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All Journal Jurnal Teknologi dan Sistem Komputer Jurnal Ilmiah FIFO Jurnal Teknik Informatika UNIKA Santo Thomas JUTEKIN (Jurnal Manajemen Informatika) Jurnal Restikom : Riset Teknik Informatika dan Komputer Jurnal Teknik Informatika Unika Santo Thomas (JTIUST) Journal of Community Services: Sustainability and Empowerment
Syahid Abdullah
Universitas Siber Asia
Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Social Sciences Other

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Journal : Jurnal Teknologi dan Sistem Komputer

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Prediksi interaksi protein-protein berbasis sekuens protein menggunakan fitur autocorrelation dan machine learning Syahid Abdullah; Wisnu Ananta Kusuma; Sony Hartono Wijaya
Jurnal Teknologi dan Sistem Komputer Volume 10, Issue 1, Year 2022 (January 2022)
Publisher : Department of Computer Engineering, Engineering Faculty, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/jtsiskom.2021.13984

Abstract

Protein-protein interaction (PPI) can define a protein's function by knowing the protein's position in a complex network of protein interactions. The number of PPIs that have been identified is relatively small. Therefore, several studies were conducted to predict PPI using protein sequence information. This research compares the performance of three autocorrelation methods: Moran, Geary, and Moreau-Broto, in extracting protein sequence features to predict PPI. The results of the three extractions are then applied to three machine learning algorithms, namely k-Nearest Neighbor (KNN), Random Forest, and Support Vector Machine (SVM). The prediction models with the three autocorrelation methods can produce predictions with high average accuracy, which is 95.34% for Geary in KNN, 97.43% for Geary in RF, and 97.11% for Geary and Moran in SVM. In addition, the interacting protein pairs tend to have similar autocorrelation characteristics. Thus, the autocorrelation method can be used to predict PPI well.
Co-Authors Bei Harira Irawan Catur Nugroho Cian Ramadhona Hassolthine Deni Muhammad Sepudin Muhammad Ikhwani Saputra Noto Sunoto Riad Sahara Rizal Ardiyansyah Seno Prasetyo Sony Hartono Wijaya Wisnu Ananta Kusuma Yunita Ramanda