AIMC Topic: Epigenomics

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SVFX: a machine learning framework to quantify the pathogenicity of structural variants.

Genome biology Nov 9, 2020
There is a lack of approaches for identifying pathogenic genomic structural variants (SVs) although they play a crucial role in many diseases. We present a mechanism-agnostic machine learning-based workflow, called SVFX, to assign pathogenicity score...
Epigenomics Virulence Genome, Human Oncogenes Genomic Structural Variation Humans Machine Learning Genomics
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Deep learning approach for predicting functional Z-DNA regions using omics data.

Scientific reports Nov 5, 2020
Computational methods to predict Z-DNA regions are in high demand to understand the functional role of Z-DNA. The previous state-of-the-art method Z-Hunt is based on statistical mechanical and energy considerations about B- to Z-DNA transition using ...
Epigenomics Databases, Genetic Gene Expression Regulation Genome Genomics Computational Biology Epigenesis, Genetic DNA, Z-Form Humans Deep Learning
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Single-Cell Techniques and Deep Learning in Predicting Drug Response.

Trends in pharmacological sciences Nov 2, 2020
Rapidly developing single-cell sequencing analyses produce more comprehensive profiles of the genomic, transcriptomic, and epigenomic heterogeneity of tumor subpopulations than do traditional bulk sequencing analyses. Moreover, single-cell techniques...
Computational Biology Epigenomics Pharmaceutical Preparations Pharmacology Deep Learning Genomics
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Computational methods and next-generation sequencing approaches to analyze epigenetics data: Profiling of methods and applications.

Methods (San Diego, Calif.) Sep 14, 2020
Epigenetics is mainly comprised of features that regulate genomic interactions thereby playing a crucial role in a vast array of biological processes. Epigenetic mechanisms such as DNA methylation and histone modifications influence gene expression b...
Data Analysis Animals Mice Epigenesis, Genetic Humans Machine Learning Histone Code Epigenomics DNA Methylation High-Throughput Nucleotide Sequencing
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Integrating multi-omics data by learning modality invariant representations for improved prediction of overall survival of cancer.

Methods (San Diego, Calif.) Aug 5, 2020
Breast and ovarian cancers are the second and the fifth leading causes of cancer death among women. Predicting the overall survival of breast and ovarian cancer patients can facilitate the therapeutics evaluation and treatment decision making. Multi-...
Gene Expression Regulation, Neoplastic Epigenomics DNA Methylation DNA Copy Number Variations Breast Neoplasms Genomics Computational Biology Ovarian Neoplasms Humans Neural Networks, Computer Machine Learning Female
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Cross-species regulatory sequence activity prediction.

PLoS computational biology Jul 20, 2020
Machine learning algorithms trained to predict the regulatory activity of nucleic acid sequences have revealed principles of gene regulation and guided genetic variation analysis. While the human genome has been extensively annotated and studied, mod...
Genome, Human Species Specificity Quantitative Trait Loci Mutation Hepatocytes Genetic Variation Gene Expression Regulation Databases, Genetic Sequence Analysis, DNA Models, Statistical Algorithms Animals Mice Genomics Computational Biology Epigenomics Models, Genetic Humans Neural Networks, Computer Machine Learning Software
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CNN-Peaks: ChIP-Seq peak detection pipeline using convolutional neural networks that imitate human visual inspection.

Scientific reports May 13, 2020
ChIP-seq is one of the core experimental resources available to understand genome-wide epigenetic interactions and identify the functional elements associated with diseases. The analysis of ChIP-seq data is important but poses a difficult computation...
Protein Binding Computational Biology Epigenesis, Genetic Epigenomics Histones Binding Sites Nucleotide Motifs Humans Neural Networks, Computer Software Algorithms Databases, Nucleic Acid Transcription Initiation Site Chromatin Immunoprecipitation Sequencing
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Machine learning and clinical epigenetics: a review of challenges for diagnosis and classification.

Clinical epigenetics Apr 3, 2020
BACKGROUND: Machine learning is a sub-field of artificial intelligence, which utilises large data sets to make predictions for future events. Although most algorithms used in machine learning were developed as far back as the 1950s, the advent of big...
Supervised Machine Learning Disease Diagnosis Epigenesis, Genetic Epigenomics DNA Methylation Unsupervised Machine Learning Humans Machine Learning Precision Medicine
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Estimating gene expression from DNA methylation and copy number variation: A deep learning regression model for multi-omics integration.

Genomics Mar 29, 2020
Gene expression analysis plays a significant role for providing molecular insights in cancer. Various genetic and epigenetic factors (being dealt under multi-omics) affect gene expression giving rise to cancer phenotypes. A recent growth in understan...
Epigenomics DNA Methylation DNA Copy Number Variations Linear Models Carcinoma, Hepatocellular RNA-Seq Genomics Transcriptome Deep Learning Liver Neoplasms
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Deep learning models predict regulatory variants in pancreatic islets and refine type 2 diabetes association signals.

eLife Jan 27, 2020
Genome-wide association analyses have uncovered multiple genomic regions associated with T2D, but identification of the causal variants at these remains a challenge. There is growing interest in the potential of deep learning models - which predict e...
Signal Transduction Models, Theoretical Chromatin Islets of Langerhans Humans Deep Learning Genome-Wide Association Study Diabetes Mellitus, Type 2 Genetic Predisposition to Disease Polymorphism, Single Nucleotide Epigenomics
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