Global Vectors Representation of Protein Sequences and Its Application for Predicting Self-Interacting Proteins with Multi-Grained Cascade Forest Model
Identifieur interne : 000295 ( Pmc/Checkpoint ); précédent : 000294; suivant : 000296Global Vectors Representation of Protein Sequences and Its Application for Predicting Self-Interacting Proteins with Multi-Grained Cascade Forest Model
Auteurs : Zhan-Heng Chen [République populaire de Chine] ; Zhu-Hong You [République populaire de Chine] ; Wen-Bo Zhang [République populaire de Chine] ; Yan-Bin Wang ; Li Cheng [République populaire de Chine] ; Daniyal AlghazzawiSource :
- Genes [ 2073-4425 ] ; 2019.
Abstract
Self-interacting proteins (SIPs) is of paramount importance in current molecular biology. There have been developed a number of traditional biological experiment methods for predicting SIPs in the past few years. However, these methods are costly, time-consuming and inefficient, and often limit their usage for predicting SIPs. Therefore, the development of computational method emerges at the times require. In this paper, we for the first time proposed a novel deep learning model which combined natural language processing (NLP) method for potential SIPs prediction from the protein sequence information. More specifically, the protein sequence is de novo assembled by
Url:
DOI: 10.3390/genes10110924
PubMed: 31726752
PubMed Central: 6896115
Affiliations:
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<sourceDesc><biblStruct><analytic><title xml:lang="en" level="a" type="main">Global Vectors Representation of Protein Sequences and Its Application for Predicting Self-Interacting Proteins with Multi-Grained Cascade Forest Model</title>
<author><name sortKey="Chen, Zhan Heng" sort="Chen, Zhan Heng" uniqKey="Chen Z" first="Zhan-Heng" last="Chen">Zhan-Heng Chen</name>
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<author><name sortKey="Zhang, Wen Bo" sort="Zhang, Wen Bo" uniqKey="Zhang W" first="Wen-Bo" last="Zhang">Wen-Bo Zhang</name>
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<author><name sortKey="Cheng, Li" sort="Cheng, Li" uniqKey="Cheng L" first="Li" last="Cheng">Li Cheng</name>
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<author><name sortKey="Alghazzawi, Daniyal" sort="Alghazzawi, Daniyal" uniqKey="Alghazzawi D" first="Daniyal" last="Alghazzawi">Daniyal Alghazzawi</name>
<affiliation><nlm:aff id="af3-genes-10-00924">Department of Information Systems, King Abdulaziz University, Jeddah 21589, Saudi Arabia;<email>dghazzawi@kau.edu.sa</email>
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<series><title level="j">Genes</title>
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<front><div type="abstract" xml:lang="en"><p>Self-interacting proteins (SIPs) is of paramount importance in current molecular biology. There have been developed a number of traditional biological experiment methods for predicting SIPs in the past few years. However, these methods are costly, time-consuming and inefficient, and often limit their usage for predicting SIPs. Therefore, the development of computational method emerges at the times require. In this paper, we for the first time proposed a novel deep learning model which combined natural language processing (NLP) method for potential SIPs prediction from the protein sequence information. More specifically, the protein sequence is de novo assembled by <italic>k-mers</italic>
. Then, we obtained the global vectors representation for each protein sequences by using natural language processing (NLP) technique. Finally, based on the knowledge of known self-interacting and non-interacting proteins, a multi-grained cascade forest model is trained to predict SIPs. Comprehensive experiments were performed on <italic>yeast</italic>
and <italic>human</italic>
datasets, which obtained an accuracy rate of 91.45% and 93.12%, respectively. From our evaluations, the experimental results show that the use of amino acid semantics information is very helpful for addressing the problem of sequences containing both self-interacting and non-interacting pairs of proteins. This work would have potential applications for various biological classification problems.</p>
</div>
</front>
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<pmc article-type="research-article"><pmc-dir>properties open_access</pmc-dir>
<front><journal-meta><journal-id journal-id-type="nlm-ta">Genes (Basel)</journal-id>
<journal-id journal-id-type="iso-abbrev">Genes (Basel)</journal-id>
<journal-id journal-id-type="publisher-id">genes</journal-id>
<journal-title-group><journal-title>Genes</journal-title>
</journal-title-group>
<issn pub-type="epub">2073-4425</issn>
<publisher><publisher-name>MDPI</publisher-name>
</publisher>
</journal-meta>
<article-meta><article-id pub-id-type="pmid">31726752</article-id>
<article-id pub-id-type="pmc">6896115</article-id>
<article-id pub-id-type="doi">10.3390/genes10110924</article-id>
<article-id pub-id-type="publisher-id">genes-10-00924</article-id>
<article-categories><subj-group subj-group-type="heading"><subject>Article</subject>
</subj-group>
</article-categories>
<title-group><article-title>Global Vectors Representation of Protein Sequences and Its Application for Predicting Self-Interacting Proteins with Multi-Grained Cascade Forest Model</article-title>
</title-group>
<contrib-group><contrib contrib-type="author"><name><surname>Chen</surname>
<given-names>Zhan-Heng</given-names>
</name>
<xref ref-type="aff" rid="af1-genes-10-00924">1</xref>
<xref ref-type="aff" rid="af2-genes-10-00924">2</xref>
</contrib>
<contrib contrib-type="author"><name><surname>You</surname>
<given-names>Zhu-Hong</given-names>
</name>
<xref ref-type="aff" rid="af1-genes-10-00924">1</xref>
<xref ref-type="aff" rid="af2-genes-10-00924">2</xref>
<xref rid="c1-genes-10-00924" ref-type="corresp">*</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Zhang</surname>
<given-names>Wen-Bo</given-names>
</name>
<xref ref-type="aff" rid="af1-genes-10-00924">1</xref>
<xref ref-type="aff" rid="af2-genes-10-00924">2</xref>
</contrib>
<contrib contrib-type="author"><contrib-id contrib-id-type="orcid" authenticated="true">https://orcid.org/0000-0003-1682-5712</contrib-id>
<name><surname>Wang</surname>
<given-names>Yan-Bin</given-names>
</name>
<xref ref-type="aff" rid="af1-genes-10-00924">1</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Cheng</surname>
<given-names>Li</given-names>
</name>
<xref ref-type="aff" rid="af1-genes-10-00924">1</xref>
<xref ref-type="aff" rid="af2-genes-10-00924">2</xref>
</contrib>
<contrib contrib-type="author"><contrib-id contrib-id-type="orcid" authenticated="true">https://orcid.org/0000-0002-5533-3203</contrib-id>
<name><surname>Alghazzawi</surname>
<given-names>Daniyal</given-names>
</name>
<xref ref-type="aff" rid="af3-genes-10-00924">3</xref>
</contrib>
</contrib-group>
<aff id="af1-genes-10-00924"><label>1</label>
The Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Urumqi 830011, China;<email>chenzhanheng17@mails.ucas.ac.cn</email>
(Z.-H.C.);<email>zhang_wen_bo@foxmail.com</email>
(W.-B.Z.);<email>wangyanbin15@mails.ucas.ac.cn</email>
(Y.-B.W.);<email>chengli@ms.xjb.ac.cn</email>
(L.C.)</aff>
<aff id="af2-genes-10-00924"><label>2</label>
University of Chinese Academy of Sciences, Beijing 100049, China</aff>
<aff id="af3-genes-10-00924"><label>3</label>
Department of Information Systems, King Abdulaziz University, Jeddah 21589, Saudi Arabia;<email>dghazzawi@kau.edu.sa</email>
</aff>
<author-notes><corresp id="c1-genes-10-00924"><label>*</label>
Correspondence: <email>zhuhongyou@ms.xjb.ac.cn</email>
or <email>zhuhongyou@gmail.com</email>
; Tel.: +86-991-3835-823</corresp>
</author-notes>
<pub-date pub-type="epub"><day>12</day>
<month>11</month>
<year>2019</year>
</pub-date>
<pub-date pub-type="collection"><month>11</month>
<year>2019</year>
</pub-date>
<volume>10</volume>
<issue>11</issue>
<elocation-id>924</elocation-id>
<history><date date-type="received"><day>31</day>
<month>8</month>
<year>2019</year>
</date>
<date date-type="accepted"><day>06</day>
<month>11</month>
<year>2019</year>
</date>
</history>
<permissions><copyright-statement>© 2019 by the authors.</copyright-statement>
<copyright-year>2019</copyright-year>
<license license-type="open-access"><license-p>Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (<ext-link ext-link-type="uri" xlink:href="http://creativecommons.org/licenses/by/4.0/">http://creativecommons.org/licenses/by/4.0/</ext-link>
).</license-p>
</license>
</permissions>
<abstract><p>Self-interacting proteins (SIPs) is of paramount importance in current molecular biology. There have been developed a number of traditional biological experiment methods for predicting SIPs in the past few years. However, these methods are costly, time-consuming and inefficient, and often limit their usage for predicting SIPs. Therefore, the development of computational method emerges at the times require. In this paper, we for the first time proposed a novel deep learning model which combined natural language processing (NLP) method for potential SIPs prediction from the protein sequence information. More specifically, the protein sequence is de novo assembled by <italic>k-mers</italic>
. Then, we obtained the global vectors representation for each protein sequences by using natural language processing (NLP) technique. Finally, based on the knowledge of known self-interacting and non-interacting proteins, a multi-grained cascade forest model is trained to predict SIPs. Comprehensive experiments were performed on <italic>yeast</italic>
and <italic>human</italic>
datasets, which obtained an accuracy rate of 91.45% and 93.12%, respectively. From our evaluations, the experimental results show that the use of amino acid semantics information is very helpful for addressing the problem of sequences containing both self-interacting and non-interacting pairs of proteins. This work would have potential applications for various biological classification problems.</p>
</abstract>
<kwd-group><kwd>self-interacting proteins</kwd>
<kwd>de novo protein sequence</kwd>
<kwd>global vector representation</kwd>
<kwd>multi-grained cascade forest</kwd>
</kwd-group>
</article-meta>
</front>
<floats-group><fig id="genes-10-00924-f001" orientation="portrait" position="float"><label>Figure 1</label>
<caption><p>De novo assembled protein sequences by <italic>3-mer</italic>
.</p>
</caption>
<graphic xlink:href="genes-10-00924-g001"></graphic>
</fig>
<fig id="genes-10-00924-f002" orientation="portrait" position="float"><label>Figure 2</label>
<caption><p>Process of multi-grained scanning.</p>
</caption>
<graphic xlink:href="genes-10-00924-g002"></graphic>
</fig>
<fig id="genes-10-00924-f003" orientation="portrait" position="float"><label>Figure 3</label>
<caption><p>Cascade forest model.</p>
</caption>
<graphic xlink:href="genes-10-00924-g003"></graphic>
</fig>
<fig id="genes-10-00924-f004" orientation="portrait" position="float"><label>Figure 4</label>
<caption><p>The receiver operating characteristic (ROC) curve of proposed model on <italic>yeast</italic>
dataset.</p>
</caption>
<graphic xlink:href="genes-10-00924-g004"></graphic>
</fig>
<fig id="genes-10-00924-f005" orientation="portrait" position="float"><label>Figure 5</label>
<caption><p>The ROC curve of proposed model on <italic>human</italic>
dataset.</p>
</caption>
<graphic xlink:href="genes-10-00924-g005"></graphic>
</fig>
<table-wrap id="genes-10-00924-t001" orientation="portrait" position="float"><object-id pub-id-type="pii">genes-10-00924-t001_Table 1</object-id>
<label>Table 1</label>
<caption><p>Confusion matrix. TN: true negative, FN: false negative, FP: false positive, TP: true positive.</p>
</caption>
<table frame="hsides" rules="groups"><thead><tr><th rowspan="2" colspan="2" align="center" valign="middle" style="border-top:solid thin;border-bottom:solid thin"></th>
<th colspan="2" align="center" valign="middle" style="border-top:solid thin;border-bottom:solid thin" rowspan="1">Predict</th>
</tr>
<tr><th align="center" valign="middle" style="border-bottom:solid thin" rowspan="1" colspan="1">Negative</th>
<th align="center" valign="middle" style="border-bottom:solid thin" rowspan="1" colspan="1">Positive</th>
</tr>
</thead>
<tbody><tr><td rowspan="2" align="center" valign="middle" style="border-bottom:solid thin" colspan="1">Actual</td>
<td align="center" valign="middle" rowspan="1" colspan="1">Negative</td>
<td align="center" valign="middle" rowspan="1" colspan="1">TN</td>
<td align="center" valign="middle" rowspan="1" colspan="1">FN</td>
</tr>
<tr><td align="center" valign="middle" style="border-bottom:solid thin" rowspan="1" colspan="1">Positive</td>
<td align="center" valign="middle" style="border-bottom:solid thin" rowspan="1" colspan="1">FP</td>
<td align="center" valign="middle" style="border-bottom:solid thin" rowspan="1" colspan="1">TP</td>
</tr>
</tbody>
</table>
</table-wrap>
<table-wrap id="genes-10-00924-t002" orientation="portrait" position="float"><object-id pub-id-type="pii">genes-10-00924-t002_Table 2</object-id>
<label>Table 2</label>
<caption><p>Performance of our proposed model on the two benchmark datasets. Acc: Accuracy; TNR: True negative rate; F1-score: Measuring the overall performance of the classification model; MCC: Matthews correlation.</p>
</caption>
<table frame="hsides" rules="groups"><thead><tr><th align="center" valign="middle" style="border-top:solid thin;border-bottom:solid thin" rowspan="1" colspan="1">Datasets</th>
<th align="center" valign="middle" style="border-top:solid thin;border-bottom:solid thin" rowspan="1" colspan="1">Acc (%)</th>
<th align="center" valign="middle" style="border-top:solid thin;border-bottom:solid thin" rowspan="1" colspan="1">TNR (%)</th>
<th align="center" valign="middle" style="border-top:solid thin;border-bottom:solid thin" rowspan="1" colspan="1">F1-Score (%)</th>
<th align="center" valign="middle" style="border-top:solid thin;border-bottom:solid thin" rowspan="1" colspan="1">MCC</th>
</tr>
</thead>
<tbody><tr><td align="center" valign="middle" rowspan="1" colspan="1"><bold><italic>yeast</italic>
</bold>
</td>
<td align="center" valign="middle" rowspan="1" colspan="1">91.45</td>
<td align="center" valign="middle" rowspan="1" colspan="1">99.71</td>
<td align="center" valign="middle" rowspan="1" colspan="1">37.56</td>
<td align="center" valign="middle" rowspan="1" colspan="1">0.4389</td>
</tr>
<tr><td align="center" valign="middle" style="border-bottom:solid thin" rowspan="1" colspan="1"><bold><italic>human</italic>
</bold>
</td>
<td align="center" valign="middle" style="border-bottom:solid thin" rowspan="1" colspan="1">93.12</td>
<td align="center" valign="middle" style="border-bottom:solid thin" rowspan="1" colspan="1">99.57</td>
<td align="center" valign="middle" style="border-bottom:solid thin" rowspan="1" colspan="1">39.10</td>
<td align="center" valign="middle" style="border-bottom:solid thin" rowspan="1" colspan="1">0.4421</td>
</tr>
</tbody>
</table>
</table-wrap>
<table-wrap id="genes-10-00924-t003" orientation="portrait" position="float"><object-id pub-id-type="pii">genes-10-00924-t003_Table 3</object-id>
<label>Table 3</label>
<caption><p>Performance of our proposed model and other previous methods on <italic>yeast</italic>
dataset. AUC: Area under curve.</p>
</caption>
<table frame="hsides" rules="groups"><thead><tr><th align="center" valign="middle" style="border-top:solid thin;border-bottom:solid thin" rowspan="1" colspan="1">Model</th>
<th align="center" valign="middle" style="border-top:solid thin;border-bottom:solid thin" rowspan="1" colspan="1">Acc (%)</th>
<th align="center" valign="middle" style="border-top:solid thin;border-bottom:solid thin" rowspan="1" colspan="1">TNR (%)</th>
<th align="center" valign="middle" style="border-top:solid thin;border-bottom:solid thin" rowspan="1" colspan="1">F1-Score (%)</th>
<th align="center" valign="middle" style="border-top:solid thin;border-bottom:solid thin" rowspan="1" colspan="1">MCC</th>
<th align="center" valign="middle" style="border-top:solid thin;border-bottom:solid thin" rowspan="1" colspan="1">AUC</th>
</tr>
</thead>
<tbody><tr><td align="center" valign="middle" rowspan="1" colspan="1">SLIPPER [<xref rid="B40-genes-10-00924" ref-type="bibr">40</xref>
]</td>
<td align="center" valign="middle" rowspan="1" colspan="1">71.90</td>
<td align="center" valign="middle" rowspan="1" colspan="1">72.18</td>
<td align="center" valign="middle" rowspan="1" colspan="1">36.16</td>
<td align="center" valign="middle" rowspan="1" colspan="1">0.2842</td>
<td align="center" valign="middle" rowspan="1" colspan="1">0.7723</td>
</tr>
<tr><td align="center" valign="middle" rowspan="1" colspan="1">DXECPPI [<xref rid="B41-genes-10-00924" ref-type="bibr">41</xref>
]</td>
<td align="center" valign="middle" rowspan="1" colspan="1">87.46</td>
<td align="center" valign="middle" rowspan="1" colspan="1">94.93</td>
<td align="center" valign="middle" rowspan="1" colspan="1">34.89</td>
<td align="center" valign="middle" rowspan="1" colspan="1">0.2825</td>
<td align="center" valign="middle" rowspan="1" colspan="1">0.6934</td>
</tr>
<tr><td align="center" valign="middle" rowspan="1" colspan="1">PPIevo [<xref rid="B42-genes-10-00924" ref-type="bibr">42</xref>
]</td>
<td align="center" valign="middle" rowspan="1" colspan="1">66.28</td>
<td align="center" valign="middle" rowspan="1" colspan="1">87.46</td>
<td align="center" valign="middle" rowspan="1" colspan="1">28.92</td>
<td align="center" valign="middle" rowspan="1" colspan="1">0.1801</td>
<td align="center" valign="middle" rowspan="1" colspan="1">0.6728</td>
</tr>
<tr><td align="center" valign="middle" rowspan="1" colspan="1">LocFuse [<xref rid="B43-genes-10-00924" ref-type="bibr">43</xref>
]</td>
<td align="center" valign="middle" rowspan="1" colspan="1">66.66</td>
<td align="center" valign="middle" rowspan="1" colspan="1">68.10</td>
<td align="center" valign="middle" rowspan="1" colspan="1">27.53</td>
<td align="center" valign="middle" rowspan="1" colspan="1">0.1577</td>
<td align="center" valign="middle" rowspan="1" colspan="1">0.7087</td>
</tr>
<tr><td align="center" valign="middle" rowspan="1" colspan="1">CRS [<xref rid="B10-genes-10-00924" ref-type="bibr">10</xref>
]</td>
<td align="center" valign="middle" rowspan="1" colspan="1">72.69</td>
<td align="center" valign="middle" rowspan="1" colspan="1">74.37</td>
<td align="center" valign="middle" rowspan="1" colspan="1">33.05</td>
<td align="center" valign="middle" rowspan="1" colspan="1">0.2368</td>
<td align="center" valign="middle" rowspan="1" colspan="1">0.7115</td>
</tr>
<tr><td align="center" valign="middle" rowspan="1" colspan="1">SPAR [<xref rid="B10-genes-10-00924" ref-type="bibr">10</xref>
]</td>
<td align="center" valign="middle" rowspan="1" colspan="1">76.96</td>
<td align="center" valign="middle" rowspan="1" colspan="1">80.02</td>
<td align="center" valign="middle" rowspan="1" colspan="1">34.54</td>
<td align="center" valign="middle" rowspan="1" colspan="1">0.2484</td>
<td align="center" valign="middle" rowspan="1" colspan="1">0.7455</td>
</tr>
<tr><td align="center" valign="middle" style="border-bottom:solid thin" rowspan="1" colspan="1"><bold>Proposed method</bold>
</td>
<td align="center" valign="middle" style="border-bottom:solid thin" rowspan="1" colspan="1"><bold>91.45</bold>
</td>
<td align="center" valign="middle" style="border-bottom:solid thin" rowspan="1" colspan="1"><bold>99.71</bold>
</td>
<td align="center" valign="middle" style="border-bottom:solid thin" rowspan="1" colspan="1"><bold>37.56</bold>
</td>
<td align="center" valign="middle" style="border-bottom:solid thin" rowspan="1" colspan="1"><bold>0.4389</bold>
</td>
<td align="center" valign="middle" style="border-bottom:solid thin" rowspan="1" colspan="1"><bold>0.7881</bold>
</td>
</tr>
</tbody>
</table>
</table-wrap>
<table-wrap id="genes-10-00924-t004" orientation="portrait" position="float"><object-id pub-id-type="pii">genes-10-00924-t004_Table 4</object-id>
<label>Table 4</label>
<caption><p>Performance of our proposed model and other previous methods on <italic>human</italic>
dataset.</p>
</caption>
<table frame="hsides" rules="groups"><thead><tr><th align="center" valign="middle" style="border-top:solid thin;border-bottom:solid thin" rowspan="1" colspan="1">Model</th>
<th align="center" valign="middle" style="border-top:solid thin;border-bottom:solid thin" rowspan="1" colspan="1">Acc (%)</th>
<th align="center" valign="middle" style="border-top:solid thin;border-bottom:solid thin" rowspan="1" colspan="1">TNR (%)</th>
<th align="center" valign="middle" style="border-top:solid thin;border-bottom:solid thin" rowspan="1" colspan="1">F1-score (%)</th>
<th align="center" valign="middle" style="border-top:solid thin;border-bottom:solid thin" rowspan="1" colspan="1">MCC </th>
<th align="center" valign="middle" style="border-top:solid thin;border-bottom:solid thin" rowspan="1" colspan="1">AUC</th>
</tr>
</thead>
<tbody><tr><td align="center" valign="middle" rowspan="1" colspan="1">SLIPPER [<xref rid="B40-genes-10-00924" ref-type="bibr">40</xref>
]</td>
<td align="center" valign="middle" rowspan="1" colspan="1">91.10</td>
<td align="center" valign="middle" rowspan="1" colspan="1">95.06</td>
<td align="center" valign="middle" rowspan="1" colspan="1"><bold>46.82</bold>
</td>
<td align="center" valign="middle" rowspan="1" colspan="1">0.4197</td>
<td align="center" valign="middle" rowspan="1" colspan="1"><bold>0.8723</bold>
</td>
</tr>
<tr><td align="center" valign="middle" rowspan="1" colspan="1">DXECPPI [<xref rid="B41-genes-10-00924" ref-type="bibr">41</xref>
]</td>
<td align="center" valign="middle" rowspan="1" colspan="1">30.90</td>
<td align="center" valign="middle" rowspan="1" colspan="1">25.83</td>
<td align="center" valign="middle" rowspan="1" colspan="1">17.28</td>
<td align="center" valign="middle" rowspan="1" colspan="1">0.0825</td>
<td align="center" valign="middle" rowspan="1" colspan="1">0.5806</td>
</tr>
<tr><td align="center" valign="middle" rowspan="1" colspan="1">PPIevo [<xref rid="B42-genes-10-00924" ref-type="bibr">42</xref>
]</td>
<td align="center" valign="middle" rowspan="1" colspan="1">78.04</td>
<td align="center" valign="middle" rowspan="1" colspan="1">25.82</td>
<td align="center" valign="middle" rowspan="1" colspan="1">27.73</td>
<td align="center" valign="middle" rowspan="1" colspan="1">0.2082</td>
<td align="center" valign="middle" rowspan="1" colspan="1">0.7329</td>
</tr>
<tr><td align="center" valign="middle" rowspan="1" colspan="1">LocFuse [<xref rid="B43-genes-10-00924" ref-type="bibr">43</xref>
]</td>
<td align="center" valign="middle" rowspan="1" colspan="1">80.66</td>
<td align="center" valign="middle" rowspan="1" colspan="1">80.50</td>
<td align="center" valign="middle" rowspan="1" colspan="1">27.65</td>
<td align="center" valign="middle" rowspan="1" colspan="1">0.2026</td>
<td align="center" valign="middle" rowspan="1" colspan="1">0.7087</td>
</tr>
<tr><td align="center" valign="middle" rowspan="1" colspan="1">CRS [<xref rid="B10-genes-10-00924" ref-type="bibr">10</xref>
]</td>
<td align="center" valign="middle" rowspan="1" colspan="1">91.54</td>
<td align="center" valign="middle" rowspan="1" colspan="1">96.72</td>
<td align="center" valign="middle" rowspan="1" colspan="1">36.83</td>
<td align="center" valign="middle" rowspan="1" colspan="1">0.3633</td>
<td align="center" valign="middle" rowspan="1" colspan="1">0.8196</td>
</tr>
<tr><td align="center" valign="middle" rowspan="1" colspan="1">SPAR [<xref rid="B10-genes-10-00924" ref-type="bibr">10</xref>
]</td>
<td align="center" valign="middle" rowspan="1" colspan="1">92.09</td>
<td align="center" valign="middle" rowspan="1" colspan="1">97.40</td>
<td align="center" valign="middle" rowspan="1" colspan="1">41.13</td>
<td align="center" valign="middle" rowspan="1" colspan="1">0.3836</td>
<td align="center" valign="middle" rowspan="1" colspan="1">0.8229</td>
</tr>
<tr><td align="center" valign="middle" style="border-bottom:solid thin" rowspan="1" colspan="1"><bold>Proposed method</bold>
</td>
<td align="center" valign="middle" style="border-bottom:solid thin" rowspan="1" colspan="1"><bold>93.12</bold>
</td>
<td align="center" valign="middle" style="border-bottom:solid thin" rowspan="1" colspan="1"><bold>99.57</bold>
</td>
<td align="center" valign="middle" style="border-bottom:solid thin" rowspan="1" colspan="1">39.10</td>
<td align="center" valign="middle" style="border-bottom:solid thin" rowspan="1" colspan="1"><bold>0.4421</bold>
</td>
<td align="center" valign="middle" style="border-bottom:solid thin" rowspan="1" colspan="1">0.8524</td>
</tr>
</tbody>
</table>
</table-wrap>
</floats-group>
</pmc>
<affiliations><list><country><li>République populaire de Chine</li>
</country>
<settlement><li>Pékin</li>
</settlement>
</list>
<tree><noCountry><name sortKey="Alghazzawi, Daniyal" sort="Alghazzawi, Daniyal" uniqKey="Alghazzawi D" first="Daniyal" last="Alghazzawi">Daniyal Alghazzawi</name>
<name sortKey="Wang, Yan Bin" sort="Wang, Yan Bin" uniqKey="Wang Y" first="Yan-Bin" last="Wang">Yan-Bin Wang</name>
</noCountry>
<country name="République populaire de Chine"><noRegion><name sortKey="Chen, Zhan Heng" sort="Chen, Zhan Heng" uniqKey="Chen Z" first="Zhan-Heng" last="Chen">Zhan-Heng Chen</name>
</noRegion>
<name sortKey="Cheng, Li" sort="Cheng, Li" uniqKey="Cheng L" first="Li" last="Cheng">Li Cheng</name>
<name sortKey="You, Zhu Hong" sort="You, Zhu Hong" uniqKey="You Z" first="Zhu-Hong" last="You">Zhu-Hong You</name>
<name sortKey="Zhang, Wen Bo" sort="Zhang, Wen Bo" uniqKey="Zhang W" first="Wen-Bo" last="Zhang">Wen-Bo Zhang</name>
</country>
</tree>
</affiliations>
</record>
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