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JS-MA: A Jensen-Shannon Divergence Based Method for Mapping Genome-Wide Associations on Multiple Diseases (open access)

JS-MA: A Jensen-Shannon Divergence Based Method for Mapping Genome-Wide Associations on Multiple Diseases

Article develops a a simple, fast, and powerful method, named JS-MA, based on Jensen-Shannon divergence and agglomerative hierarchical clustering, to detect the genome-wide multi-locus interactions associated with multiple diseases.
Date: October 30, 2020
Creator: Guo, Xuan
System: The UNT Digital Library
NRPreTo: A Machine Learning-Based Nuclear Receptor and Subfamily Prediction Tool (open access)

NRPreTo: A Machine Learning-Based Nuclear Receptor and Subfamily Prediction Tool

Article asserts that the nuclear receptor (NR) superfamily includes phylogenetically related ligand-activated proteins, which play a key role in various cellular activities. The authors developed Nuclear Receptor Prediction Tool (NRPreTo), a two-level NR prediction tool with a unique training approach where in addition to the sequence-based features used by existing NR prediction tools, six additional feature groups depicting various physiochemical, structural, and evolutionary features of proteins were utilized.
Date: May 30, 2023
Creator: Madugula, Sita Sirisha; Pandey, Suman; Amalapurapu, Shreya & Bozdag, Serdar
System: The UNT Digital Library
PPAD: a deep learning architecture to predict progression of Alzheimer’s disease (open access)

PPAD: a deep learning architecture to predict progression of Alzheimer’s disease

Article asserts that Alzheimer’s disease (AD) is a neurodegenerative disease that affects millions of people worldwide. The authors of the article propose two deep learning architectures based on RNN, namely Predicting Progression of Alzheimer’s Disease (PPAD) and PPAD-Autoencoder.
Date: June 30, 2023
Creator: Olaimat, Mohammad Al; Martinez, Jared; Saeed, Fahad & Bozdag, Serdar
System: The UNT Digital Library
Securing Industrial Control Systems: Components, Cyber Threats, and Machine Learning-Driven Defense Strategies (open access)

Securing Industrial Control Systems: Components, Cyber Threats, and Machine Learning-Driven Defense Strategies

Article describes how Industrial Control Systems (ICS), which include Supervisory Control and Data Acquisition (SCADA) systems, Distributed Control Systems (DCS), and Programmable Logic Controllers (PLC), play a crucial role in managing and regulating industrial processes. This article presents an overview of ICS security, covering its components, protocols, industrial applications, and performance aspects.
Date: October 30, 2023
Creator: Nankya, Mary; Chataut, Robin & Akl, Robert
System: The UNT Digital Library