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Zhaohui S. Qin on Muck Rack

Zhaohui S. Qin

Atlanta
As seen in: Nature, Wiley Online Library, arXiv, BMJ Open, Frontiers in Public Health, MDPI, Taylor & Francis Online, Genome Biology, Cell Press, medRxiv and
Researcher in Bioinformatics, Genomics and Biostatistics at Emory University.
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Articles

Locus-specific stratification and prioritization unveil genetic risk mechanism underlying complex diseases

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By Qiaoqiao Liu, Yeping Zhu, Limei Xia, Yue Guo, Luyu Gong, Zhaojun Liu, Zhaohui S. Qin, Shaolin Shi, Jingping Yang, Kangchun Wang
| Nature Verified
Abstract Although genome-wide association studies have identified thousands of disease-associated loci, the mechanistic understanding and drug target discovery remain challenging, particularly for complex diseases. The multi-signal architecture of complex diseases complicates the interpretation of genetic contributions.
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PlantPTM: A deep learning framework integrating protein language models with multi-view features for predicting diverse post-translational modification sites in plants

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By Gensheng Dou, Kaiyuan Wang, Zhaohui S. Qin, Min Zhang, Wentian Zhang, Chunbo Miao, Junzhou Li, Zhen Chen
| Cell Press Verified
Resource articleOnline now Affiliations & Notes 1State Key Laboratory of High-Efficiency Production of Wheat-Maize Double Cropping, College of Agronomy, Henan Agricultural University, Zhengzhou, 450046, China 2Henan Key Laboratory of Rice Molecular Breeding and High Efficiency Production, Henan Agricultural University, Zhengzhou, 450046, China Article Info Publication History: Received December 29, 2025; Revised May 22, 2026; Accepted August 2, 2026; Published online August 3, 2026 DOI:...
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Longitudinal plasma proteomics predict phenoconversion to clinically manifest ALS

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By Ximing Ran, Joanne Wuu, Zhaohui S. Qin, Michael McDermott Verified, Johnathan Cooper-Knock, Volkan Granit, Anne-Laure Grignon, Elizabeth Lin, Maria Fernandez, María Fernández, Christina M. Lill, Andrea Malaspina, Michael Benatar, Yindi Li, Danielle Colato, Nathan Carberry
| Nature Verified
Abstract The study of pre-symptomatic amyotrophic lateral sclerosis (ALS) and the design of disease prevention trials are greatly hampered by our inability to predict which unaffected carriers of ALS-associated pathogenic variants will phenoconvert to clinically manifest disease and when.
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