LGFC-CNN: Prediction of lncRNA-Protein Interactions by Using Multiple Types of Features through Deep Learning
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Title
LGFC-CNN: Prediction of lncRNA-Protein Interactions by Using Multiple Types of Features through Deep Learning
Authors
Keywords
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Journal
Genes
Volume 12, Issue 11, Pages 1689
Publisher
MDPI AG
Online
2021-10-25
DOI
10.3390/genes12111689
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Note: Only part of the references are listed.- VECTOR: An Integrated Correlation Network Database for the Identification of CeRNA Axes in Uveal Melanoma
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- Gene regulation by long non-coding RNAs and its biological functions
- (2020) Luisa Statello et al. NATURE REVIEWS MOLECULAR CELL BIOLOGY
- NONCODEV6: an updated database dedicated to long non-coding RNA annotation in both animals and plants
- (2020) Lianhe Zhao et al. NUCLEIC ACIDS RESEARCH
- ACCBN: ant-Colony-clustering-based bipartite network method for predicting long non-coding RNA–protein interactions
- (2019) Rong Zhu et al. BMC BIOINFORMATICS
- CPPred: coding potential prediction based on the global description of RNA sequence
- (2019) Xiaoxue Tong et al. NUCLEIC ACIDS RESEARCH
- LncRNA FAM83H-AS1 contributes to the radioresistance, proliferation, and metastasis in ovarian cancer through stabilizing HuR protein
- (2019) Qianru Dou et al. EUROPEAN JOURNAL OF PHARMACOLOGY
- LPI-BLS: Predicting lncRNA–protein interactions with a broad learning system-based stacked ensemble classifier
- (2019) Xiao-Nan Fan et al. NEUROCOMPUTING
- BioSeq-Analysis2.0: an updated platform for analyzing DNA, RNA and protein sequences at sequence level and residue level based on machine learning approaches
- (2019) Bin Liu et al. NUCLEIC ACIDS RESEARCH
- NPInter v4.0: an integrated database of ncRNA interactions
- (2019) Xueyi Teng et al. NUCLEIC ACIDS RESEARCH
- LncRNA NEAT1 promotes autophagy in MPTP-induced Parkinson's disease through stabilizing PINK1 protein
- (2018) Wang Yan et al. BIOCHEMICAL AND BIOPHYSICAL RESEARCH COMMUNICATIONS
- LncADeep: an ab initio lncRNA identification and functional annotation tool based on deep learning
- (2018) Cheng Yang et al. BIOINFORMATICS
- Predicting RNA–protein binding sites and motifs through combining local and global deep convolutional neural networks
- (2018) Xiaoyong Pan et al. BIOINFORMATICS
- The linear neighborhood propagation method for predicting long non-coding RNA–protein interactions
- (2018) Wen Zhang et al. NEUROCOMPUTING
- UniProt: the universal protein knowledgebase
- (2018) The UniProt Consortium NUCLEIC ACIDS RESEARCH
- The Bipartite Network Projection Recommended Algorithm for predicting long non-coding RNA–protein interactions
- (2018) Qi Zhao et al. Molecular Therapy-Nucleic Acids
- Combining High Speed ELM Learning with a Deep Convolutional Neural Network Feature Encoding for Predicting Protein-RNA Interactions
- (2018) Lei Wang et al. IEEE-ACM Transactions on Computational Biology and Bioinformatics
- LPI-NRLMF: lncRNA-protein interaction prediction by neighborhood regularized logistic matrix factorization
- (2017) Hongsheng Liu et al. Oncotarget
- RPI-Bind: a structure-based method for accurate identification of RNA-protein binding sites
- (2017) Jiesi Luo et al. Scientific Reports
- IPMiner: hidden ncRNA-protein interaction sequential pattern mining with stacked autoencoder for accurate computational prediction
- (2016) Xiaoyong Pan et al. BMC GENOMICS
- A novel computational method for inferring competing endogenous interactions
- (2016) Davide S. Sardina et al. BRIEFINGS IN BIOINFORMATICS
- DanQ: a hybrid convolutional and recurrent deep neural network for quantifying the function of DNA sequences
- (2016) Daniel Quang et al. NUCLEIC ACIDS RESEARCH
- NPInter v3.0: an upgraded database of noncoding RNA-associated interactions
- (2016) Yajing Hao et al. Database-The Journal of Biological Databases and Curation
- OUGENE: a disease associated over-expressed and under-expressed gene database
- (2016) Xiaoyong Pan et al. Science Bulletin
- A Bipartite Network-based Method for Prediction of Long Non-coding RNA–protein Interactions
- (2016) Mengqu Ge et al. GENOMICS PROTEOMICS & BIOINFORMATICS
- Deep learning
- (2015) Yann LeCun et al. NATURE
- Computational prediction of associations between long non-coding RNAs and proteins
- (2013) Qiongshi Lu et al. BMC GENOMICS
- Targeting long non-coding RNAs in cancers: Progress and prospects
- (2013) Chi Han Li et al. INTERNATIONAL JOURNAL OF BIOCHEMISTRY & CELL BIOLOGY
- ViennaRNA Package 2.0
- (2011) Ronny Lorenz et al. Algorithms for Molecular Biology
- Predicting RNA-Protein Interactions Using Only Sequence Information
- (2011) Usha K Muppirala et al. BMC BIOINFORMATICS
- Discriminative prediction of mammalian enhancers from DNA sequence
- (2011) D. Lee et al. GENOME RESEARCH
- RNA–protein interactions in human health and disease
- (2011) Ahmad M. Khalil et al. SEMINARS IN CELL & DEVELOPMENTAL BIOLOGY
- A new taxonomy-based protein fold recognition approach based on autocross-covariance transformation
- (2009) Qiwen Dong et al. BIOINFORMATICS
- Biopython: freely available Python tools for computational molecular biology and bioinformatics
- (2009) P. J. A. Cock et al. BIOINFORMATICS
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