Zhengqiao Zhao
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Explore the profile of Zhengqiao Zhao including associated specialties, affiliations and a list of published articles.
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Articles
10
Citations
271
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Recent Articles
1.
Ibrahim M, Gomez-Jenkins M, Scheinfeld A, Zhao Z, Cararo Lopes E, Sawant A, et al.
bioRxiv
. 2025 Mar;
PMID: 40060612
Key Points Of Paper: 1) Autophagy-deficient mice have reduced food intake, systemic inflammation, and cachexia2) CCL2, but not GDF15 or CXCL10, induces lethal cachexia caused by autophagy defect3) Autophagy-deficient mice...
2.
Pradella D, Zhang M, Gao R, Yao M, Gluchowska K, Cendon-Florez Y, et al.
Nature
. 2024 Dec;
637(8047):955-964.
PMID: 39695225
Focal gene amplifications are among the most common cancer-associated mutations but have proven challenging to engineer in primary cells and model organisms. Here we describe a general strategy to engineer...
3.
Sokhansanj B, Zhao Z, Rosen G
Biology (Basel)
. 2022 Dec;
11(12).
PMID: 36552295
Through the COVID-19 pandemic, SARS-CoV-2 has gained and lost multiple mutations in novel or unexpected combinations. Predicting how complex mutations affect COVID-19 disease severity is critical in planning public health...
4.
Meyer F, Fritz A, Deng Z, Koslicki D, Lesker T, Gurevich A, et al.
Nat Methods
. 2022 Apr;
19(4):429-440.
PMID: 35396482
Evaluating metagenomic software is key for optimizing metagenome interpretation and focus of the Initiative for the Critical Assessment of Metagenome Interpretation (CAMI). The CAMI II challenge engaged the community to...
5.
Zhao Z, Woloszynek S, Agbavor F, Mell J, Sokhansanj B, Rosen G
PLoS Comput Biol
. 2021 Sep;
17(9):e1009345.
PMID: 34550967
Recurrent neural networks with memory and attention mechanisms are widely used in natural language processing because they can capture short and long term sequential information for diverse tasks. We propose...
6.
ValizadehAslani T, Zhao Z, Sokhansanj B, Rosen G
Biology (Basel)
. 2020 Oct;
9(11).
PMID: 33126516
Machine learning algorithms can learn mechanisms of antimicrobial resistance from the data of DNA sequence without any a priori information. Interpreting a trained machine learning algorithm can be exploited for...
7.
Zhao Z, Cristian A, Rosen G
BMC Bioinformatics
. 2020 Sep;
21(1):412.
PMID: 32957925
Background: It is a computational challenge for current metagenomic classifiers to keep up with the pace of training data generated from genome sequencing projects, such as the exponentially-growing NCBI RefSeq...
8.
Zhao Z, Sokhansanj B, Malhotra C, Zheng K, Rosen G
PLoS Comput Biol
. 2020 Sep;
16(9):e1008269.
PMID: 32941419
We propose an efficient framework for genetic subtyping of SARS-CoV-2, the novel coronavirus that causes the COVID-19 pandemic. Efficient viral subtyping enables visualization and modeling of the geographic distribution and...
9.
Cullen C, Aneja K, Beyhan S, Cho C, Woloszynek S, Convertino M, et al.
Front Microbiol
. 2020 Mar;
11:136.
PMID: 32140140
Microbiome research has increased dramatically in recent years, driven by advances in technology and significant reductions in the cost of analysis. Such research has unlocked a wealth of data, which...
10.
Woloszynek S, Mell J, Zhao Z, Simpson G, OConnor M, Rosen G
PLoS One
. 2019 Dec;
14(12):e0219235.
PMID: 31825995
Analysis of microbiome data involves identifying co-occurring groups of taxa associated with sample features of interest (e.g., disease state). Elucidating such relations is often difficult as microbiome data are compositional,...