To Assist Oncologists: An Efficient Machine Learning-Based Approach for Anti-Cancer Peptides Classification
In the modern technological era, Anti-cancer peptides (ACPs) have been considered a promising cancer treatment. It’s critical to find new ACPs to ensure a better knowledge of their functioning processes and vaccine development. Thus, timely and efficient ACPs using a computational technique are high...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-05-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/22/11/4005 |
| _version_ | 1797491793617485824 |
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| author | Majed Alsanea Abdulsalam S. Dukyil Afnan Bushra Riaz Farhan Alebeisat Muhammad Islam Shabana Habib |
| author_facet | Majed Alsanea Abdulsalam S. Dukyil Afnan Bushra Riaz Farhan Alebeisat Muhammad Islam Shabana Habib |
| author_sort | Majed Alsanea |
| collection | DOAJ |
| description | In the modern technological era, Anti-cancer peptides (ACPs) have been considered a promising cancer treatment. It’s critical to find new ACPs to ensure a better knowledge of their functioning processes and vaccine development. Thus, timely and efficient ACPs using a computational technique are highly needed because of the enormous peptide sequences generated in the post-genomic era. Recently, numerous adaptive statistical algorithms have been developed for separating ACPs and NACPs. Despite great advancements, existing approaches still have insufficient feature descriptors and learning methods, limiting predictive performance. To address this, a trustworthy framework is developed for the precise identification of ACPs. Particularly, the presented approach incorporates four hypothetical feature encoding mechanisms namely: amino acid, dipeptide, tripeptide, and an improved version of pseudo amino acid composition are applied to indicate the motif of the target class. Moreover, principal component analysis (PCA) is employed for feature pruning, while selecting optimal, deep, and highly variated features. Due to the diverse nature of learning, experiments are performed over numerous algorithms to select the optimum operating method. After investigating the empirical outcomes, the support vector machine with hybrid feature space shows better performance. The proposed framework achieved an accuracy of 97.09% and 98.25% over the benchmark and independent datasets, respectively. The comparative analysis demonstrates that our proposed model outperforms as compared to the existing methods and is beneficial in drug development, and oncology. |
| first_indexed | 2024-03-10T00:54:22Z |
| format | Article |
| id | doaj.art-f49f7ff7610b4dab927933265fd6db06 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T00:54:22Z |
| publishDate | 2022-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-f49f7ff7610b4dab927933265fd6db062023-11-23T14:47:07ZengMDPI AGSensors1424-82202022-05-012211400510.3390/s22114005To Assist Oncologists: An Efficient Machine Learning-Based Approach for Anti-Cancer Peptides ClassificationMajed Alsanea0Abdulsalam S. Dukyil1Afnan2Bushra Riaz3Farhan Alebeisat4Muhammad Islam5Shabana Habib6Computing Department, Arabeast College, Riyadh 13544, Saudi ArabiaSTC Academy, Riyadh 13315, Saudi ArabiaDigital Image Processing Laboratory, Department of Computer Science, Islamia College University Peshawar, Peshawar 25000, PakistanDepartment of Biomedical Science, Ajou University School of Medicine, Suwon 16499, KoreaInformation Technology Department, ICT College, Tafila Technical University, Tafila 66110, JordanDepartment of Electrical Engineering, College of Engineering and Information Technology, Onaizah Colleges, Unaizah 56219, Saudi ArabiaDepartment of Information Technology, College of Computer, Qassim University, Buraydah 52571, Saudi ArabiaIn the modern technological era, Anti-cancer peptides (ACPs) have been considered a promising cancer treatment. It’s critical to find new ACPs to ensure a better knowledge of their functioning processes and vaccine development. Thus, timely and efficient ACPs using a computational technique are highly needed because of the enormous peptide sequences generated in the post-genomic era. Recently, numerous adaptive statistical algorithms have been developed for separating ACPs and NACPs. Despite great advancements, existing approaches still have insufficient feature descriptors and learning methods, limiting predictive performance. To address this, a trustworthy framework is developed for the precise identification of ACPs. Particularly, the presented approach incorporates four hypothetical feature encoding mechanisms namely: amino acid, dipeptide, tripeptide, and an improved version of pseudo amino acid composition are applied to indicate the motif of the target class. Moreover, principal component analysis (PCA) is employed for feature pruning, while selecting optimal, deep, and highly variated features. Due to the diverse nature of learning, experiments are performed over numerous algorithms to select the optimum operating method. After investigating the empirical outcomes, the support vector machine with hybrid feature space shows better performance. The proposed framework achieved an accuracy of 97.09% and 98.25% over the benchmark and independent datasets, respectively. The comparative analysis demonstrates that our proposed model outperforms as compared to the existing methods and is beneficial in drug development, and oncology.https://www.mdpi.com/1424-8220/22/11/4005anticancer peptidesartificial intelligencebiomedicinestatistical approachmachine learning |
| spellingShingle | Majed Alsanea Abdulsalam S. Dukyil Afnan Bushra Riaz Farhan Alebeisat Muhammad Islam Shabana Habib To Assist Oncologists: An Efficient Machine Learning-Based Approach for Anti-Cancer Peptides Classification Sensors anticancer peptides artificial intelligence biomedicine statistical approach machine learning |
| title | To Assist Oncologists: An Efficient Machine Learning-Based Approach for Anti-Cancer Peptides Classification |
| title_full | To Assist Oncologists: An Efficient Machine Learning-Based Approach for Anti-Cancer Peptides Classification |
| title_fullStr | To Assist Oncologists: An Efficient Machine Learning-Based Approach for Anti-Cancer Peptides Classification |
| title_full_unstemmed | To Assist Oncologists: An Efficient Machine Learning-Based Approach for Anti-Cancer Peptides Classification |
| title_short | To Assist Oncologists: An Efficient Machine Learning-Based Approach for Anti-Cancer Peptides Classification |
| title_sort | to assist oncologists an efficient machine learning based approach for anti cancer peptides classification |
| topic | anticancer peptides artificial intelligence biomedicine statistical approach machine learning |
| url | https://www.mdpi.com/1424-8220/22/11/4005 |
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