role in biological phenomena. Contrasting the observed frequency of annotated terms with those of the background is at the core of overrepresentation analysis (ORA). Gene Ontology (GO) is a means to consistently classify and annotate gene products and has become a mainstay in ORA. Alternatively, Medical Subject Headings (MeSH) offers a comprehensive life science vocabulary including additional categories that are not covered by GO. Although MeSH is applied predominantly in human and model organism research, its full potential in livestock genetics is yet to be explored. In this study, MeSH ORA was evaluated to discern biological properties of identified genes and contrast them with the results obtained from GO enrichment analysis. Three published datasets were employed for this purpose, representing a gene expression study in dairy cattle, the use of SNPs for genome‐wide prediction in swine and the identification of genomic regions targeted by selection in horses. We found that several overrepresented MeSH annotations linked to these gene sets share similar concepts with those of GO terms. Moreover, MeSH yielded unique annotations, which are not directly provided by GO terms, suggesting that MeSH has the potential to refine and enrich the representation of biological knowledge. We demonstrated that MeSH can be regarded as another choice of annotation to draw biological inferences from genes identified via experimental analyses. When used in combination with GO terms, our results indicate that MeSH can enhance our functional interpretations for specific biological conditions or the genetic basis of complex traits in livestock species.

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Section: Discussionmentioning

confidence: 97%

An application of Me SH enrichment analysis in livestock

et al. 2015

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“…The public FAERS dataset contained 7.9 million cases and held records regarding the patients' treatments (medications), the indications of those treatments (disease or condition), and the observed reactions and outcomes (e.g., "death" or "hospitalization") reported in these AEs. To compensate for ambiguities introduced by the non-standardized use of drug names [20], FAERS free-text medication descriptions were consolidated via a stepwise process that matched each name to standardized dictionaries [21]. Indications and reactions, coded by FAERS in terms from the Medical Dictionary for Regulatory Activities (MedDRA), were analyzed at the Preferred Term (PT) level (i.e., MedDRA Level 4 descriptions).…”

Section: Data Integrationmentioning

confidence: 99%

Radioimmunotherapy in Non-Hodgkin’s Lymphoma: Retrospective Adverse Event Profiling of Zevalin and Bexxar

Sachpekidis

Jackson²,

Soldatos³

2019

Pharmaceuticals

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The development of monoclonal antibodies has dramatically changed the outcome of patients with non-Hodgkin’s lymphoma (NHL), the most common hematological malignancy. However, despite the satisfying results of monoclonal antibody treatment, only few NHL patients are permanently cured with single-agent therapies. In this context, radioimmunotherapy, the administration of radionuclides conjugated to monoclonal antibodies, is aimed to augment the single-agent efficacy of immunotherapy in order to deliver targeted radiation to tumors, particularly CD20+ B-cell lymphomas. Based on evidence from several trials in NHL, the radiolabeled antibodies 90Y-ibritumomab tiuxetan (Zevalin, Spectrum Pharmaceuticals) and 131I-tositumomab (Bexxar, GlaxoSmithKline) received FDA approval in 2002 and 2003, respectively. However, none of the two radioimmunotherapeutic agents has been broadly applied in clinical practice. The main reason for the under-utilization of radioimmunotherapy includes economic and logistic considerations. However, concerns about potential side effects have also been raised. Driven by these developments, we performed retrospective analysis of adverse events reporting Zevalin or Bexxar, extracted from the FDA’s Adverse Event Reporting System (FAERS) and the World Health Organization’s VigiBase repository. Our results indicate that the two radioimmunotherapeutic agents have both related and distinct side effect profiles and confirm their known toxicological considerations. Our work also suggests that computational analysis of real-world post-marketing data can provide informative clinical insights. While more prospective studies are necessary to fully characterize the efficacy and safety of radioimmunotherapy, we expect that it has not yet reached its full therapeutic potential in modern hematological oncology.

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“…By far the most common application of computational representations of knowledge to problems in biomedicine is enrichment analysis, see e.g. [ 24 , 43 , 46 ]. Enrichment analysis generates hypotheses about the concerted functions of collections of genes by testing for annotations that occur more frequently in the collection than would be expected by chance.…”

Section: Knowledge-based Inferencementioning

confidence: 99%

Knowledge-based biomedical Data Science

Hunter

2017

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Computational manipulation of knowledge is an important, and often under-appreciated, aspect of biomedical Data Science. The first Data Science initiative from the US National Institutes of Health was entitled “Big Data to Knowledge (BD2K).” The main emphasis of the more than $200M allocated to that program has been on “Big Data;” the “Knowledge” component has largely been the implicit assumption that the work will lead to new biomedical knowledge. However, there is long-standing and highly productive work in computational knowledge representation and reasoning, and computational processing of knowledge has a role in the world of Data Science. Knowledge-based biomedical Data Science involves the design and implementation of computer systems that act as if they knew about biomedicine. There are many ways in which a computational approach might act as if it knew something: for example, it might be able to answer a natural language question about a biomedical topic, or pass an exam; it might be able to use existing biomedical knowledge to rank or evaluate hypotheses; it might explain or interpret data in light of prior knowledge, either in a Bayesian or other sort of framework. These are all examples of automated reasoning that act on computational representations of knowledge. After a brief survey of existing approaches to knowledge-based data science, this position paper argues that such research is ripe for expansion, and expanded application.

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How to learn about gene function: text-mining or ontologies?

Cited by 26 publications

References 64 publications

An application of Me SH enrichment analysis in livestock

An application of Me SH enrichment analysis in livestock

Radioimmunotherapy in Non-Hodgkin’s Lymphoma: Retrospective Adverse Event Profiling of Zevalin and Bexxar

Knowledge-based biomedical Data Science

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