AIMC Topic: Chromatin

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DeepCAGE: Incorporating Transcription Factors in Genome-wide Prediction of Chromatin Accessibility.

Genomics, proteomics & bioinformatics Mar 12, 2022
Although computational approaches have been complementing high-throughput biological experiments for the identification of functional regions in the human genome, it remains a great challenge to systematically decipher interactions between transcript...
Humans Deep Learning Binding Sites Regulatory Sequences, Nucleic Acid Chromatin Chromatin Assembly and Disassembly Protein Binding Transcription Factors Genome, Human
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Deep learning and alignment of spatially resolved single-cell transcriptomes with Tangram.

Nature methods Oct 28, 2021
Charting an organs' biological atlas requires us to spatially resolve the entire single-cell transcriptome, and to relate such cellular features to the anatomical scale. Single-cell and single-nucleus RNA-seq (sc/snRNA-seq) can profile cells comprehe...
Transcriptome Deep Learning Single-Cell Analysis Animals Male Gene Expression Profiling Gene Expression Regulation Regulatory Sequences, Nucleic Acid Chromatin RNA-Seq Mice Mice, Inbred C57BL Software Brain Female
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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...
Humans Chromatin Pattern Recognition, Automated Thorax Small Cell Lung Carcinoma Image Processing, Computer-Assisted Lung Neoplasms Adenocarcinoma Supervised Machine Learning Reproducibility of Results Neural Networks, Computer Image Interpretation, Computer-Assisted Lung Retrospective Studies Medical Informatics Deep Learning Carcinoma, Squamous Cell
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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...
Genome Leukemia Chromatin Machine Learning Computational Biology Base Sequence Humans Neural Networks, Computer
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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. ...
Genomic Structural Variation Humans Genome, Human Chromatin Machine Learning Neoplasms
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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...
Algorithms Computer Simulation Animals Deep Learning DNA Multigene Family Neural Networks, Computer Chromatin Gene Silencing Transcription, Genetic Gene Expression Regulation Enhancer Elements, Genetic Promoter Regions, Genetic Genome, Insect Drosophila melanogaster
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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 ...
Genome Chromatin Machine Learning Protein Binding Genomics
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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 Organ Specificity Single-Cell Analysis Gene Expression Profiling Computational Biology Chromatin Gene Expression Regulation Transcriptome Chromatin Assembly and Disassembly
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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...
Microscopy, Confocal Optical Phenomena Deep Learning Microscopy, Fluorescence Humans Imaging, Three-Dimensional Chromatin Image Processing, Computer-Assisted 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...
Alleles Promoter Regions, Genetic Chromatin Artificial Intelligence Deep Learning
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