AIMC Topic: K562 Cells

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Combining array-assisted SERS microfluidic chips and machine learning algorithms for clinical leukemia phenotyping.

Talanta Nov 2, 2024
The disease progression and treatment options of leukemia between different subtypes vary considerably, emphasizing the importance of phenotyping. However, early typing of leukemia remains challenging due to the lack of highly sensitive and specific ...
Spectrum Analysis, Raman K562 Cells Lab-On-A-Chip Devices Leukemia Humans Machine Learning Algorithms Phenotype
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Nested epistasis enhancer networks for robust genome regulation.

Science (New York, N.Y.) Aug 11, 2022
Mammalian genomes have multiple enhancers spanning an ultralong distance (>megabases) to modulate important genes, but it is unclear how these enhancers coordinate to achieve this task. We combine multiplexed CRISPRi screening with machine learning t...
K562 Cells Cell Cycle Proteins Epistasis, Genetic Enhancer Elements, Genetic Nuclear Proteins Transcription Factors Disease Humans Machine Learning Genome-Wide Association Study
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Precipitate-Supported Thermal Proteome Profiling Coupled with Deep Learning for Comprehensive Screening of Drug Target Proteins.

ACS chemical biology Jan 6, 2022
Although thermal proteome profiling (TPP) acts as a popular modification-free approach for drug target deconvolution, some key problems are still limiting screening sensitivity. In the prevailing TPP workflow, only the soluble fractions are analyzed ...
Proteins Chemical Precipitation Hot Temperature Drug Delivery Systems Humans Deep Learning K562 Cells Algorithms Proteomics Proteome
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DeepD2V: A Novel Deep Learning-Based Framework for Predicting Transcription Factor Binding Sites from Combined DNA Sequence.

International journal of molecular sciences May 24, 2021
Predicting in vivo protein-DNA binding sites is a challenging but pressing task in a variety of fields like drug design and development. Most promoters contain a number of transcription factor (TF) binding sites, but only a small minority has been id...
Humans Software Deep Learning DNA DNA-Binding Proteins Promoter Regions, Genetic K562 Cells Computational Biology Transcription Factors Cell Line Binding Sites
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Deep-learning-based three-dimensional label-free tracking and analysis of immunological synapses of CAR-T cells.

eLife Dec 17, 2020
The immunological synapse (IS) is a cell-cell junction between a T cell and a professional antigen-presenting cell. Since the IS formation is a critical step for the initiation of an antigen-specific immune response, various live-cell imaging techniq...
Immunological Synapses Deep Learning K562 Cells Tomography, Optical Humans Receptors, Chimeric Antigen T-Lymphocytes
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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...
Genome, Human K562 Cells Gene Expression Regulation, Leukemic Chromatin Algorithms Computer Simulation Support Vector Machine Supervised Machine Learning Humans
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A supervised learning framework for chromatin loop detection in genome-wide contact maps.

Nature communications Jul 9, 2020
Accurately predicting chromatin loops from genome-wide interaction matrices such as Hi-C data is critical to deepening our understanding of proper gene regulation. Current approaches are mainly focused on searching for statistically enriched dots on ...
Genome Chromatin Humans Databases, Genetic Sequence Analysis, DNA Supervised Machine Learning Species Specificity Organ Specificity K562 Cells
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Graph embedding and unsupervised learning predict genomic sub-compartments from HiC chromatin interaction data.

Nature communications Mar 3, 2020
Chromatin interaction studies can reveal how the genome is organized into spatially confined sub-compartments in the nucleus. However, accurately identifying sub-compartments from chromatin interaction data remains a challenge in computational biolog...
Humans Neural Networks, Computer Data Analysis Algorithms Reproducibility of Results Genomics Cluster Analysis Epigenome Unsupervised Machine Learning K562 Cells Computer Graphics Gene Expression Chromatin Markov Chains
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Deep-learning augmented RNA-seq analysis of transcript splicing.

Nature methods Mar 25, 2019
A major limitation of RNA sequencing (RNA-seq) analysis of alternative splicing is its reliance on high sequencing coverage. We report DARTS (https://github.com/Xinglab/DARTS), a computational framework that integrates deep-learning-based predictions...
Humans K562 Cells Algorithms RNA Splicing Hep G2 Cells Exons Reproducibility of Results Models, Statistical Alternative Splicing Gene Expression Profiling RNA Bayes Theorem Deep Learning High-Throughput Nucleotide Sequencing Signal Processing, Computer-Assisted Sequence Analysis, RNA Gene Expression Regulation Epigenomics
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Predictable and precise template-free CRISPR editing of pathogenic variants.

Nature Nov 7, 2018
Following Cas9 cleavage, DNA repair without a donor template is generally considered stochastic, heterogeneous and impractical beyond gene disruption. Here, we show that template-free Cas9 editing is predictable and capable of precise repair to a pre...
HEK293 Cells Fibroblasts K562 Cells Base Sequence Hermanski-Pudlak Syndrome CRISPR-Cas Systems Humans Gene Editing Substrate Specificity Alleles Menkes Kinky Hair Syndrome Machine Learning DNA Repair HCT116 Cells Templates, Genetic Reproducibility of Results CRISPR-Associated Protein 9
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