Semi-supervised multi-label feature selection with adaptive structure learning and manifold learning

2021 ◽  
pp. 106757
Author(s):  
Sitao Lv ◽  
Shengfei Shi ◽  
Hongzhi Wang ◽  
Feng Li
Keyword(s):  
Feature Selection ◽  
Manifold Learning ◽  
Structure Learning ◽  
Adaptive Structure

Adaptive structure learning for low-rank supervised feature selection

2018 ◽  
Vol 109 ◽  
pp. 89-96 ◽  
Author(s):  
Yonghua Zhu ◽  
Xuejun Zhang ◽  
Rongyao Hu ◽  
Guoqiu Wen
Keyword(s):  
Feature Selection ◽  
Structure Learning ◽  
Low Rank ◽  
Adaptive Structure

scholarly journals Prediction of hot spots in protein–DNA binding interfaces based on supervised isometric feature mapping and extreme gradient boosting

2020 ◽  
Vol 21 (S13) ◽  
Author(s):  
Ke Li ◽  
Sijia Zhang ◽  
Di Yan ◽  
Yannan Bin ◽  
Junfeng Xia
Keyword(s):  
Feature Selection ◽  
Manifold Learning ◽  
Hot Spots ◽  
Large Scale ◽  
Computational Method ◽  
Gradient Boosting ◽  
Feature Mapping ◽  
Accessible Information ◽  
Extreme Gradient Boosting ◽  
Isometric Feature Mapping

Abstract Background Identification of hot spots in protein-DNA interfaces provides crucial information for the research on protein-DNA interaction and drug design. As experimental methods for determining hot spots are time-consuming, labor-intensive and expensive, there is a need for developing reliable computational method to predict hot spots on a large scale. Results Here, we proposed a new method named sxPDH based on supervised isometric feature mapping (S-ISOMAP) and extreme gradient boosting (XGBoost) to predict hot spots in protein-DNA complexes. We obtained 114 features from a combination of the protein sequence, structure, network and solvent accessible information, and systematically assessed various feature selection methods and feature dimensionality reduction methods based on manifold learning. The results show that the S-ISOMAP method is superior to other feature selection or manifold learning methods. XGBoost was then used to develop hot spots prediction model sxPDH based on the three dimensionality-reduced features obtained from S-ISOMAP. Conclusion Our method sxPDH boosts prediction performance using S-ISOMAP and XGBoost. The AUC of the model is 0.773, and the F1 score is 0.713. Experimental results on benchmark dataset indicate that sxPDH can achieve generally better performance in predicting hot spots compared to the state-of-the-art methods.

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