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Prediksi interaksi protein-protein berbasis sekuens protein menggunakan fitur autocorrelation dan machine learning

Sequence-based prediction of protein-protein interaction using autocorrelation features and machine learning

1Department of Computer Science, IPB University. Jl. Raya Dramaga, Kampus IPB Dramaga, Bogor 16680, Indonesia

2Tropical Biopharmaca Research Center, IPB University. Jl. Taman Kencana No. 3, Bogor 16128, Indonesia

Received: 18 Nov 2020; Revised: 14 Sep 2021; Accepted: 4 Jan 2022; Published: 31 Jan 2022.
Open Access Copyright (c) 2022 The authors. Published by Department of Computer Engineering, Universitas Diponegoro
Creative Commons License This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

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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.
Keywords: autocorrelation; machine learning; protein-protein interaction; protein sequence
Funding: Kementrian Riset, Teknologi, dan Pendidikan Tinggi under contract 4168/IT3.I.1/PN/2019

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