AIMC Topic: Chromatin

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DeepGRN: prediction of transcription factor binding site across cell-types using attention-based deep neural networks.

BMC bioinformatics Feb 1, 2021
BACKGROUND: Due to the complexity of the biological systems, the prediction of the potential DNA binding sites for transcription factors remains a difficult problem in computational biology. Genomic DNA sequences and experimental results from paralle...
Transcription Factors Binding Sites Chromatin Neural Networks, Computer Protein Binding Computational Biology
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An interpretable bimodal neural network characterizes the sequence and preexisting chromatin predictors of induced transcription factor binding.

Genome biology Jan 7, 2021
BACKGROUND: Transcription factor (TF) binding specificity is determined via a complex interplay between the transcription factor's DNA binding preference and cell type-specific chromatin environments. The chromatin features that correlate with transc...
Binding Sites Genome Chromatin DNA-Binding Proteins Gene Expression Regulation Histones Humans Neural Networks, Computer Protein Binding Transcription Factors Basic Helix-Loop-Helix Transcription Factors
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A pitfall for machine learning methods aiming to predict across cell types.

Genome biology Nov 19, 2020
Machine learning models that predict genomic activity are most useful when they make accurate predictions across cell types. Here, we show that when the training and test sets contain the same genomic loci, the resulting model may falsely appear to p...
Epigenomics Gene Expression Chromatin Machine Learning Genomics Humans
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Cancer classification based on chromatin accessibility profiles with deep adversarial learning model.

PLoS computational biology Nov 9, 2020
Given the complexity and diversity of the cancer genomics profiles, it is challenging to identify distinct clusters from different cancer types. Numerous analyses have been conducted for this propose. Still, the methods they used always do not direct...
RNA-Seq Genomics Computational Biology Humans Neoplasms Deep Learning Chromatin Immunoprecipitation Sequencing Databases, Nucleic Acid Chromatin Multigene Family Oncogenes Normal Distribution
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DeepC: predicting 3D genome folding using megabase-scale transfer learning.

Nature methods Oct 12, 2020
Predicting the impact of noncoding genetic variation requires interpreting it in the context of three-dimensional genome architecture. We have developed deepC, a transfer-learning-based deep neural network that accurately predicts genome folding from...
Base Sequence Genome, Human Chromatin CCCTC-Binding Factor Genomic Structural Variation Computer Simulation Genomics Models, Genetic Humans Neural Networks, Computer
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Mustache: multi-scale detection of chromatin loops from Hi-C and Micro-C maps using scale-space representation.

Genome biology Sep 30, 2020
We present MUSTACHE, a new method for multi-scale detection of chromatin loops from Hi-C and Micro-C contact maps. MUSTACHE employs scale-space theory, a technical advance in computer vision, to detect blob-shaped objects in contact maps. MUSTACHE is...
Genomics Nucleic Acid Conformation Chromatin Artificial Intelligence Software
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Characterizing chromatin folding coordinate and landscape with deep learning.

PLoS computational biology Sep 28, 2020
Genome organization is critical for setting up the spatial environment of gene transcription, and substantial progress has been made towards its high-resolution characterization. The underlying molecular mechanism for its establishment is much less u...
Computer Simulation Deep Learning Models, Biological Protein Conformation Chromatin Thermodynamics Histone Code Genome
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Deep learning of immune cell differentiation.

Proceedings of the National Academy of Sciences of the United States of America Sep 25, 2020
Although we know many sequence-specific transcription factors (TFs), how the DNA sequence of cis-regulatory elements is decoded and orchestrated on the genome scale to determine immune cell differentiation is beyond our grasp. Leveraging a granular a...
Transcription Factors Databases, Genetic Gene Expression Regulation Hematopoiesis Regulatory Sequences, Nucleic Acid Chromatin Animals Humans Deep Learning Mice
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A Comparative Study of Supervised Machine Learning Algorithms for the Prediction of Long-Range Chromatin Interactions.

Genes Aug 24, 2020
The role of three-dimensional genome organization as a critical regulator of gene expression has become increasingly clear over the last decade. Most of our understanding of this association comes from the study of long range chromatin interaction ma...
Humans Algorithms Computer Simulation K562 Cells Support Vector Machine Gene Expression Regulation, Leukemic Supervised Machine Learning Chromatin Genome, Human
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Cell type prioritization in single-cell data.

Nature biotechnology Jul 20, 2020
We present Augur, a method to prioritize the cell types most responsive to biological perturbations in single-cell data. Augur employs a machine-learning framework to quantify the separability of perturbed and unperturbed cells within a high-dimensio...
Nerve Net Mice Chromatin Machine Learning Gene Expression Profiling Rats Computational Biology Animals Transcriptome Databases, Genetic Sequence Analysis, RNA Single-Cell Analysis Walking
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