Manual curation is not sufficient for annotation of genomic databases

Baumgartner, William A.; Cohen, K. Bretonnel; Fox, Lynne M.; Acquaah‐Mensah, George; Hunter, Lawrence

doi:10.1093/bioinformatics/btm229

Cited by 214 publications

(167 citation statements)

References 33 publications

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“…(Cohen et al, 2005) notent que beaucoup de travail reste à faire pour l'évaluation en contexte réel des systèmes de fouille de textes. Mais cette évaluation est importante car les bases de données existantes sont incomplètes et (Baumgartner et al, 2007) montrent que la complétion manuelle ne sera jamais suffisante.…”

Section: Interface D'aide Au Peuplement D'une Base De Donnéesunclassified

Extraction de relations complexes. Application à des résultats expérimentaux en physiologie rénale

Minard¹,

Grau²,

Ligozat³

et al. 2013

Techniques et sciences informatiques

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Section: Interface D'aide Au Peuplement D'une Base De Donnéesunclassified

Extraction de relations complexes. Application à des résultats expérimentaux en physiologie rénale

Minard¹,

Grau²,

Ligozat³

et al. 2013

Techniques et sciences informatiques

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“…Integrating data sources with overlapping scope can help to highlight hot-spots of poor data quality as these often appear as clusters of contradicting values [Müller et al 2003]. Conflicts, once detected, may be resolved by domain experts familiar with domain constraints, regularities, and possible pitfalls in the data generation process; however, these experts are often overwhelmed by the sheer number of conflicts [Baumgartner et al 2007]. On the other hand, conflicts between overlapping data sources often are systematic, that is, they are produced not at random, but due to some characteristics of the different systems (such as the parser error in the example given before).…”

Section: Introductionmentioning

confidence: 99%

Improving data quality by source analysis

Müller

Freytag

Leser

2012

J. Data and Information Quality

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In many domains, data cleaning is hampered by our limited ability to specify a comprehensive set of integrity constraints to assist in identification of erroneous data. An alternative approach to improve data quality is to exploit different data sources that contain information about the same set of objects. Such overlapping sources highlight hot-spots of poor data quality through conflicting data values and immediately provide alternative values for conflict resolution. In order to derive a dataset of high quality, we can merge the overlapping sources based on a quality assessment of the conflicting values. The quality of the resulting dataset, however, is highly dependent on our ability to asses the quality of conflicting values effectively.The main objective of this article is to introduce methods that aid the developer of an integrated system over overlapping, but contradicting sources in the task of improving the quality of data. Value conflicts between contradicting sources are often systematic, caused by some characteristic of the different sources. Our goal is to identify such systematic differences and outline data patterns that occur in conjunction with them. Evaluated by an expert user, the regularities discovered provide insights into possible conflict reasons and help to assess the quality of inconsistent values. The contributions of this article are two concepts of systematic conflicts: contradiction patterns and minimal update sequences. Contradiction patterns resemble a special form of association rules that summarize characteristic data properties for conflict occurrence. We adapt existing association rule mining algorithms for mining contradiction patterns. Contradiction patterns, however, view each class of conflicts in isolation, sometimes leading to largely overlapping patterns. Sequences of set-oriented update operations that transform one data source into the other are compact descriptions for all regular differences among the sources. We consider minimal update sequences as the most likely explanation for observed differences between overlapping data sources. Furthermore, the order of operations within the sequences point out potential dependencies between systematic differences. Finding minimal update sequences, however, is beyond reach in practice. We show that the problem already is NP-complete for a restricted set of operations. In the light of this intractability result, we present heuristics that lead to convincing results for all examples we considered.

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“…It has been observed already a few years ago that such an approach is not sufficiently efficient in order to cope with the increasing quantity of published results [3]. In order to support this process, researchers are turning their attention to text mining methodologies, not with the aim of replacing manual curation, which is not possible in the foreseeable future, but rather with the aim of providing tools that can make the curation process more efficient.…”

Section: Introductionmentioning

confidence: 99%

Ranking Interactions for a Curation Task

Clematide

Rinaldi

2011

2011 10th International Conference on Machine Learning and Applications and Workshops

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One of the key pieces of information which biomedical text mining systems are expected to extract from the literature are interactions among different types of biomedical entities (proteins, genes, diseases, drugs, etc.). Different types of entities might be considered, for example protein-protein interactions have been extensively studied as part of the Bio Creative competitive evaluations. However, more complex interactions such as those among genes, drugs, and diseases are increasingly of interest. Different databases have been used as reference for the evaluation of extraction and ranking techniques. The aim of this paper is to describe a machine-learning based reranking approach for candidate interactions extracted from the literature. The results are evaluated using data derived from the Pharm GKB database. The importance of a good ranking is particularly evident when the results are applied to support human curators. Different types of entities might be considered, for example protein-protein interactions have been extensively studied as part of the BioCreative competitive evaluations. However, more complex interactions such as those among genes, drugs, and diseases are increasingly of interest. Different databases have been used as reference for the evaluation of extraction and ranking techniques.The aim of this paper is to describe a machine-learning based reranking approach for candidate interactions extracted from the literature. The results are evaluated using data derived from the PharmGKB database. The importance of a good ranking is particularly evident in the case the results are applied to support human curators.

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Manual curation is not sufficient for annotation of genomic databases

Abstract: Motivation: Knowledge base construction has been an area of intense activity and great importance in the growth of computational biology.

Cited by 214 publications

References 33 publications

Extraction de relations complexes. Application à des résultats expérimentaux en physiologie rénale

Extraction de relations complexes. Application à des résultats expérimentaux en physiologie rénale

Improving data quality by source analysis

Ranking Interactions for a Curation Task

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