Hybrid Deep Learning Based on a Heterogeneous Network Profile for Functional Annotations of <i>Plasmodium falciparum</i> Genes
Functional annotation of unknown function genes reveals unidentified functions that can enhance our understanding of complex genome communications. A common approach for inferring gene function involves the ortholog-based method. However, genetic data alone are often not enough to provide informatio...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-09-01
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| Series: | International Journal of Molecular Sciences |
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| Online Access: | https://www.mdpi.com/1422-0067/22/18/10019 |
| _version_ | 1797518908416065536 |
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| author | Apichat Suratanee Kitiporn Plaimas |
| author_facet | Apichat Suratanee Kitiporn Plaimas |
| author_sort | Apichat Suratanee |
| collection | DOAJ |
| description | Functional annotation of unknown function genes reveals unidentified functions that can enhance our understanding of complex genome communications. A common approach for inferring gene function involves the ortholog-based method. However, genetic data alone are often not enough to provide information for function annotation. Thus, integrating other sources of data can potentially increase the possibility of retrieving annotations. Network-based methods are efficient techniques for exploring interactions among genes and can be used for functional inference. In this study, we present an analysis framework for inferring the functions of <i>Plasmodium falciparum</i> genes based on connection profiles in a heterogeneous network between human and <i>Plasmodium falciparum</i> proteins. These profiles were fed into a hybrid deep learning algorithm to predict the orthologs of unknown function genes. The results show high performance of the model’s predictions, with an AUC of 0.89. One hundred and twenty-one predicted pairs with high prediction scores were selected for inferring the functions using statistical enrichment analysis. Using this method, <i>PF3D7_1248700</i> and <i>PF3D7_0401800</i> were found to be involved with muscle contraction and striated muscle tissue development, while <i>PF3D7_1303800</i> and <i>PF3D7_1201000</i> were found to be related to protein dephosphorylation. In conclusion, combining a heterogeneous network and a hybrid deep learning technique can allow us to identify unknown gene functions of malaria parasites. This approach is generalized and can be applied to other diseases that enhance the field of biomedical science. |
| first_indexed | 2024-03-10T07:35:57Z |
| format | Article |
| id | doaj.art-a4c8b7db85a5420999502370a2443ed6 |
| institution | Directory Open Access Journal |
| issn | 1661-6596 1422-0067 |
| language | English |
| last_indexed | 2024-03-10T07:35:57Z |
| publishDate | 2021-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | International Journal of Molecular Sciences |
| spelling | doaj.art-a4c8b7db85a5420999502370a2443ed62023-11-22T13:31:15ZengMDPI AGInternational Journal of Molecular Sciences1661-65961422-00672021-09-0122181001910.3390/ijms221810019Hybrid Deep Learning Based on a Heterogeneous Network Profile for Functional Annotations of <i>Plasmodium falciparum</i> GenesApichat Suratanee0Kitiporn Plaimas1Department of Mathematics, Faculty of Applied Science, King Mongkut’s University of Technology North Bangkok, Bangkok 10800, ThailandAdvanced Virtual and Intelligent Computing (AVIC) Center, Department of Mathematics and Computer Science, Faculty of Science, Chulalongkorn University, Bangkok 10330, ThailandFunctional annotation of unknown function genes reveals unidentified functions that can enhance our understanding of complex genome communications. A common approach for inferring gene function involves the ortholog-based method. However, genetic data alone are often not enough to provide information for function annotation. Thus, integrating other sources of data can potentially increase the possibility of retrieving annotations. Network-based methods are efficient techniques for exploring interactions among genes and can be used for functional inference. In this study, we present an analysis framework for inferring the functions of <i>Plasmodium falciparum</i> genes based on connection profiles in a heterogeneous network between human and <i>Plasmodium falciparum</i> proteins. These profiles were fed into a hybrid deep learning algorithm to predict the orthologs of unknown function genes. The results show high performance of the model’s predictions, with an AUC of 0.89. One hundred and twenty-one predicted pairs with high prediction scores were selected for inferring the functions using statistical enrichment analysis. Using this method, <i>PF3D7_1248700</i> and <i>PF3D7_0401800</i> were found to be involved with muscle contraction and striated muscle tissue development, while <i>PF3D7_1303800</i> and <i>PF3D7_1201000</i> were found to be related to protein dephosphorylation. In conclusion, combining a heterogeneous network and a hybrid deep learning technique can allow us to identify unknown gene functions of malaria parasites. This approach is generalized and can be applied to other diseases that enhance the field of biomedical science.https://www.mdpi.com/1422-0067/22/18/10019heterogeneous networkhybrid deep learningfunctional annotationsprotein network profiles |
| spellingShingle | Apichat Suratanee Kitiporn Plaimas Hybrid Deep Learning Based on a Heterogeneous Network Profile for Functional Annotations of <i>Plasmodium falciparum</i> Genes International Journal of Molecular Sciences heterogeneous network hybrid deep learning functional annotations protein network profiles |
| title | Hybrid Deep Learning Based on a Heterogeneous Network Profile for Functional Annotations of <i>Plasmodium falciparum</i> Genes |
| title_full | Hybrid Deep Learning Based on a Heterogeneous Network Profile for Functional Annotations of <i>Plasmodium falciparum</i> Genes |
| title_fullStr | Hybrid Deep Learning Based on a Heterogeneous Network Profile for Functional Annotations of <i>Plasmodium falciparum</i> Genes |
| title_full_unstemmed | Hybrid Deep Learning Based on a Heterogeneous Network Profile for Functional Annotations of <i>Plasmodium falciparum</i> Genes |
| title_short | Hybrid Deep Learning Based on a Heterogeneous Network Profile for Functional Annotations of <i>Plasmodium falciparum</i> Genes |
| title_sort | hybrid deep learning based on a heterogeneous network profile for functional annotations of i plasmodium falciparum i genes |
| topic | heterogeneous network hybrid deep learning functional annotations protein network profiles |
| url | https://www.mdpi.com/1422-0067/22/18/10019 |
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