AIMC Topic: Cell Differentiation
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Establishment of a morphological atlas of the Caenorhabditis elegans embryo using deep-learning-based 4D segmentation.
Nature communications
Dec 7, 2020
The invariant development and transparent body of the nematode Caenorhabditis elegans enables complete delineation of cell lineages throughout development. Despite extensive studies of cell division, cell migration and cell fate differentiation, cell...
Recognized trophoblast-like cells conversion from human embryonic stem cells by BMP4 based on convolutional neural network.
Reproductive toxicology (Elmsford, N.Y.)
Nov 26, 2020
The use of models of stem cell differentiation to trophoblastic cells provides an effective perspective for understanding the early molecular events in the establishment and maintenance of human pregnancy. In combination with the newly developed deep...
Unsupervised generative and graph representation learning for modelling cell differentiation.
Scientific reports
Jun 17, 2020
Using machine learning techniques to build representations from biomedical data can help us understand the latent biological mechanism of action and lead to important discoveries. Recent developments in single-cell RNA-sequencing protocols have allow...
Deep Learning Based on MRI for Differentiation of Low- and High-Grade in Low-Stage Renal Cell Carcinoma.
Journal of magnetic resonance imaging : JMRI
Mar 28, 2020
UNLABELLED: Pretreatment determination of renal cell carcinoma aggressiveness may help to guide clinical decision-making.
Bio-transformation of green tea infusion with tannase and its improvement on adipocyte metabolism.
Enzyme and microbial technology
Dec 23, 2019
Catechins in green tea possess various health benefits. Enzymatic treatment improves physiological activities by inducing bioconversion of catechins. Here, we investigated the effect of green tea infusion (GT) after tannase treatment, which transform...
Deciphering epigenomic code for cell differentiation using deep learning.
BMC genomics
Sep 12, 2019
BACKGROUND: Although DNA sequence plays a crucial role in establishing the unique epigenome of a cell type, little is known about the sequence determinants that lead to the unique epigenomes of different cell types produced during cell differentiatio...
Evaluating adipocyte differentiation of bone marrow-derived mesenchymal stem cells by a deep learning method for automatic lipid droplet counting.
Computers in biology and medicine
Jul 22, 2019
Stem cells are a group of competent cells capable of self-renewal and differentiating into osteogenic, chondrogenic, and adipogenic lineages. These cells provide the possibility of successfully treating patients. During differentiation into adipose t...
Deep learning for high-throughput quantification of oligodendrocyte ensheathment at single-cell resolution.
Communications biology
Mar 26, 2019
High-throughput quantification of oligodendrocyte myelination is a challenge that, if addressed, would facilitate the development of therapeutics to promote myelin protection and repair. Here, we established a high-throughput method to assess oligode...
Deep Learning Neural Networks Highly Predict Very Early Onset of Pluripotent Stem Cell Differentiation.
Stem cell reports
Mar 14, 2019
Deep learning is a significant step forward for developing autonomous tasks. One of its branches, computer vision, allows image recognition with high accuracy thanks to the use of convolutional neural networks (CNNs). Our goal was to train a CNN with...
Differentiation of glioblastoma from solitary brain metastases using radiomic machine-learning classifiers.
Cancer letters
Mar 13, 2019
This study aimed to identify the optimal radiomic machine-learning classifier for differentiating glioblastoma (GBM) from solitary brain metastases (MET) preoperatively. Four hundred and twelve patients with solitary brain tumors (242 GBM and 170 sol...
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