BGFE: A Deep Learning Model for ncRNA-Protein Interaction Predictions Based on Improved Sequence Information
Identifieur interne : 000394 ( Pmc/Checkpoint ); précédent : 000393; suivant : 000395BGFE: A Deep Learning Model for ncRNA-Protein Interaction Predictions Based on Improved Sequence Information
Auteurs : Zhao-Hui Zhan ; Li-Na Jia [République populaire de Chine] ; Yong Zhou ; Li-Ping Li ; Hai-Cheng YiSource :
- International Journal of Molecular Sciences [ 1422-0067 ] ; 2019.
Abstract
The interactions between ncRNAs and proteins are critical for regulating various cellular processes in organisms, such as gene expression regulations. However, due to limitations, including financial and material consumptions in recent experimental methods for predicting ncRNA and protein interactions, it is essential to propose an innovative and practical approach with convincing performance of prediction accuracy. In this study, based on the protein sequences from a biological perspective, we put forward an effective deep learning method, named BGFE, to predict ncRNA and protein interactions. Protein sequences are represented by bi-gram probability feature extraction method from Position Specific Scoring Matrix (PSSM), and for ncRNA sequences, k-mers sparse matrices are employed to represent them. Furthermore, to extract hidden high-level feature information, a stacked auto-encoder network is employed with the stacked ensemble integration strategy. We evaluate the performance of the proposed method by using three datasets and a five-fold cross-validation after classifying the features through the random forest classifier. The experimental results clearly demonstrate the effectiveness and the prediction accuracy of our approach. In general, the proposed method is helpful for ncRNA and protein interacting predictions and it provides some serviceable guidance in future biological research.
Url:
DOI: 10.3390/ijms20040978
PubMed: 30813451
PubMed Central: 6412311
Affiliations:
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<front><div type="abstract" xml:lang="en"><p>The interactions between ncRNAs and proteins are critical for regulating various cellular processes in organisms, such as gene expression regulations. However, due to limitations, including financial and material consumptions in recent experimental methods for predicting ncRNA and protein interactions, it is essential to propose an innovative and practical approach with convincing performance of prediction accuracy. In this study, based on the protein sequences from a biological perspective, we put forward an effective deep learning method, named BGFE, to predict ncRNA and protein interactions. Protein sequences are represented by bi-gram probability feature extraction method from Position Specific Scoring Matrix (PSSM), and for ncRNA sequences, k-mers sparse matrices are employed to represent them. Furthermore, to extract hidden high-level feature information, a stacked auto-encoder network is employed with the stacked ensemble integration strategy. We evaluate the performance of the proposed method by using three datasets and a five-fold cross-validation after classifying the features through the random forest classifier. The experimental results clearly demonstrate the effectiveness and the prediction accuracy of our approach. In general, the proposed method is helpful for ncRNA and protein interacting predictions and it provides some serviceable guidance in future biological research.</p>
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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">Int J Mol Sci</journal-id>
<journal-id journal-id-type="iso-abbrev">Int J Mol Sci</journal-id>
<journal-id journal-id-type="publisher-id">ijms</journal-id>
<journal-title-group><journal-title>International Journal of Molecular Sciences</journal-title>
</journal-title-group>
<issn pub-type="epub">1422-0067</issn>
<publisher><publisher-name>MDPI</publisher-name>
</publisher>
</journal-meta>
<article-meta><article-id pub-id-type="pmid">30813451</article-id>
<article-id pub-id-type="pmc">6412311</article-id>
<article-id pub-id-type="doi">10.3390/ijms20040978</article-id>
<article-id pub-id-type="publisher-id">ijms-20-00978</article-id>
<article-categories><subj-group subj-group-type="heading"><subject>Article</subject>
</subj-group>
</article-categories>
<title-group><article-title>BGFE: A Deep Learning Model for ncRNA-Protein Interaction Predictions Based on Improved Sequence Information</article-title>
</title-group>
<contrib-group><contrib contrib-type="author"><name><surname>Zhan</surname>
<given-names>Zhao-Hui</given-names>
</name>
<xref ref-type="aff" rid="af1-ijms-20-00978">1</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Jia</surname>
<given-names>Li-Na</given-names>
</name>
<xref ref-type="aff" rid="af2-ijms-20-00978">2</xref>
<xref rid="c1-ijms-20-00978" ref-type="corresp">*</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Zhou</surname>
<given-names>Yong</given-names>
</name>
<xref ref-type="aff" rid="af1-ijms-20-00978">1</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Li</surname>
<given-names>Li-Ping</given-names>
</name>
<xref ref-type="aff" rid="af3-ijms-20-00978">3</xref>
</contrib>
<contrib contrib-type="author"><contrib-id contrib-id-type="orcid" authenticated="true">https://orcid.org/0000-0001-8339-396X</contrib-id>
<name><surname>Yi</surname>
<given-names>Hai-Cheng</given-names>
</name>
<xref ref-type="aff" rid="af3-ijms-20-00978">3</xref>
</contrib>
</contrib-group>
<aff id="af1-ijms-20-00978"><label>1</label>
China University of Mining and Technology, Xuzhou 221116, China;<email>TS16170022A3@cumt.edu.cn</email>
(Z.-H.Z.);<email>yzhou@cumt.edu.cn</email>
(Y.Z.)</aff>
<aff id="af2-ijms-20-00978"><label>2</label>
College of Information Science and Engineering, Zaozhuang University, Zaozhuang 277100, Shandong, China</aff>
<aff id="af3-ijms-20-00978"><label>3</label>
Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Urumqi 830011, China;<email>Lipingli@ms.xjb.ac.cn</email>
(L.-P.L.);<email>yihaicheng17@mails.ucas.ac.cn</email>
(H.-C.Y.)</aff>
<author-notes><corresp id="c1-ijms-20-00978"><label>*</label>
Correspondence: <email>jialina@uzz.edu.cn</email>
; Tel.: +86-139-6328-2286</corresp>
</author-notes>
<pub-date pub-type="epub"><day>23</day>
<month>2</month>
<year>2019</year>
</pub-date>
<pub-date pub-type="collection"><month>2</month>
<year>2019</year>
</pub-date>
<volume>20</volume>
<issue>4</issue>
<elocation-id>978</elocation-id>
<history><date date-type="received"><day>01</day>
<month>1</month>
<year>2019</year>
</date>
<date date-type="accepted"><day>20</day>
<month>2</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>The interactions between ncRNAs and proteins are critical for regulating various cellular processes in organisms, such as gene expression regulations. However, due to limitations, including financial and material consumptions in recent experimental methods for predicting ncRNA and protein interactions, it is essential to propose an innovative and practical approach with convincing performance of prediction accuracy. In this study, based on the protein sequences from a biological perspective, we put forward an effective deep learning method, named BGFE, to predict ncRNA and protein interactions. Protein sequences are represented by bi-gram probability feature extraction method from Position Specific Scoring Matrix (PSSM), and for ncRNA sequences, k-mers sparse matrices are employed to represent them. Furthermore, to extract hidden high-level feature information, a stacked auto-encoder network is employed with the stacked ensemble integration strategy. We evaluate the performance of the proposed method by using three datasets and a five-fold cross-validation after classifying the features through the random forest classifier. The experimental results clearly demonstrate the effectiveness and the prediction accuracy of our approach. In general, the proposed method is helpful for ncRNA and protein interacting predictions and it provides some serviceable guidance in future biological research.</p>
</abstract>
<kwd-group><kwd>ncRNA-protein interaction</kwd>
<kwd>bi-gram</kwd>
<kwd>position specific scoring matrix</kwd>
<kwd>k-mers</kwd>
<kwd>deep learning</kwd>
</kwd-group>
</article-meta>
</front>
<floats-group><fig id="ijms-20-00978-f001" orientation="portrait" position="float"><label>Figure 1</label>
<caption><p>Step-wise work flow for the proposed BGFE method. In the non-coding RNA (ncRNA) and protein sequences used for training and prediction, Singular Value Decomposition (SVD) converts ncRNA sequences into feature vectors from 4-mer sparse matrices, while protein sequences are represented by bi-gram algorithm form Position Specific Scoring Matrix (PSSM). These feature vectors are processed by multi-layer stack auto-encoder to obtain deeper feature information. Subsequently, training data and labels are fed into a random forest classifier for classification training. In addition, fine-tuning the model parameters after obtaining the machine learning model further contributes the model accuracy.</p>
</caption>
<graphic xlink:href="ijms-20-00978-g001"></graphic>
</fig>
<fig id="ijms-20-00978-f002" orientation="portrait" position="float"><label>Figure 2</label>
<caption><p>ROC curves of performance comparisons between BGFE and other strategies on dataset RPI488.</p>
</caption>
<graphic xlink:href="ijms-20-00978-g002"></graphic>
</fig>
<fig id="ijms-20-00978-f003" orientation="portrait" position="float"><label>Figure 3</label>
<caption><p>ROC curves of performance comparisons between BGFE and other strategies on dataset RPI1807.</p>
</caption>
<graphic xlink:href="ijms-20-00978-g003"></graphic>
</fig>
<fig id="ijms-20-00978-f004" orientation="portrait" position="float"><label>Figure 4</label>
<caption><p>ROC curves of performance comparisons between BGFE and other strategies on dataset RPI2241.</p>
</caption>
<graphic xlink:href="ijms-20-00978-g004"></graphic>
</fig>
<table-wrap id="ijms-20-00978-t001" orientation="portrait" position="float"><object-id pub-id-type="pii">ijms-20-00978-t001_Table 1</object-id>
<label>Table 1</label>
<caption><p>Prediction Performance on Dataset RPI488, RPI1807, and RPI2241.</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">Dataset</th>
<th align="center" valign="middle" style="border-top:solid thin;border-bottom:solid thin" rowspan="1" colspan="1">Accuracy</th>
<th align="center" valign="middle" style="border-top:solid thin;border-bottom:solid thin" rowspan="1" colspan="1">Sensitivity</th>
<th align="center" valign="middle" style="border-top:solid thin;border-bottom:solid thin" rowspan="1" colspan="1">Specificity</th>
<th align="center" valign="middle" style="border-top:solid thin;border-bottom:solid thin" rowspan="1" colspan="1">Precision</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">RPI488</td>
<td align="center" valign="middle" rowspan="1" colspan="1">0.8868</td>
<td align="center" valign="middle" rowspan="1" colspan="1">0.9268</td>
<td align="center" valign="middle" rowspan="1" colspan="1">0.8354</td>
<td align="center" valign="middle" rowspan="1" colspan="1">0.9328</td>
<td align="center" valign="middle" rowspan="1" colspan="1">0.7743</td>
</tr>
<tr><td align="center" valign="middle" rowspan="1" colspan="1">RPI1807</td>
<td align="center" valign="middle" rowspan="1" colspan="1">0.9600</td>
<td align="center" valign="middle" rowspan="1" colspan="1">0.9344</td>
<td align="center" valign="middle" rowspan="1" colspan="1">0.9989</td>
<td align="center" valign="middle" rowspan="1" colspan="1">0.9117</td>
<td align="center" valign="middle" rowspan="1" colspan="1">0.9217</td>
</tr>
<tr><td align="center" valign="middle" style="border-bottom:solid thin" rowspan="1" colspan="1">RPI2241</td>
<td align="center" valign="middle" style="border-bottom:solid thin" rowspan="1" colspan="1">0.9130</td>
<td align="center" valign="middle" style="border-bottom:solid thin" rowspan="1" colspan="1">0.8772</td>
<td align="center" valign="middle" style="border-bottom:solid thin" rowspan="1" colspan="1">0.9660</td>
<td align="center" valign="middle" style="border-bottom:solid thin" rowspan="1" colspan="1">0.8590</td>
<td align="center" valign="middle" style="border-bottom:solid thin" rowspan="1" colspan="1">0.8335</td>
</tr>
</tbody>
</table>
</table-wrap>
<table-wrap id="ijms-20-00978-t002" orientation="portrait" position="float"><object-id pub-id-type="pii">ijms-20-00978-t002_Table 2</object-id>
<label>Table 2</label>
<caption><p>Specific Performance of Four Methods on Dataset RPI488.</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">RPI488</th>
<th align="center" valign="middle" style="border-top:solid thin;border-bottom:solid thin" rowspan="1" colspan="1">Accuracy</th>
<th align="center" valign="middle" style="border-top:solid thin;border-bottom:solid thin" rowspan="1" colspan="1">Sensitivity</th>
<th align="center" valign="middle" style="border-top:solid thin;border-bottom:solid thin" rowspan="1" colspan="1">Specificity</th>
<th align="center" valign="middle" style="border-top:solid thin;border-bottom:solid thin" rowspan="1" colspan="1">Precision</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">BGFE</td>
<td align="center" valign="middle" rowspan="1" colspan="1"><bold>0.8868</bold>
</td>
<td align="center" valign="middle" rowspan="1" colspan="1"><bold>0.9268</bold>
</td>
<td align="center" valign="middle" rowspan="1" colspan="1"><bold>0.8354</bold>
</td>
<td align="center" valign="middle" rowspan="1" colspan="1">0.9328</td>
<td align="center" valign="middle" rowspan="1" colspan="1"><bold>0.7743</bold>
</td>
</tr>
<tr><td align="center" valign="middle" rowspan="1" colspan="1">Raw feature</td>
<td align="center" valign="middle" rowspan="1" colspan="1">0.8168</td>
<td align="center" valign="middle" rowspan="1" colspan="1">0.8083</td>
<td align="center" valign="middle" rowspan="1" colspan="1">0.8192</td>
<td align="center" valign="middle" rowspan="1" colspan="1">0.8104</td>
<td align="center" valign="middle" rowspan="1" colspan="1">0.6299</td>
</tr>
<tr><td align="center" valign="middle" rowspan="1" colspan="1">Stacked auto-encoder</td>
<td align="center" valign="middle" rowspan="1" colspan="1">0.8806</td>
<td align="center" valign="middle" rowspan="1" colspan="1">0.9243</td>
<td align="center" valign="middle" rowspan="1" colspan="1">0.8255</td>
<td align="center" valign="middle" rowspan="1" colspan="1"><bold>0.9351</bold>
</td>
<td align="center" valign="middle" rowspan="1" colspan="1">0.7638</td>
</tr>
<tr><td align="center" valign="middle" style="border-bottom:solid thin" rowspan="1" colspan="1">Stacked auto-encoder without fine tuning</td>
<td align="center" valign="middle" style="border-bottom:solid thin" rowspan="1" colspan="1">0.8600</td>
<td align="center" valign="middle" style="border-bottom:solid thin" rowspan="1" colspan="1">0.8848</td>
<td align="center" valign="middle" style="border-bottom:solid thin" rowspan="1" colspan="1">0.8271</td>
<td align="center" valign="middle" style="border-bottom:solid thin" rowspan="1" colspan="1">0.8850</td>
<td align="center" valign="middle" style="border-bottom:solid thin" rowspan="1" colspan="1">0.7187</td>
</tr>
</tbody>
</table>
<table-wrap-foot><fn><p>The boldface indicates this measure performance is the best among the compared methods for individual dataset.</p>
</fn>
</table-wrap-foot>
</table-wrap>
<table-wrap id="ijms-20-00978-t003" orientation="portrait" position="float"><object-id pub-id-type="pii">ijms-20-00978-t003_Table 3</object-id>
<label>Table 3</label>
<caption><p>Specific Performance of Four Methods on Dataset RPI1807.</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">RPI1807</th>
<th align="center" valign="middle" style="border-top:solid thin;border-bottom:solid thin" rowspan="1" colspan="1">Accuracy</th>
<th align="center" valign="middle" style="border-top:solid thin;border-bottom:solid thin" rowspan="1" colspan="1">Sensitivity</th>
<th align="center" valign="middle" style="border-top:solid thin;border-bottom:solid thin" rowspan="1" colspan="1">Specificity</th>
<th align="center" valign="middle" style="border-top:solid thin;border-bottom:solid thin" rowspan="1" colspan="1">Precision</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">BGFE</td>
<td align="center" valign="middle" rowspan="1" colspan="1">0.9600</td>
<td align="center" valign="middle" rowspan="1" colspan="1">0.9344</td>
<td align="center" valign="middle" rowspan="1" colspan="1">0.9989</td>
<td align="center" valign="middle" rowspan="1" colspan="1">0.9117</td>
<td align="center" valign="middle" rowspan="1" colspan="1">0.9217</td>
</tr>
<tr><td align="center" valign="middle" rowspan="1" colspan="1">Raw feature</td>
<td align="center" valign="middle" rowspan="1" colspan="1">0.9349</td>
<td align="center" valign="middle" rowspan="1" colspan="1">0.9508</td>
<td align="center" valign="middle" rowspan="1" colspan="1">0.9308</td>
<td align="center" valign="middle" rowspan="1" colspan="1">0.9400</td>
<td align="center" valign="middle" rowspan="1" colspan="1">0.8688</td>
</tr>
<tr><td align="center" valign="middle" rowspan="1" colspan="1">Stacked auto-encoder</td>
<td align="center" valign="middle" rowspan="1" colspan="1">0.9396</td>
<td align="center" valign="middle" rowspan="1" colspan="1">0.9029</td>
<td align="center" valign="middle" rowspan="1" colspan="1"><bold>0.9994</bold>
</td>
<td align="center" valign="middle" rowspan="1" colspan="1">0.8651</td>
<td align="center" valign="middle" rowspan="1" colspan="1">0.8830</td>
</tr>
<tr><td align="center" valign="middle" style="border-bottom:solid thin" rowspan="1" colspan="1">Stacked auto-encoder without fine tuning</td>
<td align="center" valign="middle" style="border-bottom:solid thin" rowspan="1" colspan="1"><bold>0.9645</bold>
</td>
<td align="center" valign="middle" style="border-bottom:solid thin" rowspan="1" colspan="1"><bold>0.9672</bold>
</td>
<td align="center" valign="middle" style="border-bottom:solid thin" rowspan="1" colspan="1">0.9688</td>
<td align="center" valign="middle" style="border-bottom:solid thin" rowspan="1" colspan="1"><bold>0.9590</bold>
</td>
<td align="center" valign="middle" style="border-bottom:solid thin" rowspan="1" colspan="1"><bold>0.9281</bold>
</td>
</tr>
</tbody>
</table>
<table-wrap-foot><fn><p>The boldface indicates this measure performance is the best among the compared methods for individual dataset.</p>
</fn>
</table-wrap-foot>
</table-wrap>
<table-wrap id="ijms-20-00978-t004" orientation="portrait" position="float"><object-id pub-id-type="pii">ijms-20-00978-t004_Table 4</object-id>
<label>Table 4</label>
<caption><p>Specific Performance of Four Methods on Dataset RPI2241.</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">RPI2241</th>
<th align="center" valign="middle" style="border-top:solid thin;border-bottom:solid thin" rowspan="1" colspan="1">Accuracy</th>
<th align="center" valign="middle" style="border-top:solid thin;border-bottom:solid thin" rowspan="1" colspan="1">Sensitivity</th>
<th align="center" valign="middle" style="border-top:solid thin;border-bottom:solid thin" rowspan="1" colspan="1">Specificity</th>
<th align="center" valign="middle" style="border-top:solid thin;border-bottom:solid thin" rowspan="1" colspan="1">Precision</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">BGFE</td>
<td align="center" valign="middle" rowspan="1" colspan="1"><bold>0.9130</bold>
</td>
<td align="center" valign="middle" rowspan="1" colspan="1">0.8772</td>
<td align="center" valign="middle" rowspan="1" colspan="1"><bold>0.9660</bold>
</td>
<td align="center" valign="middle" rowspan="1" colspan="1">0.8590</td>
<td align="center" valign="middle" rowspan="1" colspan="1"><bold>0.8335</bold>
</td>
</tr>
<tr><td align="center" valign="middle" rowspan="1" colspan="1">Raw feature</td>
<td align="center" valign="middle" rowspan="1" colspan="1">0.6438</td>
<td align="center" valign="middle" rowspan="1" colspan="1">0.6525</td>
<td align="center" valign="middle" rowspan="1" colspan="1">0.6313</td>
<td align="center" valign="middle" rowspan="1" colspan="1">0.6565</td>
<td align="center" valign="middle" rowspan="1" colspan="1">0.2881</td>
</tr>
<tr><td align="center" valign="middle" rowspan="1" colspan="1">Stacked auto-encoder</td>
<td align="center" valign="middle" rowspan="1" colspan="1">0.9041</td>
<td align="center" valign="middle" rowspan="1" colspan="1"><bold>0.8895</bold>
</td>
<td align="center" valign="middle" rowspan="1" colspan="1">0.9329</td>
<td align="center" valign="middle" rowspan="1" colspan="1"><bold>0.8747</bold>
</td>
<td align="center" valign="middle" rowspan="1" colspan="1">0.8156</td>
</tr>
<tr><td align="center" valign="middle" style="border-bottom:solid thin" rowspan="1" colspan="1">Stacked auto-encoder without fine tuning</td>
<td align="center" valign="middle" style="border-bottom:solid thin" rowspan="1" colspan="1">0.6438</td>
<td align="center" valign="middle" style="border-bottom:solid thin" rowspan="1" colspan="1">0.6517</td>
<td align="center" valign="middle" style="border-bottom:solid thin" rowspan="1" colspan="1">0.6327</td>
<td align="center" valign="middle" style="border-bottom:solid thin" rowspan="1" colspan="1">0.6551</td>
<td align="center" valign="middle" style="border-bottom:solid thin" rowspan="1" colspan="1">0.2879</td>
</tr>
</tbody>
</table>
<table-wrap-foot><fn><p>The boldface indicates this measure performance is the best among the compared methods for individual dataset.</p>
</fn>
</table-wrap-foot>
</table-wrap>
<table-wrap id="ijms-20-00978-t005" orientation="portrait" position="float"><object-id pub-id-type="pii">ijms-20-00978-t005_Table 5</object-id>
<label>Table 5</label>
<caption><p>The Performance Comparison between BGFE and Other Methods on Dataset RPI1807 and RPI2241.</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">RPI1807</th>
<th align="center" valign="middle" style="border-top:solid thin;border-bottom:solid thin" rowspan="1" colspan="1">Accuracy</th>
<th align="center" valign="middle" style="border-top:solid thin;border-bottom:solid thin" rowspan="1" colspan="1">Sensitivity</th>
<th align="center" valign="middle" style="border-top:solid thin;border-bottom:solid thin" rowspan="1" colspan="1">Precision</th>
</tr>
</thead>
<tbody><tr><td align="center" valign="middle" rowspan="1" colspan="1">BGFE</td>
<td align="center" valign="middle" rowspan="1" colspan="1">0.9600</td>
<td align="center" valign="middle" rowspan="1" colspan="1">0.9344</td>
<td align="center" valign="middle" rowspan="1" colspan="1">0.9117</td>
</tr>
<tr><td align="center" valign="middle" style="border-bottom:solid thin" rowspan="1" colspan="1">RPI-Pred</td>
<td align="center" valign="middle" style="border-bottom:solid thin" rowspan="1" colspan="1">0.9300</td>
<td align="center" valign="middle" style="border-bottom:solid thin" rowspan="1" colspan="1">0.9400</td>
<td align="center" valign="middle" style="border-bottom:solid thin" rowspan="1" colspan="1">0.9400</td>
</tr>
<tr><td align="center" valign="middle" style="border-bottom:solid thin" rowspan="1" colspan="1"><bold>RPI2241</bold>
</td>
<td align="center" valign="middle" style="border-bottom:solid thin" rowspan="1" colspan="1"><bold>Accuracy</bold>
</td>
<td align="center" valign="middle" style="border-bottom:solid thin" rowspan="1" colspan="1"><bold>Sensitivity</bold>
</td>
<td align="center" valign="middle" style="border-bottom:solid thin" rowspan="1" colspan="1"><bold>Precision</bold>
</td>
</tr>
<tr><td align="center" valign="middle" rowspan="1" colspan="1">BGFE</td>
<td align="center" valign="middle" rowspan="1" colspan="1">0.9130</td>
<td align="center" valign="middle" rowspan="1" colspan="1">0.8772</td>
<td align="center" valign="middle" rowspan="1" colspan="1">0.8590</td>
</tr>
<tr><td align="center" valign="middle" rowspan="1" colspan="1">RPI-Pred</td>
<td align="center" valign="middle" rowspan="1" colspan="1">0.8400</td>
<td align="center" valign="middle" rowspan="1" colspan="1">0.7800</td>
<td align="center" valign="middle" rowspan="1" colspan="1">0.8800</td>
</tr>
<tr><td align="center" valign="middle" rowspan="1" colspan="1">Usha K Muppirala</td>
<td align="center" valign="middle" rowspan="1" colspan="1">0.8960</td>
<td align="center" valign="middle" rowspan="1" colspan="1">0.9000</td>
<td align="center" valign="middle" rowspan="1" colspan="1">0.8900</td>
</tr>
<tr><td align="center" valign="middle" style="border-bottom:solid thin" rowspan="1" colspan="1">Ying Wang</td>
<td align="center" valign="middle" style="border-bottom:solid thin" rowspan="1" colspan="1">0.7400</td>
<td align="center" valign="middle" style="border-bottom:solid thin" rowspan="1" colspan="1">0.9160</td>
<td align="center" valign="middle" style="border-bottom:solid thin" rowspan="1" colspan="1">0.6990</td>
</tr>
</tbody>
</table>
</table-wrap>
<table-wrap id="ijms-20-00978-t006" orientation="portrait" position="float"><object-id pub-id-type="pii">ijms-20-00978-t006_Table 6</object-id>
<label>Table 6</label>
<caption><p>Details of the ncRNA-Protein Interaction Datasets.</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">Dataset</th>
<th align="center" valign="middle" style="border-top:solid thin;border-bottom:solid thin" rowspan="1" colspan="1">Interaction Pairs</th>
<th align="center" valign="middle" style="border-top:solid thin;border-bottom:solid thin" rowspan="1" colspan="1">Number of Proteins</th>
<th align="center" valign="middle" style="border-top:solid thin;border-bottom:solid thin" rowspan="1" colspan="1">Number of RNAs</th>
</tr>
</thead>
<tbody><tr><td align="center" valign="middle" rowspan="1" colspan="1">RPI488 <sup>1</sup>
</td>
<td align="center" valign="middle" rowspan="1" colspan="1">243</td>
<td align="center" valign="middle" rowspan="1" colspan="1">25</td>
<td align="center" valign="middle" rowspan="1" colspan="1">247</td>
</tr>
<tr><td align="center" valign="middle" rowspan="1" colspan="1">RPI1807 <sup>1</sup>
</td>
<td align="center" valign="middle" rowspan="1" colspan="1">1807</td>
<td align="center" valign="middle" rowspan="1" colspan="1">1807</td>
<td align="center" valign="middle" rowspan="1" colspan="1">1078</td>
</tr>
<tr><td align="center" valign="middle" style="border-bottom:solid thin" rowspan="1" colspan="1">RPI2241 <sup>1</sup>
</td>
<td align="center" valign="middle" style="border-bottom:solid thin" rowspan="1" colspan="1">2241</td>
<td align="center" valign="middle" style="border-bottom:solid thin" rowspan="1" colspan="1">2043</td>
<td align="center" valign="middle" style="border-bottom:solid thin" rowspan="1" colspan="1">332</td>
</tr>
</tbody>
</table>
<table-wrap-foot><fn><p><sup>1</sup>
RPI488 is lncRNA-protein interactions based on structure complexes, RPI2241 and RPI1807 are RNA-protein interactions.</p>
</fn>
</table-wrap-foot>
</table-wrap>
</floats-group>
</pmc>
<affiliations><list><country><li>République populaire de Chine</li>
</country>
</list>
<tree><noCountry><name sortKey="Li, Li Ping" sort="Li, Li Ping" uniqKey="Li L" first="Li-Ping" last="Li">Li-Ping Li</name>
<name sortKey="Yi, Hai Cheng" sort="Yi, Hai Cheng" uniqKey="Yi H" first="Hai-Cheng" last="Yi">Hai-Cheng Yi</name>
<name sortKey="Zhan, Zhao Hui" sort="Zhan, Zhao Hui" uniqKey="Zhan Z" first="Zhao-Hui" last="Zhan">Zhao-Hui Zhan</name>
<name sortKey="Zhou, Yong" sort="Zhou, Yong" uniqKey="Zhou Y" first="Yong" last="Zhou">Yong Zhou</name>
</noCountry>
<country name="République populaire de Chine"><noRegion><name sortKey="Jia, Li Na" sort="Jia, Li Na" uniqKey="Jia L" first="Li-Na" last="Jia">Li-Na Jia</name>
</noRegion>
</country>
</tree>
</affiliations>
</record>
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