Protein secondary structure prediction (PSSP) using different machine algorithms
Abstract Background The computational biology approach has advanced exponentially in protein secondary structure prediction (PSSP), which is vital for the pharmaceutical industry. Extracting protein structure from the laboratory has insufficient information for PSSP that is used in bioinformatics st...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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SpringerOpen
2021-06-01
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| Series: | Egyptian Journal of Medical Human Genetics |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s43042-021-00173-w |
| _version_ | 1818644403137282048 |
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| author | Heba M. Afify Mohamed B. Abdelhalim Mai S. Mabrouk Ahmed Y. Sayed |
| author_facet | Heba M. Afify Mohamed B. Abdelhalim Mai S. Mabrouk Ahmed Y. Sayed |
| author_sort | Heba M. Afify |
| collection | DOAJ |
| description | Abstract Background The computational biology approach has advanced exponentially in protein secondary structure prediction (PSSP), which is vital for the pharmaceutical industry. Extracting protein structure from the laboratory has insufficient information for PSSP that is used in bioinformatics studies. In this paper, the support vector machine (SVM) model and decision tree are presented on the RS126 dataset to address the problem of PSSP. A decision tree is applied for the SVM outcome to obtain the relevant guidelines possible for PSSP. Furthermore, the number of produced rules was fairly small, and they show a greater degree of comprehensibility compared to other rules. Several of the proposed principles have compelling and relevant biological clarification. Results The results confirmed that the existence of a particular amino acid in a protein sequence increases the stability for the forecast of protein secondary structure. The suggested algorithm achieved 85% accuracy for the E|~E classifier. Conclusions The proposed rules can be very important in managing wet laboratory experiments intended at determining protein secondary structure. Lastly, future work will focus mainly on large protein datasets without overfitting and expand the amount of extracted regulations for PSSP. |
| first_indexed | 2024-12-17T00:14:17Z |
| format | Article |
| id | doaj.art-1d547c55c1d74cf986eef8af0fcf863d |
| institution | Directory Open Access Journal |
| issn | 2090-2441 |
| language | English |
| last_indexed | 2024-12-17T00:14:17Z |
| publishDate | 2021-06-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Egyptian Journal of Medical Human Genetics |
| spelling | doaj.art-1d547c55c1d74cf986eef8af0fcf863d2022-12-21T22:10:44ZengSpringerOpenEgyptian Journal of Medical Human Genetics2090-24412021-06-0122111010.1186/s43042-021-00173-wProtein secondary structure prediction (PSSP) using different machine algorithmsHeba M. Afify0Mohamed B. Abdelhalim1Mai S. Mabrouk2Ahmed Y. Sayed3Systems and Biomedical Engineering Department, Higher Institute of EngineeringCollege of Computing and Information Technology (CCIT), Arab Academy for Science Technology and Maritime Transport (AASTMT)Misr University for Science and Technology (MUST)Department of Engineering Mathematics and Physics, Faculty of Engineering El-Mataria, Halwan UniversityAbstract Background The computational biology approach has advanced exponentially in protein secondary structure prediction (PSSP), which is vital for the pharmaceutical industry. Extracting protein structure from the laboratory has insufficient information for PSSP that is used in bioinformatics studies. In this paper, the support vector machine (SVM) model and decision tree are presented on the RS126 dataset to address the problem of PSSP. A decision tree is applied for the SVM outcome to obtain the relevant guidelines possible for PSSP. Furthermore, the number of produced rules was fairly small, and they show a greater degree of comprehensibility compared to other rules. Several of the proposed principles have compelling and relevant biological clarification. Results The results confirmed that the existence of a particular amino acid in a protein sequence increases the stability for the forecast of protein secondary structure. The suggested algorithm achieved 85% accuracy for the E|~E classifier. Conclusions The proposed rules can be very important in managing wet laboratory experiments intended at determining protein secondary structure. Lastly, future work will focus mainly on large protein datasets without overfitting and expand the amount of extracted regulations for PSSP.https://doi.org/10.1186/s43042-021-00173-wSupport vector machineProtein structure predictionDecision tree |
| spellingShingle | Heba M. Afify Mohamed B. Abdelhalim Mai S. Mabrouk Ahmed Y. Sayed Protein secondary structure prediction (PSSP) using different machine algorithms Egyptian Journal of Medical Human Genetics Support vector machine Protein structure prediction Decision tree |
| title | Protein secondary structure prediction (PSSP) using different machine algorithms |
| title_full | Protein secondary structure prediction (PSSP) using different machine algorithms |
| title_fullStr | Protein secondary structure prediction (PSSP) using different machine algorithms |
| title_full_unstemmed | Protein secondary structure prediction (PSSP) using different machine algorithms |
| title_short | Protein secondary structure prediction (PSSP) using different machine algorithms |
| title_sort | protein secondary structure prediction pssp using different machine algorithms |
| topic | Support vector machine Protein structure prediction Decision tree |
| url | https://doi.org/10.1186/s43042-021-00173-w |
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