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lincRNA_predict specifications

Information


Unique identifier OMICS_25397
Name lincRNA_predict
Alternative name Auto-encoder
Software type Application/Script
Interface Command line interface
Restrictions to use None
Programming languages Python
Computer skills Advanced
Stability Stable
Requirements
BaseFinder,
Maintained Yes

Subtool


  • Auto-encoder

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Versioning


No version available

Maintainer


  • person_outline Ning Yu

Publication for lincRNA_predict

lincRNA_predict citations

 (76)
library_books

NiftyNet: a deep learning platform for medical imaging

2018
Comput Methods Programs Biomed
PMCID: 5869052
PMID: 29544777
DOI: 10.1016/j.cmpb.2018.01.025

[…] however, there is common structure and functionality among these applications supported by NiftyNet. NiftyNet currently supports •image segmentation,•image regression,•image model representation (via auto-encoder applications), and•image generation (via auto-encoder and generative adversarial networks (GANs)), and it is designed in a modular way to support the addition of new application types, by […]

library_books

Action and object words are differentially anchored in the sensory motor system A perspective on cognitive embodiment

2018
Sci Rep
PMCID: 5919964
PMID: 29700312
DOI: 10.1038/s41598-018-24475-z

[…] by introducing a novel statistical model for multi-task prediction based on brain activity maps. Semi-supervised factored logistic regression is an equally weighted composite model of an exploratory auto-encoder module and an inferential task prediction module by logistic regression (lambda = 0.5). The auto-encoder, a generalization of independent component analysis or principal component analysi […]

library_books

Moving Beyond ERP Components: A Selective Review of Approaches to Integrate EEG and Behavior

2018
Front Hum Neurosci
PMCID: 5879117
PMID: 29632480
DOI: 10.3389/fnhum.2018.00106

[…] cture, and the outputs of the kernel may be visualized as topographic feature maps (Figure ; Bashivan et al., ).A common approach for unsupervised training of ANNs which goes back to the 1980s is the auto-encoder (Bourlard and Kamp, ). Such a network consists of an encoder and a decoder. The encoder transforms input data into an internal representation whereas the decoder computes a reconstruction […]

library_books

Completing sparse and disconnected protein protein network by deep learning

2018
BMC Bioinformatics
PMCID: 5863833
PMID: 29566671
DOI: 10.1186/s12859-018-2112-7

[…] ovel method based on deep learning neural network and regularized Laplacian kernel to predict de novo interactions for sparse and disconnected PPI networks. We built the neural network with a typical auto-encoder structure to implicitly simulate the evolutionary processes of PPI networks. Based on the supervised learning using the rows of a sparse and disconnected training network as labels, we ca […]

library_books

A hybrid technique for speech segregation and classification using a sophisticated deep neural network

2018
PLoS One
PMCID: 5860734
PMID: 29558485
DOI: 10.1371/journal.pone.0194151

[…] les. There are two ways to train the data by using the dictionary method: the stack method, in which a stack of required layers is created using a deep Boltzmann machine (DBM) technique; or the stack auto-encoder method, which is used for dictionary training. To improve the performance of the classification, we proposed a dictionary-based fisher discrimination algorithm. explains the dictionary-b […]

library_books

Illusory Motion Reproduced by Deep Neural Networks Trained for Prediction

2018
Front Psychol
PMCID: 5863044
PMID: 29599739
DOI: 10.3389/fpsyg.2018.00345

[…] re adapted to the world that is progressing in real time (Nijhawan, ; Heeger, ).DNNs functioning in an unsupervised learning manner similar to the cerebral cortex have been gradually developed. Using auto-encoder networks or generative adversarial networks incorporating recurrent memory cells (long-short-term-memory, LSTM), it is becoming possible to predict the future state of an object in moving […]

Citations

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lincRNA_predict institution(s)
Department of Computing Sciences, The College at Brockport, State University of New York, Brockport, NY, USA; School of Information Science and Technology, Southwest Jiaotong University, Chengdu, China; Department of Computer Science, Georgia State University, Atlanta, GA, USA
lincRNA_predict funding source(s)
Supported by the College at Brockport, State University of New York.

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