AIMC Topic: K562 Cells

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Selective cytotoxic and genotoxic activities of 5-(2-bromo-5-methoxybenzylidene)-thiazolidine-2,4-dione against NCI-H292 human lung carcinoma cells.

Pharmacological reports : PR Nov 22, 2017
BACKGROUND: Thiazolidine-2,4-dione ring system is used as a pharmacophore to build various heterocyclic compounds aimed to interact with biological targets. In the present study, benzylidene-2,4-thiazolidinedione derivatives (compounds 2-5) were synt...
DNA Fragmentation Cell Line, Tumor Cytotoxins Mutagens Lung Neoplasms HL-60 Cells Comet Assay Humans Apoptosis K562 Cells MCF-7 Cells Leukocytes, Mononuclear Thiazolidinediones Antineoplastic Agents
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Imputation for transcription factor binding predictions based on deep learning.

PLoS computational biology Feb 24, 2017
Understanding the cell-specific binding patterns of transcription factors (TFs) is fundamental to studying gene regulatory networks in biological systems, for which ChIP-seq not only provides valuable data but is also considered as the gold standard....
Reproducibility of Results Binding Sites Sensitivity and Specificity Protein Interaction Mapping Humans Chromatin Immunoprecipitation Protein Binding Transcription Factors Neural Networks, Computer Hep G2 Cells Machine Learning High-Throughput Nucleotide Sequencing K562 Cells
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Combining transcription factor binding affinities with open-chromatin data for accurate gene expression prediction.

Nucleic acids research Nov 29, 2016
The binding and contribution of transcription factors (TF) to cell specific gene expression is often deduced from open-chromatin measurements to avoid costly TF ChIP-seq assays. Thus, it is important to develop computational methods for accurate TF b...
Humans CD4-Positive T-Lymphocytes Machine Learning Hepatocytes Algorithms Gene Expression Regulation Organ Specificity K562 Cells Histones Binding Sites Human Embryonic Stem Cells Hep G2 Cells Cell Line Primary Cell Culture Cell Line, Tumor Principal Component Analysis Chromatin Protein Binding DNA Chromatin Assembly and Disassembly Transcription Factors
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Identification of active transcriptional regulatory elements from GRO-seq data.

Nature methods Mar 23, 2015
Modifications to the global run-on and sequencing (GRO-seq) protocol that enrich for 5'-capped RNAs can be used to reveal active transcriptional regulatory elements (TREs) with high accuracy. Here, we introduce discriminative regulatory-element detec...
Genome-Wide Association Study Polymorphism, Single Nucleotide Regulatory Elements, Transcriptional K562 Cells Humans Cell Line Histones Gene Expression Regulation Artificial Intelligence Quantitative Trait Loci Software
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AI-based Apoptosis Cell Classification Using Phase-contrast Images of K562 Cells.

Anticancer research Mar 1, 2024
BACKGROUND/AIM: This study aimed to automate the classification of cells, particularly in identifying apoptosis, using artificial intelligence (AI) in conjunction with phase-contrast microscopy. The objective was to reduce reliance on manual observat...
Apoptosis Caspases K562 Cells Microscopy, Phase-Contrast Humans Artificial Intelligence
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A sequence-based deep learning approach to predict CTCF-mediated chromatin loop.

Briefings in bioinformatics Sep 2, 2021
Three-dimensional (3D) architecture of the chromosomes is of crucial importance for transcription regulation and DNA replication. Various high-throughput chromosome conformation capture-based methods have revealed that CTCF-mediated chromatin loops a...
Genome, Human Molecular Conformation K562 Cells Nucleotide Motifs MCF-7 Cells Datasets as Topic Gene Expression Regulation Chromatin CCCTC-Binding Factor Humans Neural Networks, Computer Software
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DeepYY1: a deep learning approach to identify YY1-mediated chromatin loops.

Briefings in bioinformatics Jul 20, 2021
The protein Yin Yang 1 (YY1) could form dimers that facilitate the interaction between active enhancers and promoter-proximal elements. YY1-mediated enhancer-promoter interaction is the general feature of mammalian gene control. Recently, some comput...
HCT116 Cells K562 Cells Chromatin YY1 Transcription Factor Databases, Factual Deep Learning Models, Biological Humans
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Deep-joint-learning analysis model of single cell transcriptome and open chromatin accessibility data.

Briefings in bioinformatics Jul 20, 2021
Simultaneous profiling transcriptomic and chromatin accessibility information in the same individual cells offers an unprecedented resolution to understand cell states. However, computationally effective methods for the integration of these inherent ...
Deep Learning Single-Cell Analysis Models, Biological K562 Cells Humans Databases, Factual Transcriptome Chromatin
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Predicting enhancer-promoter interactions by deep learning and matching heuristic.

Briefings in bioinformatics Jul 20, 2021
Enhancer-promoter interactions (EPIs) play an important role in transcriptional regulation. Recently, machine learning-based methods have been widely used in the genome-scale identification of EPIs due to their promising predictive performance. In th...
HeLa Cells Promoter Regions, Genetic K562 Cells Computational Biology Humans Enhancer Elements, Genetic Models, Genetic Deep Learning Human Umbilical Vein Endothelial Cells Heuristics
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Prediction of regulatory motifs from human Chip-sequencing data using a deep learning framework.

Nucleic acids research Sep 5, 2019
The identification of transcription factor binding sites and cis-regulatory motifs is a frontier whereupon the rules governing protein-DNA binding are being revealed. Here, we developed a new method (DEep Sequence and Shape mOtif or DESSO) for cis-re...
Transcription Factors DNA Gene Expression Regulation K562 Cells Binding Sites Nucleotide Motifs Humans Deep Learning Protein Binding Computational Biology
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