Collaborative Research: CCRI: Planning: A Multilayer Network (MLN) Community Infrastructure for Data,Interaction,Visualization, and softwarE(MLN-DIVE) (open access)

Collaborative Research: CCRI: Planning: A Multilayer Network (MLN) Community Infrastructure for Data,Interaction,Visualization, and softwarE(MLN-DIVE)

Data management plan for the grant "Collaborative Research: CCRI: Planning: A Multilayer Network (MLN) Community Infrastructure for Data,Interaction,Visualization, and softwarE(MLN-DIVE)." Research relating to creating a community infrastructure for researchers using multilayer networks (MLN). This project uses a formally established network decoupling approach to perform various aggregate analysis (community, centrality, substructure detection, etc.) using individual layers and composing them. The broader impact of this planning project is to provide meaningful and appropriate analysis tools that are grounded in theory to a broad range of applications from different domains. The focus is on facilitating the mainstream use of multilayer network analysis in data analysis, research and teaching.
Date: 2021-10-01/2022-09-30
Creator: Bhowmick, Sanjukta
Object Type: Text
System: The UNT Digital Library
Predicting psoriasis using routine laboratory tests with random forest (open access)

Predicting psoriasis using routine laboratory tests with random forest

Article describes how psoriasis is a chronic inflammatory skin disease that affects approximately 125 million people worldwide. The goal of the authors' study is to derive a powerful predictive model for psoriasis disease based on only routine hospital tests.
Date: October 19, 2021
Creator: Zhou, Jing; Li, Yuzhen & Guo, Xuan
Object Type: Article
System: The UNT Digital Library
Unsupervised learning in images and audio to produce neural receptive fields: a primer and accessible notebook (open access)

Unsupervised learning in images and audio to produce neural receptive fields: a primer and accessible notebook

This article presents a consolidated review of Independent Component Analysis (ICA) as an efficient neural coding scheme with the ability to model early visual and auditory neural processing.
Date: October 19, 2021
Creator: Urs, Namratha; Behpour, Sahar; Georgaras, Angie & Albert, Mark
Object Type: Article
System: The UNT Digital Library