Predictive Modeling of Cathepsin K Inhibitors Bioactivity for Osteoporosis Treatment by using Grey Wolf Optimizer-Support Vector Machine - Dalam bentuk buku karya ilmiah

MARCEL BINGGI ANTONIO LUMBANTOBING

Informasi Umum

Kode

25.04.7099

Klasifikasi

006.31 - Machine Learning

Jenis

Karya Ilmiah - Skripsi (S1) - Reference

Subjek

Machine Learning

Dilihat

105 kali

Informasi Lainnya

Abstraksi

<p>Osteoporosis is a condition when people diagnosed with it experience a bone mass loss due to the bone mineral density levels are below the standard. Cathepsin K (CatK), an enzyme involves in bone modelling process, is one of the causing factors of osteoporosis. The negative side effects of the existing osteoporosis treatment and CatK’s role in causing osteoporosis make CatK as the target of new osteoporosis treatment, named CatK inhibitor. However, the complex and resource significant nature of drug development raising challenges in the development of CatK inhibitor. This study aims to provide an alternative by using machine learning approach to overcome these challenges. This study develops a predictive modeling of CatK bioactivity using Grey Wolf Optimizer-Support Vector Machine (GWO-SVM). Feature selection process is performed using GWO to find the most influential features in the dataset. The SVM model proposed in this study predicts bioactivity class of CatK inhibitors which obtained from ChEMBL database. The results show that optimized SVM model with linear kernel achieves the accuracy of 0.996 and F1-score of 0.996. The GWO used in this study also proves its ability to reduce the size of the dataset significantly without compromising model performance.<i> </i></p>