AUTHOR=Ma Dong , Chen Zhihua , He Zhanpeng , Huang Xueqin 

TITLE=A SNARE Protein Identification Method Based on iLearnPlus to Efficiently Solve the Data Imbalance Problem

JOURNAL=Frontiers in Genetics

VOLUME=Volume 12 - 2021

YEAR=2022

URL=https://www.frontiersin.org/journals/genetics/articles/10.3389/fgene.2021.818841

DOI=10.3389/fgene.2021.818841

ISSN=1664-8021

ABSTRACT=Machine learning has been widely used to solve complex problems in engineering applications and 10
scientific fields, and many machine learning-based methods have achieved good results in different 11
fields. SNAREs are key elements of membrane fusion and required for the fusion process of stable 12
intermediates. They are also associated with the formation of some psychiatric disorders. This study 13
processes the original sequence data with the synthetic minority oversampling technique (SMOTE) to 14
solve the problem of data imbalance and produces the most suitable machine learning model with the 15
iLearnPlus platform for the identification of SNARE proteins. Ultimately, a sensitivity of 66.67%, 16
specificity of 93.63%, accuracy of 91.33%, and MCC of 0.528 were obtained in the cross-validation 17
dataset, and a sensitivity of 66.67%, specificity of 93.63%, accuracy of 91.33%, and MCC of 0.528 18
were obtained in the independent dataset (the adaptive skip dipeptide composition descriptor was 19
used for feature extraction, and LightGBM with proper parameters was used as the classifier). These 20
results demonstrate that this combination can perform well in the classification of SNARE proteins 21
and is superior to other methods.