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LPI-DL: A recurrent deep learning model for plant lncRNA-protein interaction and function prediction with feature optimization | IEEE Conference Publication | IEEE Xplore

LPI-DL: A recurrent deep learning model for plant lncRNA-protein interaction and function prediction with feature optimization

Publisher: IEEE

Abstract:

Predicting lncRNA-protein association is essential for insights into fundamental biological processes and disease etiology in plants and animals. There has been an enormo...View more

Abstract:

Predicting lncRNA-protein association is essential for insights into fundamental biological processes and disease etiology in plants and animals. There has been an enormous increment in the number of identified long noncoding RNAs (lncRNAs). However, less efforts has been directed towards lncRNA-protein interaction (LPI) prediction to help in characterizing the huge array of plant lncRNAs. This study presents LPI-DL, a deep learning method for predicting potential plant lncRNA-protein interaction based on sequence features and compact LSTM. The optimal combination of k-nucleotide frequencies and codon-based encoding features are used as input to the model. The recurrent neural network learns the discriminative features characterizing the long-term dependencies between sequences. We select optimal features using recursive feature elimination and support vector machine (RFE-SVM) and impose sparse projection onto the hidden states of input sequences through connection pruning. Evaluation of two plant datasets corroborates that LPI-DL is more competitive over other methods. Comparative experiments denote that the proposed method achieves state-of-the-art prediction performance. This study effectively improves the accuracy of interaction prediction and lays a foundation to foster lncRNA functional studies.
Date of Conference: 16-19 December 2020
Date Added to IEEE Xplore: 13 January 2021
ISBN Information:
Publisher: IEEE
Conference Location: Seoul, Korea (South)

References

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