Benchmarks for Measurement of Duplicate Detection Methods in Nucleotide Databases

Chen, Qingyu; Zobel, Justin; Verspoor, Karin

doi:10.1101/085324

Cited by 3 publications

(2 citation statements)

References 40 publications

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“…We assumed that we were provided with an appropriate dictionary for biomedical embeddings. We evaluated our model on a large benchmark dataset [10] consisting of 21 most heavily studied organisms in molecular biology. Our method was able to beat ML models with hand-crafted approach in 11 of these cases while it was within a F1-score of ± 5 for the remaining.…”

Section: Comparison With Existing Methodsmentioning

confidence: 99%

Distributed representations of tuples for entity resolution

EbraheemMuhammad¹,

ThirumuruganathanSaravanan²,

JotyShafiq

et al. 2018

Proc. VLDB Endow.

168

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Despite the efforts in 70+ years in all aspects of entity resolution (ER), there is still a high demand for democratizing ER -by reducing the heavy human involvement in labeling data, performing feature engineering, tuning parameters, and defining blocking functions. With the recent advances in deep learning, in particular distributed representations of words (a.k.a. word embeddings), we present a novel ER system, called DeepER, that achieves good accuracy, high efficiency, as well as ease-of-use (i.e., much less human efforts). We use sophisticated composition methods, namely uni-and bi-directional recurrent neural networks (RNNs) with long short term memory (LSTM) hidden units, to convert each tuple to a distributed representation (i.e., a vector), which can in turn be used to effectively capture similarities between tuples. We consider both the case where pre-trained word embeddings are available as well the case where they are not; we present ways to learn and tune the distributed representations that are customized for a specific ER task under different scenarios. We propose a locality sensitive hashing (LSH) based blocking approach that takes all attributes of a tuple into consideration and produces much smaller blocks, compared with traditional methods that consider only a few attributes. We evaluate our algorithms on multiple datasets (including benchmarks, biomedical data, as well as multi-lingual data) and the extensive experimental results show that DeepER outperforms existing solutions.

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Section: Comparison With Existing Methodsmentioning

confidence: 99%

Distributed representations of tuples for entity resolution

EbraheemMuhammad¹,

ThirumuruganathanSaravanan²,

JotyShafiq

et al. 2018

Proc. VLDB Endow.

168

View full text Add to dashboard Cite

show abstract

“…To our knowledge, our collection is the largest set of duplicate records merged in INSDC considered to date. Note that we have collected even larger datasets based on other strategies, including expert and automatic curation (51). We focus on this collection here, to analyse how submitters understand duplicates as one perspective.…”

Section: Characteristics Of the Duplicate Collectionmentioning

confidence: 99%

Duplicates, redundancies, and inconsistencies in the primary nucleotide databases: a descriptive study

Chen

Zobel

Verspoor

2016

Preprint

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GenBank, the EMBL European Nucleotide Archive and the DNA DataBank of Japan, known collectively as the International Nucleotide Sequence Database Collaboration or INSDC, are the three most significant nucleotide sequence databases. Their records are derived from laboratory work undertaken by different individuals, by different teams, with a range of technologies and assumptions and over a period of decades. As a consequence, they contain a great many duplicates, redundancies and inconsistencies, but neither the prevalence nor the characteristics of various types of duplicates have been rigorously assessed. Existing duplicate detection methods in bioinformatics only address specific duplicate types, with inconsistent assumptions; and the impact of duplicates in bioinformatics databases has not been carefully assessed, making it difficult to judge the value of such methods. Our goal is to assess the scale, kinds and impact of duplicates in bioinformatics databases, through a retrospective analysis of merged groups in INSDC databases. Our outcomes are threefold: (1) We analyse a benchmark dataset consisting of duplicates manually identified in INSDC-a dataset of 67 888 merged groups with 111 823 duplicate pairs across 21 organisms from INSDC databases -in terms of the prevalence, types and impacts of duplicates. (2) We categorize duplicates at both sequence and annotation level, with supporting quantitative statistics, showing that different organisms have different prevalence of distinct kinds of duplicate. (3) We show that the presence of duplicates has practical impact via a simple case study on duplicates, in terms of GC content and melting temperature. We demonstrate that duplicates not only introduce redundancy, but can lead to inconsistent results for certain tasks. Our findings lead to a better understanding of the problem of duplication in biological databases.Database URL: the merged records are available at https://cloudstor.aarnet.edu.au/plus/ index.php/s/Xef2fvsebBEAv9w

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Duplicates, redundancies and inconsistencies in the primary nucleotide databases: a descriptive study

Chen¹,

Zobel²,

Verspoor³

2017

Database

Self Cite

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GenBank, the EMBL European Nucleotide Archive and the DNA DataBank of Japan, known collectively as the International Nucleotide Sequence Database Collaboration or INSDC, are the three most significant nucleotide sequence databases. Their records are derived from laboratory work undertaken by different individuals, by different teams, with a range of technologies and assumptions and over a period of decades. As a consequence, they contain a great many duplicates, redundancies and inconsistencies, but neither the prevalence nor the characteristics of various types of duplicates have been rigorously assessed. Existing duplicate detection methods in bioinformatics only address specific duplicate types, with inconsistent assumptions; and the impact of duplicates in bioinformatics databases has not been carefully assessed, making it difficult to judge the value of such methods. Our goal is to assess the scale, kinds and impact of duplicates in bioinformatics databases, through a retrospective analysis of merged groups in INSDC databases. Our outcomes are threefold: (1) We analyse a benchmark dataset consisting of duplicates manually identified in INSDC—a dataset of 67 888 merged groups with 111 823 duplicate pairs across 21 organisms from INSDC databases – in terms of the prevalence, types and impacts of duplicates. (2) We categorize duplicates at both sequence and annotation level, with supporting quantitative statistics, showing that different organisms have different prevalence of distinct kinds of duplicate. (3) We show that the presence of duplicates has practical impact via a simple case study on duplicates, in terms of GC content and melting temperature. We demonstrate that duplicates not only introduce redundancy, but can lead to inconsistent results for certain tasks. Our findings lead to a better understanding of the problem of duplication in biological databases.Database URL: the merged records are available at https://cloudstor.aarnet.edu.au/plus/index.php/s/Xef2fvsebBEAv9w

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Benchmarks for Measurement of Duplicate Detection Methods in Nucleotide Databases

Cited by 3 publications

References 40 publications

Distributed representations of tuples for entity resolution

Distributed representations of tuples for entity resolution

Duplicates, redundancies, and inconsistencies in the primary nucleotide databases: a descriptive study

Duplicates, redundancies and inconsistencies in the primary nucleotide databases: a descriptive study

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