Publications by authors named "Guangliang Yu"

2 Publications

  • Page 1 of 1

Discovery of A031 as effective proteolysis targeting chimera (PROTAC) androgen receptor (AR) degrader for the treatment of prostate cancer.

Eur J Med Chem 2021 Apr 23;216:113307. Epub 2021 Feb 23.

State Key Laboratory of Applied Organic Chemistry, Department of Chemistry, Lanzhou University, 222 S. Tianshui Rd, Lanzhou, 730000, PR China. Electronic address:

Androgen receptor (AR) is an effective therapeutic target for the treatment of prostate cancer. We report herein the design, synthesis, and biological evaluation of highly effective proteolysis targeting chimeras (PROTAC) androgen receptor (AR) degraders, such as compound A031. It could induce the degradation of AR protein in VCaP cell lines in a time-dependent manner, achieving the IC 50 value of less than 0.25 μM. The A031 is 5 times less toxic than EZLA and works with an appropriate half-life (t 1/2) or clearance rate (Cl). Also, it has a significant inhibitory effect on tumor growth in zebrafish transplanted with human prostate cancer (VCaP). Therefore, A031 provides a further idea of developing novel drugs for prostate cancer.
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http://dx.doi.org/10.1016/j.ejmech.2021.113307DOI Listing
April 2021

Encoding physiological signals as images for affective state recognition using convolutional neural networks.

Annu Int Conf IEEE Eng Med Biol Soc 2016 Aug;2016:812-815

Affective state recognition based on multiple modalities of physiological signals has been a hot research topic. Traditional methods require designing hand-crafted features based on domain knowledge, which is time-consuming and has not achieved a satisfactory performance. On the other hand, conducting classification on raw signals directly can also cause some problems, such as the interference of noise and the curse of dimensionality. To address these problems, we propose a novel approach that encodes different modalities of data as images and use convolutional neural networks (CNN) to perform the affective state recognition task. We validate our aproach on the DECAF dataset in comparison with two state-of-the-art methods, i.e., the Support Vector Machines (SVM) and Random Forest (RF). Experimental results show that our aproach outperforms the baselines by 5% to 9%.
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http://dx.doi.org/10.1109/EMBC.2016.7590825DOI Listing
August 2016