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Functional Network Connectivity: Possible Biomarker for Autism Spectrum Disorders (ASD)

Wang et al. | Feb 23, 2015

Functional Network Connectivity: Possible Biomarker for Autism Spectrum Disorders (ASD)

Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder and is difficult to diagnose in young children. Here magnetoencephalography was used to compare the brain activity in patients with ASD to patients in a control group. The results show that patients with ASD have a high level of activity in different areas of the brain than those in the control group.

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A novel approach for predicting Alzheimer’s disease using machine learning on DNA methylation in blood

Adami et al. | Sep 20, 2023

A novel approach for predicting Alzheimer’s disease using machine learning on DNA methylation in blood
Image credit: National Cancer Institute

Here, recognizing the difficulty associated with tracking the progression of dementia, the authors used machine learning models to predict between the presence of cognitive normalcy, mild cognitive impairment, and Alzheimer's Disease, based on blood DNA methylation levels, sex, and age. With four machine learning models and two dataset dimensionality reduction methods they achieved an accuracy of 53.33%.

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DyGS: A Dynamic Gene Searching Algorithm for Cancer Detection

Wang et al. | Jun 05, 2018

DyGS: A Dynamic Gene Searching Algorithm for Cancer Detection

Wang and Gong developed a novel dynamic gene-searching algorithm called Dynamic Gene Search (DyGS) to create a gene panel for each of the 12 cancers with the highest annual incidence and death rate. The 12 gene panels the DyGS algorithm selected used only 3.5% of the original gene mutation pool, while covering every patient sample. About 40% of each gene panel is druggable, which indicates that the DyGS-generated gene panels can be used for early cancer detection as well as therapeutic targets in treatment methods.

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