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A Machine Learning Approach to Prioritizing Functionally Active F-box Members in Arabidopsis thaliana.

ABSTRACT: Protein degradation through the Ubiquitin (Ub)-26S Proteasome System (UPS) is a major gene expression regulatory pathway in plants. In this pathway, the 76-amino acid Ub proteins are covalently linked onto a large array of UPS substrates with the help of three enzymes (E1 activating, E2 conjugating, and E3 ligating enzymes) and direct them for turnover in the 26S proteasome complex. The S-phase Kinase-associated Protein 1 (Skp1), CUL1, F-box (FBX) protein (SCF) complexes have been identified as the largest E3 ligase group in plants due to the dramatic number expansion of the FBX genes in plant genomes. Since it is the FBX proteins that recognize and determine the specificity of SCF substrates, much effort has been done to characterize their genomic, physiological, and biochemical roles in the past two decades of functional genomic studies. However, the sheer size and high sequence diversity of the FBX gene family demands new approaches to uncover unknown functions. In this work, we first identified 82 known FBX members that have been functionally characterized up to date in Arabidopsis thaliana. Through comparing the genomic structure, evolutionary selection, expression patterns, domain compositions, and functional activities between known and unknown FBX gene members, we developed a neural network machine learning approach to predict whether an unknown FBX member is likely functionally active in Arabidopsis, thereby facilitating its future functional characterization.

SUBMITTER: Li Y

PROVIDER: S-EPMC8192846 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundVaccine safety surveillance is important because it is related to vaccine hesitancy, which affects vaccination rate. To increase confidence in vaccination, the active monitoring of vaccine adverse events is important. For effective active surveillance, we developed and verified a machine learning-based active surveillance system using national claim data.MethodsWe used two databases, one from the Korea Disease Control and Prevention Agency, which contains flu vaccination records for the elderly, and another from the National Health Insurance Service, which contains the claim data of vaccinated people. We developed a case-crossover design based machine learning model to predict the health outcome of interest events (anaphylaxis and agranulocytosis) using a random forest. Feature importance values were evaluated to determine candidate associations with each outcome. We investigated the relationship of the features to each event via a literature review, comparison with the Side Effect Resource, and using the Local Interpretable Model-agnostic Explanation method.ResultsThe trained model predicted each health outcome of interest with a high accuracy (approximately 70%). We found literature supporting our results, and most of the important drug-related features were listed in the Side Effect Resource database as inducing the health outcome of interest. For anaphylaxis, flu vaccination ranked high in our feature importance analysis and had a positive association in Local Interpretable Model-Agnostic Explanation analysis. Although the feature importance of vaccination was lower for agranulocytosis, it also had a positive relationship in the Local Interpretable Model-Agnostic Explanation analysis.ConclusionWe developed a machine learning-based active surveillance system for detecting possible factors that can induce adverse events using health claim and vaccination databases. The results of the study demonstrated a potentially useful application of two linked national health record databases. Our model can contribute to the establishment of a system for conducting active surveillance on vaccination.

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A Machine Learning Approach to Prioritizing Functionally Active F-box Members in Arabidopsis thaliana.

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