Max Gordon

College of Engineering

Works (3)

Updated: April 5th, 2024 12:51

2023 review

Compositionality, sparsity, spurious heterogeneity, and other data-driven challenges for machine learning algorithms within plant microbiome studies

[Review of ]. CURRENT OPINION IN PLANT BIOLOGY, 71.

By: S. Busato n, M. Gordon n, M. Chaudhari n, I. Jensen *, T. Akyol*, S. Andersen*, C. Williams n 

author keywords: Machine learning; Deep learning; Plant-associated microbiome; Compositional data analysis
MeSH headings : Microbiota; Algorithms; Machine Learning; Plants
TL;DR: The analysis is expanded to other fields to quantify the degree to which mitigation approaches improve the performance of ML and describe the mathematical basis for this. (via Semantic Scholar)
Sources: Web Of Science, ORCID, NC State University Libraries
Added: January 30, 2023

2023 journal article

FER and LecRK show haplotype-dependent cold-responsiveness and mediate freezing tolerance in Lotus japonicus

PLANT PHYSIOLOGY, 191(2), 1138–1152.

By: Y. Mustamin *, T. Akyol *, M. Gordon n, A. Manggabarani *, Y. Isomura*, Y. Kawamura *, M. Bamba *, C. Williams n , S. Andersen *, S. Sato *

MeSH headings : Lotus / metabolism; Haplotypes / genetics; Freezing; Acclimatization / genetics; Adaptation, Physiological / genetics; Gene Expression Regulation, Plant
TL;DR: This work suggests that recruiting a conserved growth regulator gene, FER, and a receptor-like kinase gene, LecRK, into the set of cold-responsive genes has contributed to freezing tolerance and local climate adaptation in L. japonicus, offering functional genetic insight into perennial herb evolution. (via Semantic Scholar)
UN Sustainable Development Goal Categories
2. Zero Hunger (Web of Science)
13. Climate Action (Web of Science; OpenAlex)
Sources: Web Of Science, ORCID, NC State University Libraries
Added: February 21, 2023

2020 journal article

Gene Regulatory Network Inference: Connecting Plant Biology and Mathematical Modeling

Frontiers in Genetics, 11.

By: L. Van den Broeck n , M. Gordon n, D. Inzé *, C. Williams n  & R. Sozzani  n

author keywords: gene regulatory network; network properties; network inference; machine learning; experimental methodologies
TL;DR: This review provides examples of experimental methodologies commonly used to identify regulatory interactions and generate GRNs, and describes network inference techniques that leverage gene expression data to predict regulatory interactions. (via Semantic Scholar)
UN Sustainable Development Goal Categories
2. Zero Hunger (Web of Science)
13. Climate Action (Web of Science)
Sources: Web Of Science, Crossref, NC State University Libraries, ORCID
Added: July 13, 2020

Citation Index includes data from a number of different sources. If you have questions about the sources of data in the Citation Index or need a set of data which is free to re-distribute, please contact us.

Certain data included herein are derived from the Web of Science© and InCites© (2024) of Clarivate Analytics. All rights reserved. You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.