An ensemble filter-based heuristic approach for cancerous gene expression classification

Uzma,; Halim, Zahid

doi:10.1016/j.knosys.2021.107560

Cited by 9 publications

(1 citation statement)

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“…In this paper, we divide into two parts feature selection, one is the filter-based feature selection. This algorithm adopts some principles involving information, consistency, dependency, and distance for measuring the feature characteristics, which are generalized for various classifiers based on the independent features of the machine learning algorithm [31]. For example, a variation filter is to remove the features with small difference value and retain the features with large variance value, because the variance of each feature determines the different degree of the feature in a sample.…”

Section: Data Preparation and Feature Selection Settingsmentioning

confidence: 99%

Machine Learning-Based Prediction of Orphan Genes and Analysis of Different Hybrid Features of Monocot and Eudicot Plants

et al. 2023

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Orphan genes (OGs) may evolve from noncoding sequences or be derived from older coding material. Some shares of OGs are present in all sequenced genomes, participating in the biochemical and physiological pathways of many species, while many of them may be associated with the response to environmental stresses and species-specific traits or regulatory patterns. However, identifying OGs is a laborious and time-consuming task. This paper presents an automated predictor, XGBoost-A2OGs (identification of OGs for angiosperm based on XGBoost), used to identify OGs for seven angiosperm species based on hybrid features and XGBoost. The precision and accuracy of the proposed model based on fivefold cross-validation and independent testing reached 0.90 and 0.91, respectively, outperforming other classifiers in cross-species validation via other models, namely, Random Forest, AdaBoost, GBDT, and SVM. Furthermore, by analyzing and subdividing the hybrid features into five sets, it was proven that different hybrid feature sets influenced the prediction performance of OGs involving eudicot and monocot groups. Finally, testing of small-scale empirical datasets of each species separately based on optimal hybrid features revealed that the proposed model performed better for eudicot groups than for monocot groups.

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Section: Data Preparation and Feature Selection Settingsmentioning

confidence: 99%