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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
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...

Feasibility study of stain-free classification of cell apoptosis based on diffraction imaging flow cytometry and supervised machine learning techniques.

Apoptosis : an international journal on programmed cell death
This study was to explore the feasibility of prediction and classification of cells in different stages of apoptosis with a stain-free method based on diffraction images and supervised machine learning. Apoptosis was induced in human chronic myelogen...

Predictable and precise template-free CRISPR editing of pathogenic variants.

Nature
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...

Prediction of K562 Cells Functional Inhibitors Based on Machine Learning Approaches.

Current pharmaceutical design
BACKGROUND: β thalassemia is a common monogenic genetic disease that is very harmful to human health. The disease arises is due to the deletion of or defects in β-globin, which reduces synthesis of the β-globin chain, resulting in a relatively excess...

Towards rapid prediction of drug-resistant cancer cell phenotypes: single cell mass spectrometry combined with machine learning.

Chemical communications (Cambridge, England)
Combined single cell mass spectrometry and machine learning methods is demonstrated for the first time to achieve rapid and reliable prediction of the phenotype of unknown single cells based on their metabolomic profiles, with experimental validation...

Deep-learning augmented RNA-seq analysis of transcript splicing.

Nature methods
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...

Prediction of regulatory motifs from human Chip-sequencing data using a deep learning framework.

Nucleic acids research
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...

Graph embedding and unsupervised learning predict genomic sub-compartments from HiC chromatin interaction data.

Nature communications
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...

A supervised learning framework for chromatin loop detection in genome-wide contact maps.

Nature communications
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 ...

A Comparative Study of Supervised Machine Learning Algorithms for the Prediction of Long-Range Chromatin Interactions.

Genes
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...