LGFC-CNN: Prediction of lncRNA-Protein Interactions by Using Multiple Types of Features through Deep Learning

Long noncoding RNA (lncRNA) plays a crucial role in many critical biological processes and participates in complex human diseases through interaction with proteins. Considering that identifying lncRNA–protein interactions through experimental methods is expensive and time-consuming, we propose a nov...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Lan Huang, Shaoqing Jiao, Sen Yang, Shuangquan Zhang, Xiaopeng Zhu, Rui Guo, Yan Wang
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	lncRNA-protein interactions convolutional neural network two sequence preprocessing methods raw sequence features hand-designed features structure features Genetics QH426-470
Acceso en línea:	https://doaj.org/article/6923740578cb41fab63fa8dd4801a63f
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:6923740578cb41fab63fa8dd4801a63f
record_format	dspace
spelling	oai:doaj.org-article:6923740578cb41fab63fa8dd4801a63f2021-11-25T17:40:52ZLGFC-CNN: Prediction of lncRNA-Protein Interactions by Using Multiple Types of Features through Deep Learning10.3390/genes121116892073-4425https://doaj.org/article/6923740578cb41fab63fa8dd4801a63f2021-10-01T00:00:00Zhttps://www.mdpi.com/2073-4425/12/11/1689https://doaj.org/toc/2073-4425Long noncoding RNA (lncRNA) plays a crucial role in many critical biological processes and participates in complex human diseases through interaction with proteins. Considering that identifying lncRNA–protein interactions through experimental methods is expensive and time-consuming, we propose a novel method based on deep learning that combines raw sequence composition features, hand-designed features and structure features, called LGFC-CNN, to predict lncRNA–protein interactions. The two sequence preprocessing methods and CNN modules (GloCNN and LocCNN) are utilized to extract the raw sequence global and local features. Meanwhile, we select hand-designed features by comparing the predictive effect of different lncRNA and protein features combinations. Furthermore, we obtain the structure features and unifying the dimensions through Fourier transform. In the end, the four types of features are integrated to comprehensively predict the lncRNA–protein interactions. Compared with other state-of-the-art methods on three lncRNA–protein interaction datasets, LGFC-CNN achieves the best performance with an accuracy of 94.14%, on RPI21850; an accuracy of 92.94%, on RPI7317; and an accuracy of 98.19% on RPI1847. The results show that our LGFC-CNN can effectively predict the lncRNA–protein interactions by combining raw sequence composition features, hand-designed features and structure features.Lan HuangShaoqing JiaoSen YangShuangquan ZhangXiaopeng ZhuRui GuoYan WangMDPI AGarticlelncRNA-protein interactionsconvolutional neural networktwo sequence preprocessing methodsraw sequence featureshand-designed featuresstructure featuresGeneticsQH426-470ENGenes, Vol 12, Iss 1689, p 1689 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	lncRNA-protein interactions convolutional neural network two sequence preprocessing methods raw sequence features hand-designed features structure features Genetics QH426-470
spellingShingle	lncRNA-protein interactions convolutional neural network two sequence preprocessing methods raw sequence features hand-designed features structure features Genetics QH426-470 Lan Huang Shaoqing Jiao Sen Yang Shuangquan Zhang Xiaopeng Zhu Rui Guo Yan Wang LGFC-CNN: Prediction of lncRNA-Protein Interactions by Using Multiple Types of Features through Deep Learning
description	Long noncoding RNA (lncRNA) plays a crucial role in many critical biological processes and participates in complex human diseases through interaction with proteins. Considering that identifying lncRNA–protein interactions through experimental methods is expensive and time-consuming, we propose a novel method based on deep learning that combines raw sequence composition features, hand-designed features and structure features, called LGFC-CNN, to predict lncRNA–protein interactions. The two sequence preprocessing methods and CNN modules (GloCNN and LocCNN) are utilized to extract the raw sequence global and local features. Meanwhile, we select hand-designed features by comparing the predictive effect of different lncRNA and protein features combinations. Furthermore, we obtain the structure features and unifying the dimensions through Fourier transform. In the end, the four types of features are integrated to comprehensively predict the lncRNA–protein interactions. Compared with other state-of-the-art methods on three lncRNA–protein interaction datasets, LGFC-CNN achieves the best performance with an accuracy of 94.14%, on RPI21850; an accuracy of 92.94%, on RPI7317; and an accuracy of 98.19% on RPI1847. The results show that our LGFC-CNN can effectively predict the lncRNA–protein interactions by combining raw sequence composition features, hand-designed features and structure features.
format	article
author	Lan Huang Shaoqing Jiao Sen Yang Shuangquan Zhang Xiaopeng Zhu Rui Guo Yan Wang
author_facet	Lan Huang Shaoqing Jiao Sen Yang Shuangquan Zhang Xiaopeng Zhu Rui Guo Yan Wang
author_sort	Lan Huang
title	LGFC-CNN: Prediction of lncRNA-Protein Interactions by Using Multiple Types of Features through Deep Learning
title_short	LGFC-CNN: Prediction of lncRNA-Protein Interactions by Using Multiple Types of Features through Deep Learning
title_full	LGFC-CNN: Prediction of lncRNA-Protein Interactions by Using Multiple Types of Features through Deep Learning
title_fullStr	LGFC-CNN: Prediction of lncRNA-Protein Interactions by Using Multiple Types of Features through Deep Learning
title_full_unstemmed	LGFC-CNN: Prediction of lncRNA-Protein Interactions by Using Multiple Types of Features through Deep Learning
title_sort	lgfc-cnn: prediction of lncrna-protein interactions by using multiple types of features through deep learning
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/6923740578cb41fab63fa8dd4801a63f
work_keys_str_mv	AT lanhuang lgfccnnpredictionoflncrnaproteininteractionsbyusingmultipletypesoffeaturesthroughdeeplearning AT shaoqingjiao lgfccnnpredictionoflncrnaproteininteractionsbyusingmultipletypesoffeaturesthroughdeeplearning AT senyang lgfccnnpredictionoflncrnaproteininteractionsbyusingmultipletypesoffeaturesthroughdeeplearning AT shuangquanzhang lgfccnnpredictionoflncrnaproteininteractionsbyusingmultipletypesoffeaturesthroughdeeplearning AT xiaopengzhu lgfccnnpredictionoflncrnaproteininteractionsbyusingmultipletypesoffeaturesthroughdeeplearning AT ruiguo lgfccnnpredictionoflncrnaproteininteractionsbyusingmultipletypesoffeaturesthroughdeeplearning AT yanwang lgfccnnpredictionoflncrnaproteininteractionsbyusingmultipletypesoffeaturesthroughdeeplearning
_version_	1718412109182664704

LGFC-CNN: Prediction of lncRNA-Protein Interactions by Using Multiple Types of Features through Deep Learning

Ejemplares similares