Alzheimer's Disease

Graph-Based Biomarker Discovery and Interpretation for Alzheimer's Disease

Early diagnosis and discovery of therapeutic drug targets are crucial objectives for the effective management of Alzheimer's Disease (AD). Current approaches for AD diagnosis and treatment planning are based on radiological imaging and largely inaccessible for population-level screening due to prohibitive costs and limited availability. Recently, blood tests have shown promise in diagnosing AD a...

The Trusted Caregiver: The Influence of Eye and Mouth Design Incorporating the Baby Schema Effect in Virtual Humanoid Agents on Older Adults Users' Perception of Trustworthiness

The increasing proportion of the older adult population has made the smart home care industry one of the critical markets for virtual human-like agents. It is crucial to effectively promote a trustworthy human-computer partnership with older adults, enhancing service acceptance and effectiveness. However, few studies have focused on the facial features of the agents themselves, where the "baby s...

Genome-wide association neural networks identify genes linked to family history of Alzheimer's disease.

Augmenting traditional genome-wide association studies (GWAS) with advanced machine learning algorithms can allow the detection of novel signals in av...

Explainable deep neural networks for predicting sample phenotypes from single-cell transcriptomics.

Recent advances in single-cell RNA-Sequencing (scRNA-Seq) technologies have revolutionized our ability to gather molecular insights into different phe...

Introducing TEC-LncMir for prediction of lncRNA-miRNA interactions through deep learning of RNA sequences.

The interactions between long noncoding RNA (lncRNA) and microRNA (miRNA) play critical roles in life processes, highlighting the necessity to enhance...

Efficient Brain Imaging Analysis for Alzheimer's and Dementia Detection Using Convolution-Derivative Operations

Alzheimer's disease (AD) is characterized by progressive neurodegeneration and results in detrimental structural changes in human brains. Detecting ...

Random feature baselines provide distributional performance and feature selection benchmarks for clinical and 'omic machine learning

Identifying predictive features from high-dimensional datasets is a major task in biomedical research. However, it is difficult to determine the rob...

Simulation of Nanorobots with Artificial Intelligence and Reinforcement Learning for Advanced Cancer Cell Detection and Tracking

Nanorobots are a promising development in targeted drug delivery and the treatment of neurological disorders, with potential for crossing the blood-...

Deep learning assisted quantitative analysis of Aβ and microglia in patients with idiopathic normal pressure hydrocephalus in relation to cognitive outcome.

Neuropathologic changes of Alzheimer disease (AD) including Aβ accumulation and neuroinflammation are frequently observed in the cerebral cortex of pa...

Comparing machine learning and deep learning models to predict cognition progression in Parkinson's disease.

Cognitive decline in Parkinson's disease (PD) varies widely. While models to predict cognitive progression exist, comparing traditional probabilistic ...

Generative forecasting of brain activity enhances Alzheimer's classification and interpretation

Understanding the relationship between cognition and intrinsic brain activity through purely data-driven approaches remains a significant challenge ...

Longitudinal Causal Image Synthesis

Clinical decision-making relies heavily on causal reasoning and longitudinal analysis. For example, for a patient with Alzheimer's disease (AD), how...

Exploiting Longitudinal Speech Sessions via Voice Assistant Systems for Early Detection of Cognitive Decline

Mild Cognitive Impairment (MCI) is an early stage of Alzheimer's disease (AD), a form of neurodegenerative disorder. Early identification of MCI is ...

Copula-Linked Parallel ICA: A Method for Coupling Structural and Functional MRI brain Networks

Different brain imaging modalities offer unique insights into brain function and structure. Combining them enhances our understanding of neural mech...

SGUQ: Staged Graph Convolution Neural Network for Alzheimer's Disease Diagnosis using Multi-Omics Data

Alzheimer's disease (AD) is a chronic neurodegenerative disorder and the leading cause of dementia, significantly impacting cost, mortality, and bur...

Modeling Alzheimer's Disease: From Memory Loss to Plaque & Tangles Formation

We employ the Hopfield model as a simplified framework to explore both the memory deficits and the biochemical processes characteristic of Alzheimer...

Brain-Aware Readout Layers in GNNs: Advancing Alzheimer's early Detection and Neuroimaging

Alzheimer's disease (AD) is a neurodegenerative disorder characterized by progressive memory and cognitive decline, affecting millions worldwide. Di...

GENEVIC: GENetic data Exploration and Visualization via Intelligent interactive Console.

SUMMARY: The vast generation of genetic data poses a significant challenge in efficiently uncovering valuable knowledge. Introducing GENEVIC, an AI-dr...

Cerebral microbleeds: Association with cognitive decline and pathology build-up

Cerebral microbleeds, markers of brain damage from vascular and amyloid pathologies, are linked to cognitive decline in aging, but their role in Alz...

Towards the Discovery of Down Syndrome Brain Biomarkers Using Generative Models

Brain imaging has allowed neuroscientists to analyze brain morphology in genetic and neurodevelopmental disorders, such as Down syndrome, pinpointin...