Discover Community Create You

Loading publication...

Noodle always counts identifier-free feature events for service operation. With your permission, Google Analytics can also estimate audience and engagement. Search text, publication identifiers, cookies, IP addresses and browser fingerprints are never included in Noodle's first-party event counters.

Privacy details

Article

Machine learning reveals prominent spontaneous behavioral changes and treatment efficacy in humanized and transgenic Alzheimer's disease models

19 Oct 2024

Abstract excerpt

mice largely prevented spontaneous behavioral alterations, indicating a key role for neuroinflammation. Thus, AD-related spontaneous behavioral alterations are prominent in knockin and transgenic models and sensitive to therapeutic interventions. VAME outcomes had higher specificity and sensitivity than conventional behavioral outcomes. We conclude that spontaneous behavior effectively captures age- and...

Read the complete abstract on PubMed

Topics

Share this publication in a Topic to start or enrich a Post.

Share to Alzheimer's disease research and treatments
Share to Dementia and Cognitive Impairment Research
Share to Health, Environment, Cognitive Aging

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Source

Identifiers

PMID: 39427315
DOI: 10.1016/j.celrep.2024.114870

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 8 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

Machine learning reveals prominent spontaneous behavioral changes and treatment efficacy in humanized and transgenic Alzheimer's disease modelsPMID 39427315Read ↗

Combining supervised and unsupervised analyses to quantify behavioral phenotypes and validate therapeutic efficacy in a triple transgenic mouse model of Alzheimer’s diseaseSemantic proximity Read ↗

Combining supervised and unsupervised analyses to quantify behavioral phenotypes and validate therapeutic efficacy in a triple transgenic mouse model of Alzheimer's disease.Semantic proximity Read ↗

A machine learning approach to screen for preclinical Alzheimer's disease.Semantic proximity Read ↗

Knowledge-guided Deep Temporal Clustering for Alzheimer’s Disease Subtypes in Completed Clinical TrialsSemantic proximity Read ↗

Large-scale phenome analysis defines a behavioral signature for Huntington's disease genotype in mice.Semantic proximity Read ↗

COMPARATIVE ORDINAL LONGITUDINAL DATA ANALYSIS TO PREDICT A DIAGNOSIS OF ALZHEIMER'S DISEASE USING MULTIMODAL DATASemantic proximity Read ↗

AI-based decoding of long covid cognitive impairments in mice using automated behavioral system and comparative transcriptomic analysisSemantic proximity Read ↗

The Use of Machine Learning Methods in Neurodegenerative Disease Research: A Scoping ReviewSemantic proximity Read ↗

A Machine Learning Approach to Predict Cognitive Decline in Alzheimer’s Disease Clinical TrialsSemantic proximity Read ↗

Integrating supervised and unsupervised machine learning for behavior segmentation reveals latent frailty signatures and improves aging clocks in isogenic and outbred miceSemantic proximity Read ↗

A novel fully-automated system for lifelong continuous phenotyping of mouse cognition and behaviourSemantic proximity Read ↗

Behavioral abnormalities in APPSwe/PS1dE9 mouse model of AD-like pathology: comparative analysis across multiple behavioral domains.Semantic proximity Read ↗

⌁Select a neighboring publication to make it the new centre.