Comparison of Deep Learning and Other Classification Algorithms in Small Dataset Studies: Example of Agonist and Antagonist Ligand

Year 2022, , 356 - 371, 10.03.2022

Fatih Mehmet Avcu

https://doi.org/10.33715/inonusaglik.1022065

Cited By: 2

Abstract

Machine learning algorithms are used in almost all branches of science today. In particular, classification algorithms are a very popular subject in terms of science and health sciences. Deep learning is one of the machine learning techniques like other algorithms Today, it has become popular again due to the increase in processor speeds. Particularly graphics processor-based calculations have made this subject popular. The aim of this study is to classify the agonist and antiagonist molecules that bind to dopamine receptors, which are well known in the literature, with the data we obtained from chemistry databases, with machine learning algorithms. The aim of the study is also to suggest the use of a deep learning algorithm for an accurate classification when classifying in cases where the number of data is small. Scikit-learn and Tensorflow-Keras from Python libraries were used for training the algorithm. The classification process has been compared with popular machine learning algorithms and the results have been presented as a table.

Keywords

Agonist-Antagonist, Classification, Deep learning, Ligand, Machine learning

AZ VERİ SETLİ ÇALIŞMALARINDA DERİN ÖĞRENME VE DİĞER SINIFLANDIRMA ALGORİTMALARININ KARŞILAŞTIRILMASI: AGONİST VE ANTAGONİST LİGAND ÖRNEĞİ

Abstract

Makine öğrenme algoritmaları günümüzde hemen hemen tüm bilim dallarında kullanılmaktadır. Özellikle sınıflandırma algoritmaları fen ve sağlık bilimleri açısından oldukça popüler bir konudur. Derin öğrenme, diğer algoritmalar gibi makina öğrenme tekniklerinden biridir. Günümüzde işlemci hızlarının artması nedeni ile tekrar popüler olmuştur. Özellikle grafik işlemci tabanlı hesaplamalar bu konuyu popüler yapmıştır. Bu çalışmanın amacı, kimyasal veri tabanlarından elde edilen veriler ile literatürde iyi bilinen, dopamin reseptörlerine bağlanan agonist ve antiagonist moleküllerini makine öğrenme algoritmaları ile sınıflandırmaktır. Çalışmanın amacı ayrıca veri sayısı az olan durumlarda sınıflandırma yaparken doğru bir sınıflandırma için derin öğrenme algoritmasının kullanımını önermektir. Algoritmanın eğitmek için, Python kütüphanelerinden Scikit-learn ve Tensorflow-Keras kullanılmıştır. Sınıflandırma işlemi popüler makine öğrenme algoritmaları ile kıyaslanmış ve sonuçlar bir tablo olarak sunulmuştur.

Keywords

Agonist-Antagonist, Derin öğrenme, Makine öğrenmesi, Sınıflandırma, Ligant

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