@misc{busato_gordon_chaudhari_jensen_akyol_andersen_williams_2023, title={Compositionality, sparsity, spurious heterogeneity, and other data-driven challenges for machine learning algorithms within plant microbiome studies}, volume={71}, ISSN={["1879-0356"]}, url={https://doi.org/10.1016/j.pbi.2022.102326}, DOI={10.1016/j.pbi.2022.102326}, abstractNote={The plant-associated microbiome is a key component of plant systems, contributing to their health, growth, and productivity. The application of machine learning (ML) in this field promises to help untangle the relationships involved. However, measurements of microbial communities by high-throughput sequencing pose challenges for ML. Noise from low sample sizes, soil heterogeneity, and technical factors can impact the performance of ML. Additionally, the compositional and sparse nature of these datasets can impact the predictive accuracy of ML. We review recent literature from plant studies to illustrate that these properties often go unmentioned. We expand our analysis to other fields to quantify the degree to which mitigation approaches improve the performance of ML and describe the mathematical basis for this. With the advent of accessible analytical packages for microbiome data including learning models, researchers must be familiar with the nature of their datasets.}, journal={CURRENT OPINION IN PLANT BIOLOGY}, author={Busato, Sebastiano and Gordon, Max and Chaudhari, Meenal and Jensen, Ib and Akyol, Turgut and Andersen, Stig and Williams, Cranos}, year={2023}, month={Feb} }
 @article{mustamin_akyol_gordon_manggabarani_isomura_kawamura_bamba_williams_andersen_sato_2023, title={FER and LecRK show haplotype-dependent cold-responsiveness and mediate freezing tolerance in Lotus japonicus}, volume={191}, ISSN={["1532-2548"]}, url={https://doi.org/10.1093/plphys/kiac533}, DOI={10.1093/plphys/kiac533}, abstractNote={Abstract}, number={2}, journal={PLANT PHYSIOLOGY}, author={Mustamin, Yusdar and Akyol, Turgut Yigit and Gordon, Max and Manggabarani, Andi Madihah and Isomura, Yoshiko and Kawamura, Yasuko and Bamba, Masaru and Williams, Cranos and Andersen, Stig Uggerhj and Sato, Shusei}, year={2023}, month={Feb}, pages={1138–1152} }
 @article{van den broeck_gordon_inzé_williams_sozzani_2020, title={Gene Regulatory Network Inference: Connecting Plant Biology and Mathematical Modeling}, volume={11}, ISSN={1664-8021}, url={http://dx.doi.org/10.3389/fgene.2020.00457}, DOI={10.3389/fgene.2020.00457}, abstractNote={Plant responses to environmental and intrinsic signals are tightly controlled by multiple transcription factors (TFs). These TFs and their regulatory connections form gene regulatory networks (GRNs), which provide a blueprint of the transcriptional regulations underlying plant development and environmental responses. This review provides examples of experimental methodologies commonly used to identify regulatory interactions and generate GRNs. Additionally, this review describes network inference techniques that leverage gene expression data to predict regulatory interactions. These computational and experimental methodologies yield complex networks that can identify new regulatory interactions, driving novel hypotheses. Biological properties that contribute to the complexity of GRNs are also described in this review. These include network topology, network size, transient binding of TFs to DNA, and competition between multiple upstream regulators. Finally, this review highlights the potential of machine learning approaches to leverage gene expression data to predict phenotypic outputs.}, journal={Frontiers in Genetics}, publisher={Frontiers Media SA}, author={Van den Broeck, Lisa and Gordon, Max and Inzé, Dirk and Williams, Cranos and Sozzani, Rosangela}, year={2020}, month={May} }