AIMC Topic: Chromatin

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Weakly supervised learning for classification of lung cytological images using attention-based multiple instance learning.

Scientific reports Oct 13, 2021
In cytological examination, suspicious cells are evaluated regarding malignancy and cancer type. To assist this, we previously proposed an automated method based on supervised learning that classifies cells in lung cytological images as benign or mal...
Lung Neoplasms Adenocarcinoma Supervised Machine Learning Lung Deep Learning Image Processing, Computer-Assisted Thorax Small Cell Lung Carcinoma Chromatin Image Interpretation, Computer-Assisted Medical Informatics Pattern Recognition, Automated Humans Neural Networks, Computer Retrospective Studies Carcinoma, Squamous Cell Reproducibility of Results
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Chromatin interaction neural network (ChINN): a machine learning-based method for predicting chromatin interactions from DNA sequences.

Genome biology Aug 16, 2021
Chromatin interactions play important roles in regulating gene expression. However, the availability of genome-wide chromatin interaction data is limited. We develop a computational method, chromatin interaction neural network (ChINN), to predict chr...
Humans Neural Networks, Computer Chromatin Base Sequence Genome Leukemia Machine Learning Computational Biology
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Predicting pathogenic non-coding SVs disrupting the 3D genome in 1646 whole cancer genomes using multiple instance learning.

Scientific reports Jul 13, 2021
Over the past years, large consortia have been established to fuel the sequencing of whole genomes of many cancer patients. Despite the increased abundance in tools to study the impact of SNVs, non-coding SVs have been largely ignored in these data. ...
Humans Machine Learning Genomic Structural Variation Neoplasms Genome, Human Chromatin
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Deep learning connects DNA traces to transcription to reveal predictive features beyond enhancer-promoter contact.

Nature communications Jun 8, 2021
Chromatin architecture plays an important role in gene regulation. Recent advances in super-resolution microscopy have made it possible to measure chromatin 3D structure and transcription in thousands of single cells. However, leveraging these comple...
Gene Silencing Enhancer Elements, Genetic Genome, Insect Chromatin DNA Multigene Family Gene Expression Regulation Promoter Regions, Genetic Drosophila melanogaster Transcription, Genetic Computer Simulation Animals Deep Learning Neural Networks, Computer Algorithms
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Machine learning for profile prediction in genomics.

Current opinion in chemical biology Jun 6, 2021
A recent deluge of publicly available multi-omics data has fueled the development of machine learning methods aimed at investigating important questions in genomics. Although the motivations for these methods vary, a task that is commonly adopted is ...
Machine Learning Protein Binding Genomics Genome Chromatin
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Schema: metric learning enables interpretable synthesis of heterogeneous single-cell modalities.

Genome biology May 3, 2021
A complete understanding of biological processes requires synthesizing information across heterogeneous modalities, such as age, disease status, or gene expression. Technological advances in single-cell profiling have enabled researchers to assay mul...
Machine Learning Gene Expression Profiling Chromatin Assembly and Disassembly Gene Expression Regulation Organ Specificity Chromatin Transcriptome Single-Cell Analysis Computational Biology
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Improving axial resolution in Structured Illumination Microscopy using deep learning.

Philosophical transactions. Series A, Mathematical, physical, and engineering sciences Apr 26, 2021
Structured Illumination Microscopy (SIM) is a widespread methodology to image live and fixed biological structures smaller than the diffraction limits of conventional optical microscopy. Using recent advances in image up-scaling through deep learning...
Chromatin Deep Learning Imaging, Three-Dimensional Optical Phenomena Image Processing, Computer-Assisted Microscopy, Confocal Microscopy, Fluorescence Humans Computer Simulation Animals
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Interpretation of allele-specific chromatin accessibility using cell state-aware deep learning.

Genome research Apr 8, 2021
Genomic sequence variation within enhancers and promoters can have a significant impact on the cellular state and phenotype. However, sifting through the millions of candidate variants in a personal genome or a cancer genome, to identify those that i...
Deep Learning Alleles Promoter Regions, Genetic Chromatin Artificial Intelligence
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Deep learning-based enhancement of epigenomics data with AtacWorks.

Nature communications Mar 8, 2021
ATAC-seq is a widely-applied assay used to measure genome-wide chromatin accessibility; however, its ability to detect active regulatory regions can depend on the depth of sequencing coverage and the signal-to-noise ratio. Here we introduce AtacWorks...
Regulatory Sequences, Nucleic Acid Chromatin Chromatin Immunoprecipitation Sequencing Epigenomics Leukocytes Humans Brain Animals Deep Learning Mice
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DeepCAPE: A Deep Convolutional Neural Network for the Accurate Prediction of Enhancers.

Genomics, proteomics & bioinformatics Feb 11, 2021
The establishment of a landscape of enhancers across human cells is crucial to deciphering the mechanism of gene regulation, cell differentiation, and disease development. High-throughput experimental approaches, which contain successfully reported e...
Chromatin Software Sequence Analysis, DNA Enhancer Elements, Genetic Humans Neural Networks, Computer
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