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.
Highlights
The process of identifying orphan genes is an emerging field
The whole genome data of the angiosperm A. thaliana were obtained from The Arabidopsis Information Resource (TAIR8) dataset ftp://ftp.arabidopsis.org/home/tair/Genes/ TAIR8_genome_release, which contained a total of 32825 gene sequences
The known orphan genes of A. thaliana downloaded from the public website https://www.biomedcentral.com/ content/supplementary/1471-2148-10-%2041-S2.TXT (Lin et al, 2010)
Summary
The process of identifying orphan genes is an emerging field. Orphan genes play critical roles in the evolution of species and the adaptability of the environment (Davies and Davies, 2010; Donoghue et al, 2011; Huang, 2013; Cooper, 2014; Gao et al, 2014). Arendsee et al, 2014), Many attempts have been made to identify orphan genes in multiple species or taxa and to analyze their functions. Orphan genes are detected mainly by comparison of genome and transcriptome sequences of related species using BLAST (Blast-Basic Local Alignment Search Tool; Altschul et al, 1990; Tollriera et al, 2009). This approach requires large server resources and time, and common problems with complexity and timeliness occur (Ye et al, 2012)
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