Paper Info
Title
A Stacked Ensemble Learning Framework with Heterogeneous Feature Combinations for Predicting ncRNA-Protein Interaction
Abstract
The interaction between ncRNA and protein is a kind of crucial molecular activities in a cell. Developing computational methods to predict ncRNA-protein interactions has attracted increasing attentions in recent years. In this work, a novel stacked ensemble learning framework is presented for predicting ncRNA-protein interaction based on heterogeneous feature combinations, named HFC-RPI. Firstly, the compositional features of k-mer with different orders were extracted from the primary sequence and secondary structure of RNA and protein respectively. Secondly, we trained a set of base learners using a variety of heterogeneous combinations of the extracted features respectively. Thirdly, the prediction results of these base learners were employed to train the stacked learner, which output the final prediction result at the higher layer in HFC-RPI. Moreover, in order to improve the generalization of HFC-RPI, when training the base learners, a cross-validation based method was applied. Extensive experimental results showed that the proposed learning framework HFC-RPI was effective and feasible for predicting the interaction of ncRNA and protein. By comparing with state-of-the-art methods, HFC-RPI was superior to them on most performance evaluation metrics.
Year
DOI
Venue
2020
10.1109/BIBM49941.2020.9313446
2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)
Keywords
DocType
ISBN
stacked,ensemble learning,non-coding RNA,protein,interaction
Conference
978-1-7281-6216-4
Citations 
PageRank 
References 
0
0.34
0
Authors
6
Name
Order
Citations
PageRank
Qiguo Dai1412.83
Zhaowei Wang200.68
Jinmiao Song301.01
Duan Xiaodong48516.18
Mao-Zu Guo552653.96
Zhen Tian601.69