Alzheimer's Disease

Machine learning compensates fold-change method and highlights oxidative phosphorylation in the brain transcriptome of Alzheimer's disease.

Alzheimer's disease (AD) is a neurodegenerative disorder causing 70% of dementia cases. However, the mechanism of disease development is still elusive. Despite the availability of a wide range of biological data, a comprehensive understanding of AD's mechanism from machine learning (ML) is so far unrealized, majorly due to the lack of needed data density. To harness the AD mechanism's knowledge fr...

Prognostic accuracy of MALDI-TOF mass spectrometric analysis of plasma in COVID-19.

SARS-CoV-2 infection poses a global health crisis. In parallel with the ongoing world effort to identify therapeutic solutions, there is a critical need for improvement in the prognosis of COVID-19. Here, we report plasma proteome fingerprinting that predict high (hospitalized) and low-risk (outpatients) cases of COVID-19 identified by a platform that combines machine learning with matrix-assisted...

Characterizing the Clinical Features and Atrophy Patterns of -Related Frontotemporal Dementia With Disease Progression Modeling.

BACKGROUND AND OBJECTIVE: Mutations in the gene cause frontotemporal dementia (FTD). Most previous studies investigating the neuroanatomical signatur...

A Real-Time Clinical Decision Support System, for Mild Cognitive Impairment Detection, Based on a Hybrid Neural Architecture.

Clinical procedure for mild cognitive impairment (MCI) is mainly based on clinical records and short cognitive tests. However, low suspicion and diffi...

Deep learning of resting-state electroencephalogram signals for three-class classification of Alzheimer's disease, mild cognitive impairment and healthy ageing.

This study aimed to produce a novel deep learning (DL) model for the classification of subjects with Alzheimer's disease (AD), mild cognitive impairme...

Machine learning models to select potential inhibitors of acetylcholinesterase activity from SistematX: a natural products database.

Alzheimer's disease is the most common form of dementia, representing 60-70% of dementia cases. The enzyme acetylcholinesterase (AChE) cleaves the est...

Machine learning analyses of antibody somatic mutations predict immunoglobulin light chain toxicity.

In systemic light chain amyloidosis (AL), pathogenic monoclonal immunoglobulin light chains (LC) form toxic aggregates and amyloid fibrils in target o...

Prediction of caregiver quality of life in amyotrophic lateral sclerosis using explainable machine learning.

Amyotrophic Lateral Sclerosis (ALS) is a rare neurodegenerative, fatal and currently incurable disease. People with ALS need support from informal car...

Barriers and facilitators to the implementation of social robots for older adults and people with dementia: a scoping review.

BACKGROUND: Psychosocial issues, such as social isolation and loneliness among older adults and people with dementia, continue to pose challenges with...

Effectiveness of robot therapy in the management of behavioural and psychological symptoms for individuals with dementia: A systematic review and meta-analysis.

Robot therapy presents a promising alternative in dementia care. However, its effectiveness has not been verified comprehensively. This systematic rev...

Pivotal Response Treatment with and without robot-assistance for children with autism: a randomized controlled trial.

Pivotal response treatment (PRT) is a promising intervention focused on improving social communication skills in children with autism spectrum disorde...

MILAMP: Multiple Instance Prediction of Amyloid Proteins.

Amyloid proteins are implicated in several diseases such as Parkinson's, Alzheimer's, prion diseases, etc. In order to characterize the amyloidogenici...

Predicting the Prognosis of MCI Patients Using Longitudinal MRI Data.

The aim of this study is to develop a computer-aided diagnosis system with a deep-learning approach for distinguishing "Mild Cognitive Impairment (MCI...

Cortical Thickness from MRI to Predict Conversion from Mild Cognitive Impairment to Dementia in Parkinson Disease: A Machine Learning-based Model.

Background Group comparison results associating cortical thinning and Parkinson disease (PD) dementia (PDD) are limited in their application to clinic...

Application of deep learning to understand resilience to Alzheimer's disease pathology.

People who have Alzheimer's disease neuropathologic change (ADNC) typically associated with dementia but not the associated cognitive decline can be c...

Acceptability of Social Robots and Adaptation of Hybrid-Face Robot for Dementia Care in India: A Qualitative Study.

OBJECTIVES: This study aims to understand the acceptability of social robots and the adaptation of the Hybrid-Face Robot for dementia care in India.

Deep recurrent model for individualized prediction of Alzheimer's disease progression.

Alzheimer's disease (AD) is known as one of the major causes of dementia and is characterized by slow progression over several years, with no treatmen...

A machine learning model for identifying patients at risk for wild-type transthyretin amyloid cardiomyopathy.

Transthyretin amyloid cardiomyopathy, an often unrecognized cause of heart failure, is now treatable with a transthyretin stabilizer. It is therefore ...

A Deep Learning Strategy for Automatic Sleep Staging Based on Two-Channel EEG Headband Data.

Sleep disturbances are common in Alzheimer's disease and other neurodegenerative disorders, and together represent a potential therapeutic target for ...

Texture analysis of muscle MRI: machine learning-based classifications in idiopathic inflammatory myopathies.

To develop a machine learning (ML) model that predicts disease groups or autoantibodies in patients with idiopathic inflammatory myopathies (IIMs) usi...