Publications by authors named "Y Y Bin"

Background: GBM is an aggressive brain tumor with limited treatment options. Prior research has indicated FOLR1 as a pivotal gene involved in cancer pathogenesis.

Aim: This study aimed to explore the involvement of folate receptor alpha (FOLR1) in glioblastoma (GBM) and evaluate its potential as a therapeutic target.

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Article Synopsis
  • Peptide detectability is key in understanding protein composition and how well peptides can be identified in samples, affecting proteomics analyses.
  • Current methods are limited by using only one type of data representation, which doesn't capture the complexity of peptides.
  • DeepPD, a new deep learning framework that integrates multiple data features and uses the information bottleneck principle, significantly improves peptide detectability predictions and shows strong performance across various datasets.
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G protein-coupled receptor 39 (GPR39), a member of the growth hormone-releasing peptide family, exhibits widespread expression across various tissues and demonstrates high constitutive activity, primarily activated by zinc ions. It plays critical roles in cell proliferation, differentiation, survival, apoptosis, and ion transport through the recruitment of Gq/11, Gs, G12/13, and β-arrestin proteins. GPR39 is involved in anti-inflammatory and antioxidant responses, highlighting its diverse pathophysiological functions.

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The development of peptide drug is hindered by the risk of amyloidogenic aggregation; if peptides tend to aggregate in this manner, they may be unsuitable for drug design. Computational methods aimed at predicting amyloidogenic sequences often face challenges in extracting high-quality features, and their predictive performance can be enchanced. To surmount these challenges, iAmyP was introduced as a specialized computational tool designed for predicting amyloidogenic hexapeptides.

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In the Paris Olympic cycle, South Korean women's athlete An Se-young rose to the top of the 2023 BWF Olympic points with a win rate of 89.5%. With An Se-young as the subject, this paper aims to carry out technical and tactical analysis of women's badminton singles and formulate a prediction model based on machine learning.

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