Results 241 to 250 of about 65,339 (267)

Relevance search for predicting lncRNA–protein interactions based on heterogeneous network

Neurocomputing, 2016
lncRNA plays important roles in many biological and pathological processes. lncRNAprotein interaction is the most common way of lncRNA performing their functions. Thus, predicting lncRNAprotein interaction is very significant to understand the nature of lncRNA.
Ao Li, Mengqu Ge, Minghui Wang
exaly   +2 more sources

Computational Prediction of lncRNA-Protein Interactions using Machine learning

2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), 2021
Long non-coding RNAs have generated much scientific interest because of their functional significance in regulating various biological processes and also their dysfunction has been implicated in disease progression. LncRNAs usually bind with proteins to perform their function.
Muhammad Mushtaq, Hammad Naveed, Zoya Khalid   +2 more
openaire   +2 more sources

Multi-feature fusion for deep learning to predict plant lncRNA-protein interaction

Genomics, 2020
Long non-coding RNAs (lncRNAs) play key roles in regulating cellular biological processes through diverse molecular mechanisms including binding to RNA binding proteins. The majority of plant lncRNAs are functionally uncharacterized, thus, accurate prediction of plant lncRNA-protein interaction is imperative for subsequent functional studies.
Jael Sanyanda Wekesa, Jun Meng, Yushi Luan   +2 more
exaly   +3 more sources

Identification of lncRNA–Protein Interactions by CLIP and RNA Pull-Down Assays

2021
The emerging data indicates that long noncoding RNAs (lncRNAs) are involved in fundamental biological processes, and their deregulation may lead to oncogenesis and other diseases. LncRNA fulfil its biological functions at least in part by interacting with distinctive proteins.
Kunming, Zhao, Xingwen, Wang, Ying, Hu
openaire   +2 more sources

A deep learning model for plant lncRNA-protein interaction prediction with graph attention

Molecular Genetics and Genomics, 2020
Long non-coding RNAs (lncRNAs) play a broad spectrum of distinctive regulatory roles through interactions with proteins. However, only a few plant lncRNAs have been experimentally characterized. We propose GPLPI, a graph representation learning method, to predict plant lncRNA-protein interaction (LPI) from sequence and structural information.
Jael Sanyanda Wekesa, Jun Meng, Yushi Luan   +2 more
openaire   +2 more sources

LPLSG: Prediction of lncRNA-protein Interaction Based on Local Network Structure

Current Bioinformatics, 2023
Background: The interaction between RNA and protein plays an important role in life activities. Long ncRNAs (lncRNAs) are large non-coding RNAs, and have received extensive attention in recent years. Because the interaction between RNA and protein is tissue-specific and condition-specific, it is time-consuming and expensive to predict the interaction ...
Wei Wang, Yongqing Wang, Bin Sun, Shihao Liang, Dong Liu, Hongjun Zhang, Xianfang Wang   +6 more
openaire   +1 more source

Predicting lncRNA-protein Interactions by Machine Learning Methods: A Review

Current Bioinformatics, 2021
In this work, a review of predicting lncRNA-protein interactions by bioinformatics methods is provided with a focus on machine learning. Firstly, a computational framework for predicting lncRNA-protein interactions is presented. Then, the currently available data resources for the predictions have been listed. The existing methods will be reviewed by
openaire   +1 more source

A text feature-based approach for literature mining of lncRNA–protein interactions

Neurocomputing, 2016
Long non-coding RNAs (lncRNAs) play important roles in regulating transcriptional and post-transcriptional levels. Currently, Knowledge of lncRNA and protein interactions (LPIs) is crucial for biomedical researches that are related to lncRNA. Many freshly discovered LPIs are stored in biomedical literature.
Ao Li 0001, Qiguang Zang, Dongdong Sun, Minghui Wang   +3 more
openaire   +1 more source

Finding lncRNA-Protein Interactions Based on Deep Learning With Dual-Net Neural Architecture

IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2022
The identification of lncRNA-protein interactions (LPIs) is important to understand the biological functions and molecular mechanisms of lncRNAs. However, most computational models are evaluated on a unique dataset, thereby resulting in prediction bias.
Lihong Peng, Chang Wang, Xiongfei Tian, Liqian Zhou, Keqin Li 0001   +4 more
openaire   +2 more sources

Cross-domain contrastive graph neural network for lncRNA–protein interaction prediction

Knowledge-Based Systems
Zhenfeng Zhu, Miaomiao Sun, Hui Li
exaly   +2 more sources