Predicting interactions between Bacillus subtilis and Pseudomonas spp.

NIAID Data Ecosystem2026-05-01 收录

微生物相互作用预测

数据链接：

https://www.ncbi.nlm.nih.gov/bioproject/PRJNA985909 数据链接链接失效反馈

官方服务：

资源简介：

To systematically determine and predict microbial interactions between B. subtilis and fluorescent pseudomonads

创建时间：

2023-06-21

RawData_Simulations_Schmitz_etal.

微生物相互作用预测

计算生态学

This file contains simulation data used in Schmitz et al. (2023) "Predicting bacterial interaction outcomes from monoculture growth and supernatant assays" available on bioRxiv.

Figshare2024-02-08 更新90

BCMicrobiome grapevine ITS sequencing

微生物组分析

微生物相互作用预测

Plants are associated with a huge diversity of interacting microorganisms. Deciphering these microbial interactions is crucial since they underpin the regulation of plant diseases. These microbial int

NIAID Data Ecosystem30

Additional file 1 of Predicting microbial interactions with approaches based on flux balance analysis: an evaluation

微生物相互作用预测

通量平衡分析评估

Additional file 1: Table S1. Overview of statistical values for growth rates predicted in monocultures. H: time step of 1 per hour, H/10: time step of 0.1 per hour. Table S2. Mean absolute difference

NIAID Data Ecosystem00

Predicted_the_microbial_interaction_under_YCFA_culture_condition

微生物相互作用预测

生物群落动态

Correlation networks inferred from the dynamics microbial community data are widely applied to predict microbial interactions in the human gut. However, the complexity of correlation networks and the

NIAID Data Ecosystem60

Additional file 1 of Predicting microbial interactions with approaches based on flux balance analysis: an evaluation

微生物相互作用预测

通量平衡分析评估

Additional file 1: Table S1. Overview of statistical values for growth rates predicted in monocultures. H: time step of 1 per hour, H/10: time step of 0.1 per hour. Table S2. Mean absolute difference

Figshare2024-08-14 更新00

Predicting interactions between Bacillus subtilis and Pseudomonas spp.

资源简介：

相关数据集