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Additional file 2: of GLADIATOR: a global approach for elucidating disease modules

NIAID Data Ecosystem2026-03-10 收录

数据链接：

https://figshare.com/articles/dataset/Additional_file_2_of_GLADIATOR_a_global_approach_for_elucidating_disease_modules/5047126 数据链接链接失效反馈

官方服务：

资源简介：

GLADIATOR performance under different random seeds. (XLSX 11 kb)

应用场景：

创建时间：

2017-05-27

决策树

算法稳健性

Decision Trees (DT), one method falling under the umbrella of Exploratory Data Mining (EDM; McArdle & Ritschard, 2013), has seen an increasing amount of use in psychological research. This can be attr

Mendeley Data2024-01-31 更新160

File S5: Module functional annotation

疾病模块识别

生物信息学

Functional and pathway annotation for modules from the DREAM Disease Module Identification Challenge. Website: https://synapse.org/modulechallenge. Preprint: Choobdar, S., Ahsen, M.E., Crawford, J.,

NIAID Data Ecosystem70

Indra DB HCM model_Degree_top55

基因网络中心性分析

疾病模块识别

Top 10% elements of the Indra DB HCM model ranked by degree centrality. More details are available at: https://biodatamining.biomedcentral.com/articles/10.1186/s13040-021-00279-2

NIAID Data Ecosystem70

基于量化最小误差熵准则处理的高维数据分类预测数据

高维数据

算法稳健性

主要面向非高斯环境下的稳健性算法研究，解决宽度学习算法在非高斯环境下，尤其是离群值较多的环境下稳健性欠佳的问题。主要记录了在UCI数据集中的Abalone数据集上的预测结果，包括预测误差与预测绝对误差，总数据量为4177条。

国家基础学科公共科学数据中心100

File S5: Module functional annotation

疾病模块识别

分子网络分析

Functional and pathway annotation for modules from the DREAM Disease Module Identification Challenge. Website: https://synapse.org/modulechallenge. Preprint: Choobdar, S., Ahsen, M.E., Crawford, J., e

DataCite Commons2025-06-01 更新70

Additional file 2: of GLADIATOR: a global approach for elucidating disease modules

资源简介：

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