Detecting duplicate biological entities using Shortest Path Edit Distance

Rudniy, Alex; Song, Min; Geller, James

doi:10.1504/ijdmb.2010.034196

Cited by 16 publications

(9 citation statements)

References 30 publications

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“…By analysing the support and confidence of each rule, the method can show the presence of erroneous data. Other approaches use approximate string matching to compute metadata similarity [10,11,12], under the assumption that duplicates have high metadata similarity. Some approaches consider duplicates at the sequence level; they examine sequence similarity and use a similarity threshold to identify duplicates.…”

Section: Duplicate Recordsmentioning

confidence: 99%

Literature Consistency of Bioinformatics Sequence Databases is Effective for Assessing Record Quality

Bouadjenek

Verspoor

Zobel

2017

Preprint

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Bioinformatics sequence databases such as Genbank or UniProt contain hundreds of millions of records of genomic data. These records are derived from direct submissions from individual laboratories, as well as from bulk submissions from large-scale sequencing centres; their diversity and scale means that they suffer from a range of data quality issues including errors, discrepancies, redundancies, ambiguities, incompleteness, and inconsistencies with the published literature. In this work, we seek to investigate and analyze the data quality of sequence databases from the perspective of a curator, who must detect anomalous and suspicious records.Specifically, we emphasize the detection of inconsistent records with respect to the literature.Focusing on GenBank, we propose a set of 24 quality indicators, which are based on treating a record as a query into the published literature, and then use query quality predictors. We then carry out an analysis that shows that the proposed quality indicators and the quality of the records have a mutual relationship, in which one depends on the other. We propose to represent recordliterature consistency as a vector of these quality indicators. By reducing the dimensionality of this representation for visualization purposes using Principal Component Analysis, we show that records which have been reported as inconsistent with the literature fall roughly in the same area, and therefore share similar characteristics. By manually analyzing records not previously known to be erroneous that fall in the same area than records know to be inconsistent, we show that 1 record out of 4 is inconsistent with respect to the literature. This high density of inconsistent record opens the way towards the development of automatic methods for the detection of faulty records. We conclude that literature inconsistency is a meaningful strategy for identifying suspicious records.

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Section: Duplicate Recordsmentioning

confidence: 99%

Literature Consistency of Bioinformatics Sequence Databases is Effective for Assessing Record Quality

Bouadjenek

Verspoor

Zobel

2017

Preprint

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“…Thirdly, the extracted noun phrases were compared with GO terms, and the number of matched phrases was stored along with the phrases. The comparison between extracted phrases and GO terms was based on string similarity between the 2, and the shortest path-based edit distance (SPED) technique [12] was used. The SPED technique is a variation of Markov random field-based edit distance (MRFED) and calculates the shortest path between 2 selected vertices of a graph.…”

Section: Datasets and Methodsmentioning

confidence: 99%

Building the process-drug–side effect network to discover the relationship between biological Processes and side effects

Lee

Song

et al. 2011

BMC Bioinformatics

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BackgroundSide effects are unwanted responses to drug treatment and are important resources for human phenotype information. The recent development of a database on side effects, the side effect resource (SIDER), is a first step in documenting the relationship between drugs and their side effects. It is, however, insufficient to simply find the association of drugs with biological processes; that relationship is crucial because drugs that influence biological processes can have an impact on phenotype. Therefore, knowing which processes respond to drugs that influence the phenotype will enable more effective and systematic study of the effect of drugs on phenotype. To the best of our knowledge, the relationship between biological processes and side effects of drugs has not yet been systematically researched.MethodsWe propose 3 steps for systematically searching relationships between drugs and biological processes: enrichment scores (ES) calculations, t-score calculation, and threshold-based filtering. Subsequently, the side effect-related biological processes are found by merging the drug-biological process network and the drug-side effect network. Evaluation is conducted in 2 ways: first, by discerning the number of biological processes discovered by our method that co-occur with Gene Ontology (GO) terms in relation to effects extracted from PubMed records using a text-mining technique and second, determining whether there is improvement in performance by limiting response processes by drugs sharing the same side effect to frequent ones alone.ResultsThe multi-level network (the process-drug-side effect network) was built by merging the drug-biological process network and the drug-side effect network. We generated a network of 74 drugs-168 side effects-2209 biological process relation resources. The preliminary results showed that the process-drug-side effect network was able to find meaningful relationships between biological processes and side effects in an efficient manner.ConclusionsWe propose a novel process-drug-side effect network for discovering the relationship between biological processes and side effects. By exploring the relationship between drugs and phenotypes through a multi-level network, the mechanisms underlying the effect of specific drugs on the human body may be understood.

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“…By analysing the support and confidence of each rule, the method can show the presence of erroneous data. Other approaches also use approximate string matching to compute metadata similarity [46,52,10]. However, as they focus only on metadata, the underlying interpretation is that duplicates are assumed to have high metadata similarity, or that their sequences are identical.…”

Section: Related Workmentioning

confidence: 99%

Automated Detection of Records in Biological Sequence Databases that are Inconsistent with the Literature

Bouadjenek

Verspoor

Zobel

2017

Preprint

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We investigate and analyse the data quality of nucleotide sequence databases with the objective of automatic detection of data anomalies and suspicious records. Specifically, we demonstrate that the published literature associated with each data record can be used to automatically evaluate its quality, by cross-checking the consistency of the key content of the database record with the referenced publications. Focusing on GenBank, we describe a set of quality indicators based on the relevance paradigm of information retrieval (IR). Then, we use these quality indicators to train an anomaly detection algorithm to classify records as "confident" or "suspicious".Our experiments on the PubMed Central collection show assessing the coherence between the literature and database records, through our algorithms, is an effective mechanism for assisting curators to perform data cleansing. Although fewer than 0.25% of the records in our data set are known to be faulty, we would expect that there are many more in GenBank that have not yet been identified. By automated comparison with literature they can be identified with a precision of up to 10% and a recall of up to 30%, while strongly outperforming several baselines. While these results leave substantial room for improvement, they reflect both the very imbalanced nature of the data, and the limited explicitly labelled data that is available.Overall, the obtained results show promise for the development of a new kind of approach to detecting lowquality and suspicious sequence records based on literature analysis and consistency. From a practical point of view, this will greatly help curators in identifying inconsistent records in large-scale sequence databases by highlighting records that are likely to be inconsistent with the literature.

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Detecting duplicate biological entities using Shortest Path Edit Distance

Cited by 16 publications

References 30 publications

Literature Consistency of Bioinformatics Sequence Databases is Effective for Assessing Record Quality

Literature Consistency of Bioinformatics Sequence Databases is Effective for Assessing Record Quality

Building the process-drug–side effect network to discover the relationship between biological Processes and side effects

Automated Detection of Records in Biological Sequence Databases that are Inconsistent with the Literature

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