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LiHong Zhao

As seen in: Color Research & Application, Nature, Wiley Online Library, PLOS, PLOS ONE, Frontiers, MDPI, ACS Publications, Royal Society of Chemistry, BioRxiv and
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Articles

Structural mechanism of 26S proteasome regulation and its pharmacological modulation

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By Kai Pei, LiHong Zhao, Mesoscopic Physics
| The Journal of Biological Chemistry Verified
Introduction Protein degradation is essential for maintaining cellular homeostasis and regulating cell fate (1). In eukaryotic cells, two major systems mediate protein turnover: the lysosomal pathway, which degrades damaged organelles and protein aggregates, and the ubiquitin–proteasome system (UPS), which selectively eliminates short-lived, misfolded, and regulatory proteins to control processes such as cell cycle progression, DNA replication, DNA repair, and immune signaling (2).
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Ensemble machine learning algorithms leveraged on serum proteomics for enhanced early detection of ovarian cancer

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By Jie Ding, Xin Dong, Li Cao, LiHong Zhao, Bingju Wang, YUE LI, Zefeng Liu, Lin Han, Wei Zhang, Xiaofei Wang, Bo Guo, Chen Huang, Xuelan Li, Aihong Guo, Lingqin Song, Jiangbo Ding
| Nature Verified
Abstract Ovarian cancer poses a significant clinical challenge due to its asymptomatic onset and poor prognosis, highlighting the critical need for effective early detection strategies. This study developed a framework that integrates serum proteomic profiling with machine learning algorithms.
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Hybrid evolutionary-gradient training improves long-term time series forecasting

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By LiHong Zhao, Zhihui Chen, Naiqi Wu, Liping Bai
| Nature Verified
Abstract Long-Term Time Series Forecasting requires robust learning under nonstationarity, noisy gradients, and delayed adaptation caused by distribution shifts. Evolutionary-Guided Module Fusion with Gradient Refinement (EGMF-GR) is an architecture-agnostic training framework that combines population-based global exploration with gradient-based local refinement, rather than introducing a new forecasting architecture.
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