Classification of IGF1R ligand compounds for Identification of herbal extracts using extreme gradient boosting

Authors

  • Mohammad Hamim Zajuli Al Faroby Department of Data Science, Faculty Information Technology and Business, Institut Teknologi Telkom Surabaya
  • Siti Amiroch Department of Mathematics, Faculty of Mathematics and Natural Science, Universitas Islam Darul ‘Ulum, Lamongan, Indonesia
  • Bernadus Anggo Seno Aji Department of Information Technology, Faculty of Information Technology and Business, Institut Teknologi Telkom Surabaya, Surabaya, Indonesia
  • Avriono Aritonang Department of Data Science, Faculty of Information Technology and Business, Institut Teknologi Telkom Surabaya, Surabaya, Indonesia

DOI:

https://doi.org/10.26555/jifo.v16i3.a23286

Keywords:

Molecular Fingerprint, Extreme Gradient Boosting, Herbal Compound, Machine Learning, IGF1R

Abstract

Diabetes Mellitus is a serious disease that requires serious treatment. The cause of this disease is due to malfunctions in insulin and insulin-producing organs. One of the proteins that become insulin signaling receptors is IGF1R, which has an important role in activating and maximizing insulin performance. In this study, we aimed to obtain herbal compounds that can activate the function of the IGF1R protein by utilizing compound data in an open database and modeling it using the ensemble method, namely extreme gradient boosting. We found that this method produces the best classification model than with other algorithms. We predicted 844 data for herbal compounds, but only 15 data met the threshold of 0.6. We got one plant from the fifteen herbal compounds, namely Zostera Marine, which was confirmed to have compounds that bind to IGF1R. These compounds have the highest probability value in the classification model that we formed compared to others.

Author Biographies

Mohammad Hamim Zajuli Al Faroby, Department of Data Science, Faculty Information Technology and Business, Institut Teknologi Telkom Surabaya

I completed my master’s degree at the Department of Mathematics, Institut Teknologi Sepuluh Nopember in 2020. My thesis is in the field of bioinformatics. Until now, I have been a lecturer in the data science study program at the Telkom Institute of Technology Surabaya; my research is in Bioinformatics, especially in protein data analysis and drug design

Siti Amiroch, Department of Mathematics, Faculty of Mathematics and Natural Science, Universitas Islam Darul ‘Ulum, Lamongan, Indonesia

She is a doctoral student in the mathematics department of ITS. Her interest is in computer science, and his dissertation topic is in the area of bioinformatics.

Bernadus Anggo Seno Aji, Department of Information Technology, Faculty of Information Technology and Business, Institut Teknologi Telkom Surabaya, Surabaya, Indonesia

His research interest in artificial intelligence and data mining

Avriono Aritonang, Department of Data Science, Faculty of Information Technology and Business, Institut Teknologi Telkom Surabaya, Surabaya, Indonesia

Student in the department of Data Science, ITTS.

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Published

2022-09-30

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Section

Computational Intelligence