Discover regulatory DNA elements using chromatin signatures and artificial neural network.
about
EP-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 dataDiscover context-specific combinatorial transcription factor interactions by integrating diverse ChIP-Seq data setsGlobal view of enhancer-promoter interactome in human cells.Taking promoters out of enhancers in sequence based predictions of tissue-specific mammalian enhancersChIP-chip versus ChIP-seq: lessons for experimental design and data analysis.Genome-wide enhancer prediction from epigenetic signatures using genetic algorithm-optimized support vector machines.Distinct and predictive histone lysine acetylation patterns at promoters, enhancers, and gene bodies.Identification of osteoarthritis biomarkers by proteomic analysis of synovial fluid.RFECS: a random-forest based algorithm for enhancer identification from chromatin state.Computational identification of active enhancers in model organisms.Enhancers in embryonic stem cells are enriched for transposable elements and genetic variations associated with cancers.DELTA: A Distal Enhancer Locating Tool Based on AdaBoost Algorithm and Shape Features of Chromatin Modifications.A microfluidic device for epigenomic profiling using 100 cells.LMethyR-SVM: Predict Human Enhancers Using Low Methylated Regions based on Weighted Support Vector Machines.ChARM: Discovery of combinatorial chromatin modification patterns in hepatitis B virus X-transformed mouse liver cancer using association rule miningIdentification of recurrent combinatorial patterns of chromatin modifications at promoters across various tissue types.PEDLA: predicting enhancers with a deep learning-based algorithmic framework.eRFSVM: a hybrid classifier to predict enhancers-integrating random forests with support vector machines.Improved regulatory element prediction based on tissue-specific local epigenomic signatures.Computational schemes for the prediction and annotation of enhancers from epigenomic assays.Progress and challenges in bioinformatics approaches for enhancer identification.Peak-valley-peak pattern of histone modifications delineates active regulatory elements and their directionalityBiRen: predicting enhancers with a deep-learning-based model using the DNA sequence alone.EMERGE: a flexible modelling framework to predict genomic regulatory elements from genomic signatures.Enhancer identification in mouse embryonic stem cells using integrative modeling of chromatin and genomic features.CD8+ T Cells Utilize Highly Dynamic Enhancer Repertoires and Regulatory Circuitry in Response to Infections.iEnhancer-2L: a two-layer predictor for identifying enhancers and their strength by pseudo k-tuple nucleotide composition.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 genesDENdb: database of integrated human enhancers.Chromatin signature discovery via histone modification profile alignments.EnhancerPred: a predictor for discovering enhancers based on the combination and selection of multiple features.A new method for enhancer prediction based on deep belief network.DEEP: a general computational framework for predicting enhancers.Computational exploration of cis-regulatory modules in rhythmic expression data using the "Exploration of Distinctive CREs and CRMs" (EDCC) and "CRM Network Generator" (CNG) programs.EnhancerPred2.0: predicting enhancers and their strength based on position-specific trinucleotide propensity and electron-ion interaction potential feature selection.Identifying noncoding risk variants using disease-relevant gene regulatory networks.Low-input and multiplexed microfluidic assay reveals epigenomic variation across cerebellum and prefrontal cortex.Towards a map of cis-regulatory sequences in the human genome.
P2860
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P2860
Discover regulatory DNA elements using chromatin signatures and artificial neural network.
description
2010 nî lūn-bûn
@nan
2010 թուականի Մայիսին հրատարակուած գիտական յօդուած
@hyw
2010 թվականի մայիսին հրատարակված գիտական հոդված
@hy
2010年の論文
@ja
2010年論文
@yue
2010年論文
@zh-hant
2010年論文
@zh-hk
2010年論文
@zh-mo
2010年論文
@zh-tw
2010年论文
@wuu
name
Discover regulatory DNA elemen ...... and artificial neural network.
@ast
Discover regulatory DNA elemen ...... and artificial neural network.
@en
type
label
Discover regulatory DNA elemen ...... and artificial neural network.
@ast
Discover regulatory DNA elemen ...... and artificial neural network.
@en
prefLabel
Discover regulatory DNA elemen ...... and artificial neural network.
@ast
Discover regulatory DNA elemen ...... and artificial neural network.
@en
P2860
P356
P1433
P1476
Discover regulatory DNA elements using chromatin signatures and artificial neural network
@en
P2093
Duygu Ucar
Hiram A Firpi
P2860
P304
P356
10.1093/BIOINFORMATICS/BTQ248
P407
P50
P577
2010-05-07T00:00:00Z