AIMC Topic: Epigenomics

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Integrating distal and proximal information to predict gene expression via a densely connected convolutional neural network.

Bioinformatics (Oxford, England) Jan 15, 2020
MOTIVATION: Interactions among cis-regulatory elements such as enhancers and promoters are main driving forces shaping context-specific chromatin structure and gene expression. Although there have been computational methods for predicting gene expres...
Neural Networks, Computer Genomics Enhancer Elements, Genetic Regulatory Sequences, Nucleic Acid Promoter Regions, Genetic Epigenomics
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Quantitative Modelling of the Waddington Epigenetic Landscape.

Methods in molecular biology (Clifton, N.J.) Jan 1, 2019
C.H. Waddington introduced the epigenetic landscape as a metaphor to represent cellular decision-making during development. Like a population of balls rolling down a rough hillside, developing cells follow specific trajectories (valleys) and eventual...
Humans Cell Lineage Models, Genetic Phenotype Epigenomics Neural Networks, Computer Cell Differentiation Stem Cells Gene Regulatory Networks
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Machine Learning Methods in Precision Medicine Targeting Epigenetic Diseases.

Current pharmaceutical design Jan 1, 2018
BACKGROUND: On a tide of big data, machine learning is coming to its day. Referring to huge amounts of epigenetic data coming from biological experiments and clinic, machine learning can help in detecting epigenetic features in genome, finding correl...
Humans Machine Learning Disease Epigenomics Precision Medicine
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Epigenomic annotation-based interpretation of genomic data: from enrichment analysis to machine learning.

Bioinformatics (Oxford, England) Oct 15, 2017
MOTIVATION: One of the goals of functional genomics is to understand the regulatory implications of experimentally obtained genomic regions of interest (ROIs). Most sequencing technologies now generate ROIs distributed across the whole genome. The in...
Molecular Sequence Annotation Software Machine Learning Guidelines as Topic Epigenomics Humans
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Denoising genome-wide histone ChIP-seq with convolutional neural networks.

Bioinformatics (Oxford, England) Jul 15, 2017
MOTIVATION: Chromatin immune-precipitation sequencing (ChIP-seq) experiments are commonly used to obtain genome-wide profiles of histone modifications associated with different types of functional genomic elements. However, the quality of histone ChI...
Histone Code Chromatin Immunoprecipitation Neural Networks, Computer Sequence Analysis, DNA Humans Epigenomics Animals Software High-Throughput Nucleotide Sequencing Mice Cell Line
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Identifying RNA 5-methylcytosine sites via pseudo nucleotide compositions.

Molecular bioSystems Oct 18, 2016
RNA 5-methylcytosine (mC) plays an important role in numerous biological processes. Accurate identification of the mC site is helpful for a better understanding of its biological functions. However, the drawbacks of the experimental methods available...
Humans Computational Biology Reproducibility of Results Epigenomics RNA 5-Methylcytosine Algorithms Support Vector Machine Nucleic Acid Conformation Methylation
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Higher order methylation features for clustering and prediction in epigenomic studies.

Bioinformatics (Oxford, England) Sep 1, 2016
MOTIVATION: DNA methylation is an intensely studied epigenetic mark, yet its functional role is incompletely understood. Attempts to quantitatively associate average DNA methylation to gene expression yield poor correlations outside of the well-under...
Genome DNA Methylation Promoter Regions, Genetic Forecasting CpG Islands Machine Learning Epigenomics Gene Expression
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