Predicting the sequence specificities of DNA- and RNA-binding proteins by deep learning.
about
Novel bioinformatic developments for exome sequencingDeep learning for computational biology.Deep Artificial Neural Networks and Neuromorphic Chips for Big Data Analysis: Pharmaceutical and Bioinformatics Applications.Analysis of Genomic Sequence Motifs for Deciphering Transcription Factor Binding and Transcriptional Regulation in Eukaryotic CellsGene expression prediction using low-rank matrix completion.Discrimination of Breast Cancer with Microcalcifications on Mammography by Deep LearningRecent Progress in Machine Learning-Based Methods for Protein Fold RecognitionMRPrimerV: a database of PCR primers for RNA virus detectionDeepChrome: deep-learning for predicting gene expression from histone modificationsPOSTAR: a platform for exploring post-transcriptional regulation coordinated by RNA-binding proteinsNeuroblastoma, a Paradigm for Big Data Science in Pediatric Oncology.DEEP MOTIF DASHBOARD: VISUALIZING AND UNDERSTANDING GENOMIC SEQUENCES USING DEEP NEURAL NETWORKS.Detecting and correcting the binding-affinity bias in ChIP-seq data using inter-species information.Sequence-based prediction of protein protein interaction using a deep-learning algorithm.DeepCpG: accurate prediction of single-cell DNA methylation states using deep learningIPMiner: hidden ncRNA-protein interaction sequential pattern mining with stacked autoencoder for accurate computational predictionDeep Neural Networks Based Recognition of Plant Diseases by Leaf Image Classification.Deep learning for computational chemistry.3D deep convolutional neural networks for amino acid environment similarity analysisIdentification and function annotation of long intervening noncoding RNAs.RCK: accurate and efficient inference of sequence- and structure-based protein-RNA binding models from RNAcompete data.Sequence-specific bias correction for RNA-seq data using recurrent neural networks.Quantifying deleterious effects of regulatory variants.High-Resolution RNA Maps Suggest Common Principles of Splicing and Polyadenylation Regulation by TDP-43.WSMD: weakly-supervised motif discovery in transcription factor ChIP-seq data.Predicting the impact of non-coding variants on DNA methylation.High-resolution interrogation of functional elements in the noncoding genomeWhich Genetics Variants in DNase-Seq Footprints Are More Likely to Alter Binding?Deep Learning for Population Genetic Inference.Forecast Modelling via Variations in Binary Image-Encoded Information Exploited by Deep Learning Neural Networks.Template-Based Modeling of Protein-RNA InteractionsPleiotropic Mechanisms Indicated for Sex Differences in AutismModel-guided combinatorial optimization of complex synthetic gene networks.Recognition of prokaryotic and eukaryotic promoters using convolutional deep learning neural networksImputation for transcription factor binding predictions based on deep learningRNA-protein binding motifs mining with a new hybrid deep learning based cross-domain knowledge integration approachCombining phylogenetic footprinting with motif models incorporating intra-motif dependencies.Fast, scalable prediction of deleterious noncoding variants from functional and population genomic data.An algorithmic perspective of de novo cis-regulatory motif finding based on ChIP-seq data.ML2Motif-Reliable extraction of discriminative sequence motifs from learning machines.
P2860
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P2860
Predicting the sequence specificities of DNA- and RNA-binding proteins by deep learning.
description
2015 nî lūn-bûn
@nan
2015 թուականի Յուլիսին հրատարակուած գիտական յօդուած
@hyw
2015 թվականի հուլիսին հրատարակված գիտական հոդված
@hy
2015年の論文
@ja
2015年論文
@yue
2015年論文
@zh-hant
2015年論文
@zh-hk
2015年論文
@zh-mo
2015年論文
@zh-tw
2015年论文
@wuu
name
Predicting the sequence specificities of DNA- and RNA-binding proteins by deep learning.
@ast
Predicting the sequence specificities of DNA- and RNA-binding proteins by deep learning.
@en
Predicting the sequence specificities of DNA- and RNA-binding proteins by deep learning.
@nl
type
label
Predicting the sequence specificities of DNA- and RNA-binding proteins by deep learning.
@ast
Predicting the sequence specificities of DNA- and RNA-binding proteins by deep learning.
@en
Predicting the sequence specificities of DNA- and RNA-binding proteins by deep learning.
@nl
prefLabel
Predicting the sequence specificities of DNA- and RNA-binding proteins by deep learning.
@ast
Predicting the sequence specificities of DNA- and RNA-binding proteins by deep learning.
@en
Predicting the sequence specificities of DNA- and RNA-binding proteins by deep learning.
@nl
P2093
P2860
P356
P1433
P1476
Predicting the sequence specificities of DNA- and RNA-binding proteins by deep learning.
@en
P2093
Andrew Delong
Brendan J Frey
Matthew T Weirauch
P2860
P2888
P304
P356
10.1038/NBT.3300
P577
2015-07-27T00:00:00Z