Deep-4mCGP: A Deep Learning Approach to Predict 4mC Sites in Geobacter pickeringii by Using Correlation-Based Feature Selection Technique
4mC is a type of DNA alteration that has the ability to synchronize multiple biological movements, for example, DNA replication, gene expressions, and transcriptional regulations. Accurate prediction of 4mC sites can provide exact information to their hereditary functions. The purpose of this study...
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| Veröffentlicht in: | International journal of molecular sciences 2022-01, Vol.23 (3), p.1251 |
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| creator | Zulfiqar, Hasan Huang, Qin-Lai Lv, Hao Sun, Zi-Jie Dao, Fu-Ying Lin, Hao |
| description | 4mC is a type of DNA alteration that has the ability to synchronize multiple biological movements, for example, DNA replication, gene expressions, and transcriptional regulations. Accurate prediction of 4mC sites can provide exact information to their hereditary functions. The purpose of this study was to establish a robust deep learning model to recognize 4mC sites in Geobacter pickeringii. In the anticipated model, two kinds of feature descriptors, namely, binary and
-mer composition were used to encode the DNA sequences of
. The obtained features from their fusion were optimized by using correlation and gradient-boosting decision tree (GBDT)-based algorithm with incremental feature selection (IFS) method. Then, these optimized features were inserted into 1D convolutional neural network (CNN) to classify 4mC sites from non-4mC sites in
. The performance of the anticipated model on independent data exhibited an accuracy of 0.868, which was 4.2% higher than the existing model. |
| doi_str_mv | 10.3390/ijms23031251 |
| format | Article |
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-mer composition were used to encode the DNA sequences of
. The obtained features from their fusion were optimized by using correlation and gradient-boosting decision tree (GBDT)-based algorithm with incremental feature selection (IFS) method. Then, these optimized features were inserted into 1D convolutional neural network (CNN) to classify 4mC sites from non-4mC sites in
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-mer composition were used to encode the DNA sequences of
. The obtained features from their fusion were optimized by using correlation and gradient-boosting decision tree (GBDT)-based algorithm with incremental feature selection (IFS) method. Then, these optimized features were inserted into 1D convolutional neural network (CNN) to classify 4mC sites from non-4mC sites in
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The performance of the anticipated model on independent data exhibited an accuracy of 0.868, which was 4.2% higher than the existing model.</description><subject>Accuracy</subject><subject>Algorithms</subject><subject>Bioinformatics</subject><subject>Communication</subject><subject>Computational Biology - methods</subject><subject>Cytosine - metabolism</subject><subject>Deep Learning</subject><subject>Deoxyribonucleic acid</subject><subject>DNA</subject><subject>DNA - genetics</subject><subject>DNA biosynthesis</subject><subject>DNA Methylation - genetics</subject><subject>Epigenesis, Genetic - genetics</subject><subject>Feature selection</subject><subject>Gene expression</subject><subject>Genomes</subject><subject>Geobacter</subject><subject>Geobacter - genetics</subject><subject>Machine Learning</subject><subject>Mutation - genetics</subject><subject>Neural networks</subject><subject>Neural Networks, Computer</subject><subject>Nucleotide sequence</subject><subject>Software</subject><subject>Transcription</subject><issn>1422-0067</issn><issn>1661-6596</issn><issn>1422-0067</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><sourceid>8G5</sourceid><sourceid>BENPR</sourceid><sourceid>GUQSH</sourceid><sourceid>M2O</sourceid><recordid>eNpdkUFv1DAQhSMEoqVw44wsceFAYGzH2YQD0rLQBWklKrU9W44z7npJ7GA7SP0J_GsctVQLp_F4vnl6o1cULym847yF9_YwRsaBUyboo-KUVoyVAPXq8dH7pHgW4wGAcSbap8UJF7TmdFWdFr8_I05lNW62Fx_Imiwd2aEKzrobsp6m4JXek-TJRcDe6kQySi5twkisI1v0ndIJA5ms_oEhL1lLultyHZf9jQ8BB5Wsd-UnFbEn56jSHJBc4oB6-SdXqPfO_pzxefHEqCHii_t6Vlyff7nafC1337ffNutdqSvKUlmxWkArOgZGM20oVNDougUQaHoOouF5JLrOQEcBwZhW9SjAdB2tALngZ8XHO91p7kbsNboU1CCnYEcVbqVXVv47cXYvb_wv2TS8Bl5ngTf3AsFn3zHJ0UaNw6Ac-jlKVrM2-1hBldHX_6EHPweXz1uolWhpXTeZentH6eBjDGgezFCQS8byOOOMvzo-4AH-Gyr_A4J-oq4</recordid><startdate>20220123</startdate><enddate>20220123</enddate><creator>Zulfiqar, Hasan</creator><creator>Huang, Qin-Lai</creator><creator>Lv, Hao</creator><creator>Sun, Zi-Jie</creator><creator>Dao, Fu-Ying</creator><creator>Lin, Hao</creator><general>MDPI AG</general><general>MDPI</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>8G5</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>CCPQU</scope><scope>COVID</scope><scope>DWQXO</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>GUQSH</scope><scope>K9.</scope><scope>M0S</scope><scope>M1P</scope><scope>M2O</scope><scope>MBDVC</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>Q9U</scope><scope>7X8</scope><scope>5PM</scope><orcidid>https://orcid.org/0000-0002-8526-371X</orcidid><orcidid>https://orcid.org/0000-0001-6265-2862</orcidid></search><sort><creationdate>20220123</creationdate><title>Deep-4mCGP: A Deep Learning Approach to Predict 4mC Sites in Geobacter pickeringii by Using Correlation-Based Feature Selection Technique</title><author>Zulfiqar, Hasan ; 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Accurate prediction of 4mC sites can provide exact information to their hereditary functions. The purpose of this study was to establish a robust deep learning model to recognize 4mC sites in Geobacter pickeringii. In the anticipated model, two kinds of feature descriptors, namely, binary and
-mer composition were used to encode the DNA sequences of
. The obtained features from their fusion were optimized by using correlation and gradient-boosting decision tree (GBDT)-based algorithm with incremental feature selection (IFS) method. Then, these optimized features were inserted into 1D convolutional neural network (CNN) to classify 4mC sites from non-4mC sites in
. The performance of the anticipated model on independent data exhibited an accuracy of 0.868, which was 4.2% higher than the existing model.</abstract><cop>Switzerland</cop><pub>MDPI AG</pub><pmid>35163174</pmid><doi>10.3390/ijms23031251</doi><orcidid>https://orcid.org/0000-0002-8526-371X</orcidid><orcidid>https://orcid.org/0000-0001-6265-2862</orcidid><oa>free_for_read</oa></addata></record> |
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| subjects | Accuracy Algorithms Bioinformatics Communication Computational Biology - methods Cytosine - metabolism Deep Learning Deoxyribonucleic acid DNA DNA - genetics DNA biosynthesis DNA Methylation - genetics Epigenesis, Genetic - genetics Feature selection Gene expression Genomes Geobacter Geobacter - genetics Machine Learning Mutation - genetics Neural networks Neural Networks, Computer Nucleotide sequence Software Transcription |
| title | Deep-4mCGP: A Deep Learning Approach to Predict 4mC Sites in Geobacter pickeringii by Using Correlation-Based Feature Selection Technique |
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