Identification of Orphan Genes in Unbalanced Datasets Based on Ensemble Learning

Abstract

Orphan genes are associated with regulatory patterns, but experimental methods for identifying orphan genes are both time-consuming and expensive. Designing an accurate and robust classification model to detect orphan and non-orphan genes in unbalanced distribution datasets poses a particularly huge challenge. Synthetic minority over-sampling algorithms (SMOTE) are selected in a preliminary step to deal with unbalanced gene datasets. To identify orphan genes in balanced and unbalanced Arabidopsis thaliana gene datasets, SMOTE algorithms were then combined with traditional and advanced ensemble classified algorithms respectively, using Support Vector Machine, Random Forest (RF), AdaBoost (adaptive boosting), GBDT (gradient boosting decision tree), and XGBoost (extreme gradient boosting). After comparing the performance of these ensemble models, SMOTE algorithms with XGBoost achieved an F1 score of 0.94 with the balanced A. thaliana gene datasets, but a lower score with the unbalanced datasets. The proposed ensemble method combines different balanced data algorithms including Borderline SMOTE (BSMOTE), Adaptive Synthetic Sampling (ADSYN), SMOTE-Tomek, and SMOTE-ENN with the XGBoost model separately. The performances of the SMOTE-ENN-XGBoost model, which combined over-sampling and under-sampling algorithms with XGBoost, achieved higher predictive accuracy than the other balanced algorithms with XGBoost models. Thus, SMOTE-ENN-XGBoost provides a theoretical basis for developing evaluation criteria for identifying orphan genes in unbalanced and biological datasets.

Predicting Heritability of Oil Palm Breeding Using Phenotypic Traits and Machine Learning

“…A recent study [87] used ensemble learning methods to identify orphan genes. In the study, it was found that XGBoost has better performance than RF and SVM.…”

Research Advances and Prospects of Orphan Genes in Plants

“…Taking into consideration the rice genome, in which 37 OGs were obtained under BLAST and BLAT (BLAST-Like Alignment Tool) programs (Jin et al, 2019). Other effective modules or programs include the SMOTE-ENN-XGBoost model (Synthetic Minority Over-sampling TEchnique-Edited Nearest Neighbors-eXtreme Gradient Boosting) (Gao et al, 2020), BIND (BRAK-ER-Inferred Directly), and MIND (MAKER-Inferred Directly) platforms (Li J. et al, 2021), ORFanFinder (Ekstrom and Yin, 2016), combined BLAST and Microarray-based genome hybridization methods (Li G. et al, 2019).…”

Relevant Characteristics Analysis Using Natural Language Processing and Machine Learning Based on Phenotypes and T-Cell Subsets in Systemic Lupus Erythematosus Patients With Anxiety

“…We matched clinical information, laboratory indicators, disease phenotypes, and cell subpopulation data separately to construct five types of original data sets. Then, the use of the original data sets was compared and the oversampling and the algorithm of Syntic priority oversampling technology (SMOTE) methods (22) which was used to make up for the imbalance in the number of cases included in the data set on various models, including Lasso regression (LR) (23), Random Forest (RF) (22) and XGBoost (24). When applying the oversampling and SMOTE method, the (see Supplementary Table 1).…”