Regression-Based Modeling of Complex Plant Traits Based on Metabolomics Data

2018 ◽  
pp. 321-327
Author(s):  
Francisco de Abreu e Lima ◽  
Lydia Leifels ◽  
Zoran Nikoloski
Keyword(s):  
Plant Traits ◽  
Metabolomics Data

Plant Traits Response to Grazing Exclusion by Fencing Assessed via Multiple Classification Approach: A Case from a Subalpine Meadow

2019 ◽  
Vol 67 (1) ◽  
pp. 33 ◽  
Author(s):  
Wen Jin Li ◽  
Shuang Shuang Liu ◽  
Jin Hua Li ◽  
Ru Lan Zhang ◽  
Ka Zhuo Cai Rang ◽  
...  
Keyword(s):  
Plant Traits ◽  
Classification Approach ◽  
Grazing Exclusion ◽  
Subalpine Meadow ◽  
Multiple Classification

Backcross Response of Two Mature Plant Traits for Certain Corn‐Teosinte Hybrids 1

Crop Science ◽  
1965 ◽  
Vol 5 (3) ◽  
pp. 239-241 ◽  
Author(s):  
R. J. Lambert ◽  
E. R. Leng
Keyword(s):  
Plant Traits ◽  
Mature Plant

Cytoplasmic Effects on Plant Traits in Interpecific Matings of Avena 1

Crop Science ◽  
1984 ◽  
Vol 24 (1) ◽  
pp. 200-204 ◽  
Author(s):  
L. D. Robertson ◽  
K. J. Frey
Keyword(s):  
Plant Traits ◽  
Cytoplasmic Effects

Phenotypic plasticity of plant traits contributing to grain and biomass yield of dual-purpose sorghum

Planta ◽  
2021 ◽  
Vol 253 (4) ◽  
Author(s):  
Malick Ndiaye ◽  
Bertrand Muller ◽  
Komla Kyky Ganyo ◽  
Aliou Guissé ◽  
Ndiaga Cissé ◽  
...  
Keyword(s):  
Phenotypic Plasticity ◽  
Biomass Yield ◽  
Plant Traits ◽  
Dual Purpose

scholarly journals Repository scale classification and decomposition of tandem mass spectral data

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Mihir Mongia ◽  
Hosein Mohimani
Keyword(s):  
High Accuracy ◽  
Mass Spectral Data ◽  
Metabolomics Data ◽  
Mass Spectral ◽  
Molecular Features ◽  
Oriented Samples ◽  
Ingredient Composition ◽  
Single Phenotype ◽  
Tandem Mass Spectral Data ◽  
Scale Classification

AbstractVarious studies have shown associations between molecular features and phenotypes of biological samples. These studies, however, focus on a single phenotype per study and are not applicable to repository scale metabolomics data. Here we report MetSummarizer, a method for predicting (i) the biological phenotypes of environmental and host-oriented samples, and (ii) the raw ingredient composition of complex mixtures. We show that the aggregation of various metabolomic datasets can improve the accuracy of predictions. Since these datasets have been collected using different standards at various laboratories, in order to get unbiased results it is crucial to detect and discard standard-specific features during the classification step. We further report high accuracy in prediction of the raw ingredient composition of complex foods from the Global Foodomics Project.

scholarly journals Insights into the molecular mechanisms of Huangqi decoction on liver fibrosis via computational systems pharmacology approaches

2021 ◽  
Vol 16 (1) ◽  
Author(s):  
Biting Wang ◽  
Zengrui Wu ◽  
Weihua Li ◽  
Guixia Liu ◽  
Yun Tang
Keyword(s):  
Molecular Docking ◽  
Liver Fibrosis ◽  
Molecular Mechanisms ◽  
Docking Simulation ◽  
Chinese Herbs ◽  
Network Pharmacology ◽  
Bipartite Network ◽  
Metabolomics Data ◽  
Metabolic Products ◽  
Compound Target

Abstract Background The traditional Chinese medicine Huangqi decoction (HQD) consists of Radix Astragali and Radix Glycyrrhizae in a ratio of 6: 1, which has been used for the treatment of liver fibrosis. In this study, we tried to elucidate its action of mechanism (MoA) via a combination of metabolomics data, network pharmacology and molecular docking methods. Methods Firstly, we collected prototype components and metabolic products after administration of HQD from a publication. With known and predicted targets, compound-target interactions were obtained. Then, the global compound-liver fibrosis target bipartite network and the HQD-liver fibrosis protein–protein interaction network were constructed, separately. KEGG pathway analysis was applied to further understand the mechanisms related to the target proteins of HQD. Additionally, molecular docking simulation was performed to determine the binding efficiency of compounds with targets. Finally, considering the concentrations of prototype compounds and metabolites of HQD, the critical compound-liver fibrosis target bipartite network was constructed. Results 68 compounds including 17 prototype components and 51 metabolic products were collected. 540 compound-target interactions were obtained between the 68 compounds and 95 targets. Combining network analysis, molecular docking and concentration of compounds, our final results demonstrated that eight compounds (three prototype compounds and five metabolites) and eight targets (CDK1, MMP9, PPARD, PPARG, PTGS2, SERPINE1, TP53, and HIF1A) might contribute to the effects of HQD on liver fibrosis. These interactions would maintain the balance of ECM, reduce liver damage, inhibit hepatocyte apoptosis, and alleviate liver inflammation through five signaling pathways including p53, PPAR, HIF-1, IL-17, and TNF signaling pathway. Conclusions This study provides a new way to understand the MoA of HQD on liver fibrosis by considering the concentrations of components and metabolites, which might be a model for investigation of MoA of other Chinese herbs.

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