A detailed error analysis of 13 kernel methods for protein–protein interaction extraction
Identifieur interne : 000870 ( Ncbi/Merge ); précédent : 000869; suivant : 000871A detailed error analysis of 13 kernel methods for protein–protein interaction extraction
Auteurs : Domonkos Tikk [Allemagne, Hongrie] ; Illés Solt [Allemagne, Hongrie] ; Philippe Thomas [Allemagne] ; Ulf Leser [Allemagne]Source :
- BMC Bioinformatics [ 1471-2105 ] ; 2013.
Abstract
Kernel-based classification is the current state-of-the-art for extracting pairs of interacting proteins (PPIs) from free text. Various proposals have been put forward, which diverge especially in the specific kernel function, the type of input representation, and the feature sets. These proposals are regularly compared to each other regarding their overall performance on different gold standard corpora, but little is known about their respective performance on the instance level.
We report on a detailed analysis of the shared characteristics and the differences between 13 current methods using five PPI corpora. We identified a large number of rather difficult (misclassified by most methods) and easy (correctly classified by most methods) PPIs. We show that kernels using the same input representation perform similarly on these pairs and that building ensembles using dissimilar kernels leads to significant performance gain. However, our analysis also reveals that characteristics shared between difficult pairs are few, which lowers the hope that new methods, if built along the same line as current ones, will deliver breakthroughs in extraction performance.
Our experiments show that current methods do not seem to do very well in capturing the shared characteristics of positive PPI pairs, which must also be attributed to the heterogeneity of the (still very few) available corpora. Our analysis suggests that performance improvements shall be sought after rather in novel feature sets than in novel kernel functions.
Url:
DOI: 10.1186/1471-2105-14-12
PubMed: 23323857
PubMed Central: 3680070
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<front><div type="abstract" xml:lang="en"><sec><title>Background</title>
<p>Kernel-based classification is the current state-of-the-art for extracting pairs of interacting proteins (PPIs) from free text. Various proposals have been put forward, which diverge especially in the specific kernel function, the type of input representation, and the feature sets. These proposals are regularly compared to each other regarding their overall performance on different gold standard corpora, but little is known about their respective performance on the instance level.</p>
</sec>
<sec><title>Results</title>
<p>We report on a detailed analysis of the shared characteristics and the differences between 13 current methods using five PPI corpora. We identified a large number of rather difficult (misclassified by most methods) and easy (correctly classified by most methods) PPIs. We show that kernels using the same input representation perform similarly on these pairs and that building ensembles using dissimilar kernels leads to significant performance gain. However, our analysis also reveals that characteristics shared between difficult pairs are few, which lowers the hope that new methods, if built along the same line as current ones, will deliver breakthroughs in extraction performance.</p>
</sec>
<sec><title>Conclusions</title>
<p>Our experiments show that current methods do not seem to do very well in capturing the shared characteristics of positive PPI pairs, which must also be attributed to the heterogeneity of the (still very few) available corpora. Our analysis suggests that performance improvements shall be sought after rather in novel feature sets than in novel kernel functions.</p>
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<pmc article-type="research-article" xml:lang="en"><pmc-dir>properties open_access</pmc-dir>
<front><journal-meta><journal-id journal-id-type="nlm-ta">BMC Bioinformatics</journal-id>
<journal-id journal-id-type="iso-abbrev">BMC Bioinformatics</journal-id>
<journal-title-group><journal-title>BMC Bioinformatics</journal-title>
</journal-title-group>
<issn pub-type="epub">1471-2105</issn>
<publisher><publisher-name>BioMed Central</publisher-name>
</publisher>
</journal-meta>
<article-meta><article-id pub-id-type="pmid">23323857</article-id>
<article-id pub-id-type="pmc">3680070</article-id>
<article-id pub-id-type="publisher-id">1471-2105-14-12</article-id>
<article-id pub-id-type="doi">10.1186/1471-2105-14-12</article-id>
<article-categories><subj-group subj-group-type="heading"><subject>Research Article</subject>
</subj-group>
</article-categories>
<title-group><article-title>A detailed error analysis of 13 kernel methods for protein–protein interaction extraction</article-title>
</title-group>
<contrib-group><contrib contrib-type="author" corresp="yes" id="A1"><name><surname>Tikk</surname>
<given-names>Domonkos</given-names>
</name>
<xref ref-type="aff" rid="I1">1</xref>
<xref ref-type="aff" rid="I2">2</xref>
<email>tikk@informatik.hu-berlin.de</email>
</contrib>
<contrib contrib-type="author" id="A2"><name><surname>Solt</surname>
<given-names>Illés</given-names>
</name>
<xref ref-type="aff" rid="I1">1</xref>
<xref ref-type="aff" rid="I3">3</xref>
<email>solt@tmit.bme.hu</email>
</contrib>
<contrib contrib-type="author" id="A3"><name><surname>Thomas</surname>
<given-names>Philippe</given-names>
</name>
<xref ref-type="aff" rid="I1">1</xref>
<email>thomas@informatik.hu-berlin.de</email>
</contrib>
<contrib contrib-type="author" id="A4"><name><surname>Leser</surname>
<given-names>Ulf</given-names>
</name>
<xref ref-type="aff" rid="I1">1</xref>
<email>leser@informatik.hu-berlin.de</email>
</contrib>
</contrib-group>
<aff id="I1"><label>1</label>
Knowledge Management in Bioinformatics, Computer Science Department, Humboldt-Universität zu Berlin, 10099 Berlin, Germany</aff>
<aff id="I2"><label>2</label>
Software Engineering Institute, Óbuda University, 1034 Budapest, Hungary</aff>
<aff id="I3"><label>3</label>
Department of Telecommunications and Telematics, Budapest University of Technology and Economics, 1117 Budapest, Hungary</aff>
<pub-date pub-type="collection"><year>2013</year>
</pub-date>
<pub-date pub-type="epub"><day>16</day>
<month>1</month>
<year>2013</year>
</pub-date>
<volume>14</volume>
<fpage>12</fpage>
<lpage>12</lpage>
<history><date date-type="received"><day>4</day>
<month>11</month>
<year>2012</year>
</date>
<date date-type="accepted"><day>19</day>
<month>12</month>
<year>2012</year>
</date>
</history>
<permissions><copyright-statement>Copyright © 2013 Tikk et al.; licensee BioMed Central Ltd.</copyright-statement>
<copyright-year>2013</copyright-year>
<copyright-holder>Tikk et al.; licensee BioMed Central Ltd.</copyright-holder>
<license license-type="open-access" xlink:href="http://creativecommons.org/licenses/by/2.0"><license-p>This is an Open Access article distributed under the terms of the Creative Commons Attribution License (<ext-link ext-link-type="uri" xlink:href="http://creativecommons.org/licenses/by/2.0">http://creativecommons.org/licenses/by/2.0</ext-link>
), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.</license-p>
</license>
</permissions>
<self-uri xlink:href="http://www.biomedcentral.com/1471-2105/14/12"></self-uri>
<abstract><sec><title>Background</title>
<p>Kernel-based classification is the current state-of-the-art for extracting pairs of interacting proteins (PPIs) from free text. Various proposals have been put forward, which diverge especially in the specific kernel function, the type of input representation, and the feature sets. These proposals are regularly compared to each other regarding their overall performance on different gold standard corpora, but little is known about their respective performance on the instance level.</p>
</sec>
<sec><title>Results</title>
<p>We report on a detailed analysis of the shared characteristics and the differences between 13 current methods using five PPI corpora. We identified a large number of rather difficult (misclassified by most methods) and easy (correctly classified by most methods) PPIs. We show that kernels using the same input representation perform similarly on these pairs and that building ensembles using dissimilar kernels leads to significant performance gain. However, our analysis also reveals that characteristics shared between difficult pairs are few, which lowers the hope that new methods, if built along the same line as current ones, will deliver breakthroughs in extraction performance.</p>
</sec>
<sec><title>Conclusions</title>
<p>Our experiments show that current methods do not seem to do very well in capturing the shared characteristics of positive PPI pairs, which must also be attributed to the heterogeneity of the (still very few) available corpora. Our analysis suggests that performance improvements shall be sought after rather in novel feature sets than in novel kernel functions.</p>
</sec>
</abstract>
<kwd-group><kwd>Protein–protein interaction</kwd>
<kwd>Relation extraction</kwd>
<kwd>Kernel methods</kwd>
<kwd>Error analysis</kwd>
<kwd>Kernel similarity</kwd>
</kwd-group>
</article-meta>
</front>
</pmc>
<affiliations><list><country><li>Allemagne</li>
<li>Hongrie</li>
</country>
<region><li>Berlin</li>
<li>Hongrie centrale</li>
</region>
<settlement><li>Berlin</li>
<li>Budapest</li>
</settlement>
</list>
<tree><country name="Allemagne"><region name="Berlin"><name sortKey="Tikk, Domonkos" sort="Tikk, Domonkos" uniqKey="Tikk D" first="Domonkos" last="Tikk">Domonkos Tikk</name>
</region>
<name sortKey="Leser, Ulf" sort="Leser, Ulf" uniqKey="Leser U" first="Ulf" last="Leser">Ulf Leser</name>
<name sortKey="Solt, Illes" sort="Solt, Illes" uniqKey="Solt I" first="Illés" last="Solt">Illés Solt</name>
<name sortKey="Thomas, Philippe" sort="Thomas, Philippe" uniqKey="Thomas P" first="Philippe" last="Thomas">Philippe Thomas</name>
</country>
<country name="Hongrie"><region name="Hongrie centrale"><name sortKey="Tikk, Domonkos" sort="Tikk, Domonkos" uniqKey="Tikk D" first="Domonkos" last="Tikk">Domonkos Tikk</name>
</region>
<name sortKey="Solt, Illes" sort="Solt, Illes" uniqKey="Solt I" first="Illés" last="Solt">Illés Solt</name>
</country>
</tree>
</affiliations>
</record>
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