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Solanum dulcamara (Bittersweet nightshade) shows significant intraspecific variation in glycoalkaloid (GA) composition and concentration. We previously showed that constitutive differences in overall GA levels are correlated with feeding preference of the grey field slug (GFS; Deroceras reticulatum)...
2020-05-16 | MTBLS1132 | MetaboLights
In natural environments, plants have to deal with a wide range of different herbivores whose communities vary in time and space. It is believed that the chemical diversity within plant species has mainly arisen from selection pressures exerted by herbivores. So far, the effects of chemical diversity...
2019-06-17 | MTBLS738 | MetaboLights

Root mutualistic microbes can modulate the production of plant secondary metabolites affecting plant–herbivore interactions. Still, the main mechanisms underlying the impact of root mutualists on herbivore performance remain ambiguous. In particular, little is known about how changes in the plant...

2023-09-12 | MTBLS2700 | MetaboLights

Plants produce thousands of compounds, collectively called the metabolome, which mediate interactions with other organisms. The metabolome of an individual plant may change according to the number and nature of these interactions. We tested the hypothesis that tree diversity level affects the met...

2021-08-09 | MTBLS1968 | MetaboLights
Species-rich plant communities can induce unique soil biotic legacy effects through changing the abundance and composition of soil biota. These soil legacy effects can cause feedbacks to influence plant performance. In addition, soil biota can induce (defensive) secondary metabolites in shoots ...
2019-05-30 | MTBLS544 | MetaboLights
This is a Random Forest algorithm-based machine learning model called RF16, which incorporates a total of 16 genomic, molecular, demographic, and clinical features to predict the immunotherapy response for a patient. The model assigns a value of 0 for NonResponder and 1 for Responder. Please be awar...
2023-05-09 | BIOMD0000001066 | BioModels
In this study, the authors had developed a machine learning model to predict immune checkpoint blockade (ICB) response by integrating genomic, molecular, demographic and clinical data from a curated cohort (MSK-IMPACT) with 1479 patients treated with ICB across 16 different types of cancer. This mod...
2024-07-23 | MODEL2407210002 | BioModels
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