Multivariate Information Fusion With Fast Kernel Learning to Kernel Ridge Regression in Predicting LncRNA-Protein Interactions.
TLDR
A novel method for identifying LPI by employing Kernel Ridge Regression, based on Fast Kernel Learning (LPI-FKLKRR), which has extraordinary performance compared with LPI prediction schemes.Abstract:
Long non-coding RNAs (lncRNAs) constitute a large class of transcribed RNA molecules. They have a characteristic length of more than 200 nucleotides which do not encode proteins. They play an important role in regulating gene expression by interacting with the homologous RNA-binding proteins. Due to the laborious and time-consuming nature of wet experimental methods, more researchers should pay great attention to computational approaches for the prediction of lncRNA-protein interaction (LPI). An in-depth literature review in the state-of-the-art in silico investigations, leads to the conclusion that there is still room for improving the accuracy and velocity. This paper propose a novel method for identifying LPI by employing Kernel Ridge Regression, based on Fast Kernel Learning (LPI-FKLKRR). This approach, uses four distinct similarity measures for lncRNA and protein space, respectively. It is remarkable, that we extract Gene Ontology (GO) with proteins, in order to improve the quality of information in protein space. The process of heterogeneous kernels integration, applies Fast Kernel Learning (FastKL) to deal with weight optimization. The extrapolation model is obtained by gaining the ultimate prediction associations, after using Kernel Ridge Regression (KRR). Experimental outcomes show that the ability of modeling with LPI-FKLKRR has extraordinary performance compared with LPI prediction schemes. On benchmark dataset, it has been observed that the best Area Under Precision Recall Curve (AUPR) of 0.6950 is obtained by our proposed model LPI-FKLKRR, which outperforms the integrated LPLNP (AUPR: 0.4584), RWR (AUPR: 0.2827), CF (AUPR: 0.2357), LPIHN (AUPR: 0.2299), and LPBNI (AUPR: 0.3302). Also, combined with the experimental results of a case study on a novel dataset, it is anticipated that LPI-FKLKRR will be a useful tool for LPI prediction.read more
Citations
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Probing lncRNA-Protein Interactions: Data Repositories, Models, and Algorithms.
Lihong Peng,Fuxing Liu,Jialiang Yang,Xiaojun Liu,Yajie Meng,Xiaojun Deng,Cheng Peng,Geng Tian,Liqian Zhou +8 more
TL;DR: To detect the performance of computational methods, parts of LPI prediction models are compared on Leave-One-Out cross-validation (LOOCV) and fivefold cross- validation (AUC) and the results show that SFPEL-LPI obtained the best performance of AUC.
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Machine-learning and high-throughput studies for high-entropy materials
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TL;DR: In this article , the authors review both the materials informatics and experimental approaches for the high-throughput (HT) approach, especially to identify the specified functions for the new HEMs development.
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A deep learning model for plant lncRNA-protein interaction prediction with graph attention
TL;DR: GPLPI, a graph representation learning method, is proposed to predict plant lncRNA-protein interaction (LPI) from sequence and structural information and consistently outperforms other state-of-the-art methods.
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