GraphProt: modeling binding preferences of RNA-binding proteins.
about
Specificity and nonspecificity in RNA-protein interactionsHigh-throughput characterization of protein-RNA interactionsAffinity regression predicts the recognition code of nucleic acid–binding proteinsIntegrating Epigenomics into the Understanding of Biomedical InsightA deep learning framework for modeling structural features of RNA-binding protein targets.Computational Methods for CLIP-seq Data Processing.Analysis of sequencing data for probing RNA secondary structures and protein-RNA binding in studying posttranscriptional regulations.RCK: accurate and efficient inference of sequence- and structure-based protein-RNA binding models from RNAcompete data.Bioinformatic tools for analysis of CLIP ribonucleoprotein dataLineage-specific splicing of a brain-enriched alternative exon promotes glioblastoma progression.PTRcombiner: mining combinatorial regulation of gene expression from post-transcriptional interaction mapsLocus-specific targeting to the X chromosome revealed by the RNA interactome of CTCF.Computational challenges, tools, and resources for analyzing co- and post-transcriptional events in high throughput.Design and bioinformatics analysis of genome-wide CLIP experiments.Co-evolution of Bacterial Ribosomal Protein S15 with Diverse mRNA Regulatory Structures.From benchmarking HITS-CLIP peak detection programs to a new method for identification of miRNA-binding sites from Ago2-CLIP dataRNA-protein binding motifs mining with a new hybrid deep learning based cross-domain knowledge integration approachA deep boosting based approach for capturing the sequence binding preferences of RNA-binding proteins from high-throughput CLIP-seq data.Recognizing RNA structural motifs in HT-SELEX data for ribosomal protein S15.RNASeqMetaDB: a database and web server for navigating metadata of publicly available mouse RNA-Seq datasetsRBP-Var: a database of functional variants involved in regulation mediated by RNA-binding proteins.Modeling the combined effect of RNA-binding proteins and microRNAs in post-transcriptional regulationThe lncRNA landscape of breast cancer reveals a role for DSCAM-AS1 in breast cancer progressionMechanism of β-actin mRNA Recognition by ZBP1Biological and bioinformatical approaches to study crosstalk of long-non-coding RNAs and chromatin-modifying proteins.CLIP: viewing the RNA world from an RNA-protein interactome perspective.Revealing protein-lncRNA interactionAssociating transcription factors and conserved RNA structures with gene regulation in the human brain.RAIN: RNA-protein Association and Interaction Networks.A novel method for the identification of conserved structural patterns in RNA: From small scale to high-throughput applications.RNAcommender: genome-wide recommendation of RNA-protein interactions.Orthogonal matrix factorization enables integrative analysis of multiple RNA binding proteins.PredcircRNA: computational classification of circular RNA from other long non-coding RNA using hybrid features.Identification of high-confidence RNA regulatory elements by combinatorial classification of RNA-protein binding sites.The RBPome: where the brains meet the brawn.'Oming in on RNA-protein interactions.ssHMM: extracting intuitive sequence-structure motifs from high-throughput RNA-binding protein data.Towards enhanced and interpretable clustering/classification in integrative genomics.DynaMIT: the dynamic motif integration toolkit.Discriminating cirRNAs from other lncRNAs using a hierarchical extreme learning machine (H-ELM) algorithm with feature selection.
P2860
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P2860
GraphProt: modeling binding preferences of RNA-binding proteins.
description
2014 nî lūn-bûn
@nan
2014 թուականի Յունուարին հրատարակուած գիտական յօդուած
@hyw
2014 թվականի հունվարին հրատարակված գիտական հոդված
@hy
2014年の論文
@ja
2014年論文
@yue
2014年論文
@zh-hant
2014年論文
@zh-hk
2014年論文
@zh-mo
2014年論文
@zh-tw
2014年论文
@wuu
name
GraphProt: modeling binding preferences of RNA-binding proteins.
@ast
GraphProt: modeling binding preferences of RNA-binding proteins.
@en
type
label
GraphProt: modeling binding preferences of RNA-binding proteins.
@ast
GraphProt: modeling binding preferences of RNA-binding proteins.
@en
prefLabel
GraphProt: modeling binding preferences of RNA-binding proteins.
@ast
GraphProt: modeling binding preferences of RNA-binding proteins.
@en
P2860
P356
P1433
P1476
GraphProt: modeling binding preferences of RNA-binding proteins
@en
P2093
Daniel Maticzka
Sita J Lange
P2860
P2888
P356
10.1186/GB-2014-15-1-R17
P577
2014-01-22T00:00:00Z
P5875
P6179
1031344865