PEDLA: predicting enhancers with a deep learning-based algorithmic framework.
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Improved regulatory element prediction based on tissue-specific local epigenomic signatures.BiRen: predicting enhancers with a deep-learning-based model using the DNA sequence alone.Regulatory elements in molecular networks.Deep learning of the regulatory grammar of yeast 5' untranslated regions from 500,000 random sequences.Deep Learning based multi-omics integration robustly predicts survival in liver cancer.Functional Variants Identified Efficiently through an Integrated Transcriptome and Epigenome Analysis.Enhancing Hi-C data resolution with deep convolutional neural network HiCPlus.Deep Learning and its Applications in Biomedicine.Deep learning in pharmacogenomics: from gene regulation to patient stratification.Genome-wide prediction of cis-regulatory regions using supervised deep learning methods.A neural network based model effectively predicts enhancers from clinical ATAC-seq samplesSequence based prediction of enhancer regions from DNA random walk
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PEDLA: predicting enhancers with a deep learning-based algorithmic framework.
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article científic
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article scientifique
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articolo scientifico
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artigo científico
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bilimsel makale
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scientific article published on 22 June 2016
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vedecký článok
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vetenskaplig artikel
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videnskabelig artikel
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vědecký článek
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name
PEDLA: predicting enhancers with a deep learning-based algorithmic framework.
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PEDLA: predicting enhancers with a deep learning-based algorithmic framework.
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type
label
PEDLA: predicting enhancers with a deep learning-based algorithmic framework.
@en
PEDLA: predicting enhancers with a deep learning-based algorithmic framework.
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prefLabel
PEDLA: predicting enhancers with a deep learning-based algorithmic framework.
@en
PEDLA: predicting enhancers with a deep learning-based algorithmic framework.
@nl
P2093
P2860
P356
P1433
P1476
PEDLA: predicting enhancers with a deep learning-based algorithmic framework
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P2093
P2860
P2888
P356
10.1038/SREP28517
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P50
P577
2016-06-22T00:00:00Z