Prediction of regulatory elements in mammalian genomes using chromatin signatures.
about
Discovery and characterization of chromatin states for systematic annotation of the human genomeEP-DNN: A Deep Neural Network-Based Global Enhancer Prediction AlgorithmOpening up the blackbox: an interpretable deep neural network-based classifier for cell-type specific enhancer predictions.Comparative annotation of functional regions in the human genome using epigenomic dataAn integrated approach to identifying cis-regulatory modules in the human genome.Computational study of associations between histone modification and protein-DNA binding in yeast genome by integrating diverse information.Discover regulatory DNA elements using chromatin signatures and artificial neural network.Successful computational prediction of novel imprinted genes from epigenomic features.Epigenome mapping in normal and disease States.Three-dimensional modeling of chromatin structure from interaction frequency data using Markov chain Monte Carlo sampling.Prediction of RNA Polymerase II recruitment, elongation and stalling from histone modification data.Clustered ChIP-Seq-defined transcription factor binding sites and histone modifications map distinct classes of regulatory elements.Genome-wide enhancer prediction from epigenetic signatures using genetic algorithm-optimized support vector machines.Computational Epigenetics: the new scientific paradigm.Role of canonical Wnt signaling/ß-catenin via Dermo1 in cranial dermal cell development.Finding combinatorial histone code by semi-supervised biclusteringDistinct and predictive histone lysine acetylation patterns at promoters, enhancers, and gene bodies.RFECS: a random-forest based algorithm for enhancer identification from chromatin state.Ligand-dependent dynamics of retinoic acid receptor binding during early neurogenesisFinding associations among histone modifications using sparse partial correlation networks.Combinatorial chromatin modification patterns in the human genome revealed by subspace clustering.Computational identification of active enhancers in model organisms.Enhancers in embryonic stem cells are enriched for transposable elements and genetic variations associated with cancers.Proteogenomics analysis reveals specific genomic orientations of distal regulatory regions composed by non-canonical histone variants.A stationary wavelet entropy-based clustering approach accurately predicts gene expressionDELTA: A Distal Enhancer Locating Tool Based on AdaBoost Algorithm and Shape Features of Chromatin Modifications.ChARM: Discovery of combinatorial chromatin modification patterns in hepatitis B virus X-transformed mouse liver cancer using association rule miningGlobal identification of transcriptional regulators of pluripotency and differentiation in embryonic stem cells.PEDLA: predicting enhancers with a deep learning-based algorithmic framework.Long non-coding RNA identification over mouse brain development by integrative modeling of chromatin and genomic featuresPredictive chromatin signatures in the mammalian genome.Improved regulatory element prediction based on tissue-specific local epigenomic signatures.A wavelet-based method to exploit epigenomic language in the regulatory region.Epigenetics and gene expression.Control of nuclear receptor function by local chromatin structure.CIPHER: a flexible and extensive workflow platform for integrative next-generation sequencing data analysis and genomic regulatory element prediction.Progress and challenges in bioinformatics approaches for enhancer identification.EMERGE: a flexible modelling framework to predict genomic regulatory elements from genomic signatures.Genome-wide prediction of transcription factor binding sites using an integrated model.EMdeCODE: a novel algorithm capable of reading words of epigenetic code to predict enhancers and retroviral integration sites and to identify H3R2me1 as a distinctive mark of coding versus non-coding genes
P2860
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P2860
Prediction of regulatory elements in mammalian genomes using chromatin signatures.
description
2008 nî lūn-bûn
@nan
2008 թուականի Դեկտեմբերին հրատարակուած գիտական յօդուած
@hyw
2008 թվականի դեկտեմբերին հրատարակված գիտական հոդված
@hy
2008年の論文
@ja
2008年論文
@yue
2008年論文
@zh-hant
2008年論文
@zh-hk
2008年論文
@zh-mo
2008年論文
@zh-tw
2008年论文
@wuu
name
Prediction of regulatory elements in mammalian genomes using chromatin signatures.
@ast
Prediction of regulatory elements in mammalian genomes using chromatin signatures.
@en
type
label
Prediction of regulatory elements in mammalian genomes using chromatin signatures.
@ast
Prediction of regulatory elements in mammalian genomes using chromatin signatures.
@en
prefLabel
Prediction of regulatory elements in mammalian genomes using chromatin signatures.
@ast
Prediction of regulatory elements in mammalian genomes using chromatin signatures.
@en
P2093
P2860
P356
P1433
P1476
Prediction of regulatory elements in mammalian genomes using chromatin signatures
@en
P2093
Iouri Chepelev
P2860
P2888
P356
10.1186/1471-2105-9-547
P577
2008-12-18T00:00:00Z
P5875
P6179
1018796059