scholarly journals End-to-end learning framework for circular RNA classification from other long non-coding RNAs using multi-modal deep learning.

2018 ◽  
Author(s):  
Mohamed Chaabane
Keyword(s):  
Deep Learning ◽  
Circular Rna ◽  
Learning Framework ◽  
Non Coding Rnas ◽  
End To End ◽  
Rna Classification

scholarly journals lncRNA_Mdeep: An Alignment-Free Predictor for Distinguishing Long Non-Coding RNAs from Protein-Coding Transcripts by Multimodal Deep Learning

2020 ◽  
Vol 21 (15) ◽  
pp. 5222 ◽  
Author(s):  
Xiao-Nan Fan ◽  
Shao-Wu Zhang ◽  
Song-Yao Zhang ◽  
Jin-Jie Ni
Keyword(s):  
Deep Learning ◽  
Prediction Accuracy ◽  
Protein Coding ◽  
Learning Framework ◽  
Alignment Free ◽  
Independent Test ◽  
Non Coding Rnas ◽  
High Level ◽  
Fold Cross Validation ◽  
Human Complex

Long non-coding RNAs (lncRNAs) play crucial roles in diverse biological processes and human complex diseases. Distinguishing lncRNAs from protein-coding transcripts is a fundamental step for analyzing the lncRNA functional mechanism. However, the experimental identification of lncRNAs is expensive and time-consuming. In this study, we presented an alignment-free multimodal deep learning framework (namely lncRNA_Mdeep) to distinguish lncRNAs from protein-coding transcripts. LncRNA_Mdeep incorporated three different input modalities, then a multimodal deep learning framework was built for learning the high-level abstract representations and predicting the probability whether a transcript was lncRNA or not. LncRNA_Mdeep achieved 98.73% prediction accuracy in a 10-fold cross-validation test on humans. Compared with other eight state-of-the-art methods, lncRNA_Mdeep showed 93.12% prediction accuracy independent test on humans, which was 0.94%~15.41% higher than that of other eight methods. In addition, the results on 11 cross-species datasets showed that lncRNA_Mdeep was a powerful predictor for predicting lncRNAs.

scholarly journals lncRNA_Mdeep: an alignment-free predictor for long non-coding RNAs identification by multimodal deep learning

2020 ◽  
Author(s):  
Xiao-Nan Fan ◽  
Shao-Wu Zhang ◽  
Song-Yao Zhang ◽  
Jin-Jie Ni
Keyword(s):  
Deep Learning ◽  
Prediction Accuracy ◽  
Protein Coding ◽  
Learning Framework ◽  
Alignment Free ◽  
Independent Test ◽  
Non Coding Rnas ◽  
High Level ◽  
Fold Cross Validation ◽  
Human Complex

Abstract Background: Long non-coding RNAs (lncRNAs) play crucial roles in diverse biological processes and human complex diseases. Distinguishing lncRNAs from protein-coding transcripts is a fundamental step for analyzing lncRNA functional mechanism. However, the experimental identification of lncRNAs is expensive and time-consuming. Results: In this study, we present an alignment-free multimodal deep learning framework (namely lncRNA_Mdeep) to distinguish lncRNAs from protein-coding transcripts. LncRNA_Mdeep incorporates three different input modalities (i.e. OFH modality, k-mer modality, and sequence modality), then a multimodal deep learning framework is built for learning the high-level abstract representations and predicting the probability whether a transcript is lncRNA or not. Conclusions: LncRNA_Mdeep achieves 98.73% prediction accuracy in 10-fold cross-validation test on human. Compared with other eight state-of-the-art methods, lncRNA_Mdeep shows 93.12% prediction accuracy independent test on human, which is 0.94%~15.41% higher than that of other eight methods. In addition, the results on 11 cross-species datasets show that lncRNA_Mdeep is a powerful predictor for identifying lncRNAs. The source code can be downloaded from https://github.com/NWPU-903PR/lncRNA_Mdeep.

scholarly journals End-to-end deep learning framework for digital holographic reconstruction

2019 ◽  
Vol 1 (01) ◽  
pp. 1 ◽  
Author(s):  
Zhenbo Ren ◽  
Zhimin Xu ◽  
Edmund Y. Lam
Keyword(s):  
Deep Learning ◽  
Learning Framework ◽  
End To End ◽  
Holographic Reconstruction

A robust end-to-end deep learning framework for detecting Martian landforms with arbitrary orientations

2021 ◽  
pp. 107562
Author(s):  
Shancheng Jiang ◽  
Fan Wu ◽  
K.L. Yung ◽  
Yingqiao Yang ◽  
W.H. Ip ◽  
...  
Keyword(s):  
Deep Learning ◽  
Learning Framework ◽  
End To End

An End-to-End Multi-Task Deep Learning Framework for Skin Lesion Analysis

2020 ◽  
Vol 24 (10) ◽  
pp. 2912-2921
Author(s):  
Lei Song ◽  
Jianzhe Lin ◽  
Z. Jane Wang ◽  
Haoqian Wang
Keyword(s):  
Deep Learning ◽  
Skin Lesion ◽  
Learning Framework ◽  
Lesion Analysis ◽  
End To End
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