Author

Qizhi Zhang

Argonne National LaboratoryORCID 0000-0002-5131-2202Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Organism:bacteria (other)· 3 Research Focus:database / resource· 2 Approach:statistical model· 2 Assay:whole-genome sequencing· 2 Organism:fungi (other)· 1 Approach:machine learning (classical)· 1 Assay:metabolomics· 1 Organism:plant (other)· 1

Frequent Co-Authors

Christopher S. HenryiD· 3 shared Janaka N. EdirisingheiD· 3 shared Aaron A. BestiD· 2 shared Adam P. ArkiniD· 2 shared Filipe LiuiD· 2 shared José P. FariaiD· 2 shared Matthew DeJonghiD· 2 shared Robert W. CottinghamiD· 2 shared

Activity by Year

2020-2023 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 38 mutual co-author links

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3 shared papersSystems Biology

Christopher S. HenryORCID

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Author profile →

3 papers

Recent·Most cited

ModelSEED v2: High-throughput genome-scale metabolic model reconstruction with enhanced energy biosynthesis pathway prediction

José P. Faria, Filipe Liu, Janaka N. Edirisinghe, Nidhi Gupta, Samuel M. D. Seaver, Andrew Freiburger, Vibhav Setlur, Qizhi Zhang, Pamela B. Weisenhorn, Neal R. Conrad, Raphy Zarecki, Hyun‐Seob Song, Matthew DeJongh, Aaron A. Best, Robert W. Cottingham, , Christopher S. Henry

ModelSEED v2 improves genome-scale metabolic model reconstruction with enhanced energy metabolism and better pathway curation, leading to more accurate and comprehensive models.

bioRxiv · Oct 4, 2023 · PreprintSystems Biology

Machine Learning-Driven Phenotype Predictions based on Genome Annotations

Janaka N. Edirisinghe, Samaksh Goyal, Alexander Brace, Zahmeeth Sakkaff, Venkat Sai Suman Lamba Karanam, Dileep Kishore, Ricardo Colasanti, Tianhao Gu, Boris Sadhkin, Qizhi Zhang, Roy T. Kamimura, Christopher S. Henry

This paper develops a machine learning framework using genome annotations to predict bacterial phenotypes, achieving high accuracy for Gram-stain type and respiration type.

bioRxiv · Aug 11, 2023 · PreprintSystems Biology

The ModelSEED Database for the integration of metabolic annotations and the reconstruction, comparison, and analysis of metabolic models for plants, fungi, and microbes

Samuel M. D. Seaver, Filipe Liu, Qizhi Zhang, James G. Jeffryes, José P. Faria, Janaka N. Edirisinghe, Michael B. Mundy, Nicholas Chia, Εlad Noor, Aaron A. Best, Matthew DeJongh, Jeffrey A. Kimbrel, Patrik D’haeseleer, S. McCorkle, Jay R Bolton, , Christopher S. Henry

The paper introduces the ModelSEED biochemistry database, which integrates metabolic annotations and supports the reconstruction and analysis of metabolic models for plants, fungi, and microbes.

Nucleic Acids Research · Sep 24, 2020Peer-reviewedSystems Biology