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
BibTex Citation Data :
@article{JTSISKOM13984, author = {Syahid Abdullah and Wisnu Ananta Kusuma and Sony Hartono Wijaya}, title = {Prediksi interaksi protein-protein berbasis sekuens protein menggunakan fitur autocorrelation dan machine learning}, journal = {Jurnal Teknologi dan Sistem Komputer}, volume = {10}, number = {1}, year = {2022}, keywords = {autocorrelation; machine learning; protein-protein interaction; protein sequence}, 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.}, issn = {2338-0403}, pages = {1--11} doi = {10.14710/jtsiskom.2022.13984}, url = {https://jtsiskom.undip.ac.id/article/view/13984} }
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