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Cell Differentiation

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Unsupervised generative and graph representation learning for modelling cell differentiation.

Scientific reports
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
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
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
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
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
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
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
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...

Whey Protein Complexes with Green Tea Polyphenols: Antimicrobial, Osteoblast-Stimulatory, and Antioxidant Activities.

Cells, tissues, organs
Polyphenols are known for their antimicrobial activity, whilst both polyphenols and the globular protein β-lactoglobulin (bLG) are suggested to have antioxidant properties and promote cell proliferation. These are potentially useful properties for a ...

DeepNEU: cellular reprogramming comes of age - a machine learning platform with application to rare diseases research.

Orphanet journal of rare diseases
BACKGROUND: Conversion of human somatic cells into induced pluripotent stem cells (iPSCs) is often an inefficient, time consuming and expensive process. Also, the tendency of iPSCs to revert to their original somatic cell type over time continues to ...