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

Integrating gene expression and phenotypic information to analyze Alzheimer's disease.

Journal of Alzheimer's disease : JAD - 1 Jan 2009

Ray Monika, Zhang Weixiong

Abstract excerpt

The assessment of the relationship between gene expression profiling, clinical and histopathological phenotypes would be better suited to understanding Alzheimer's disease (AD) pathogenesis. We developed a multiple linear regression (MLR) method to simultaneously model three variables - Mini-Mental Status Examination (MMSE) score, neurofibrillary tangles (NFT) score and gene expression profile - to identify...

Read the complete abstract on PubMed

Topics

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

Share to Algorithms
Share to Alzheimer Disease
Share to Artificial Intelligence

Methodology Data sources Editorial policy Privacy Delete account & data About Contact Helena Bioinformatics

Share to Data Interpretation, Statistical

Share to Databases, Factual

Share to Models, Statistical

Share to Neuropsychological Tests

Share to Regression Analysis

Source

Identifiers

PMID: 19158423
DOI: 10.3233/JAD-2009-0917

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 7 Sept 2026. Manifest publication-graph-cohort-2026-09-08.1.

Source receipt: pubmed, retrieved 7 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

Integrating gene expression and phenotypic information to analyze Alzheimer's disease.PMID 19158423Read ↗

Incipient Alzheimer's disease: Microarray correlation analyses reveal major transcriptional and tumor suppressor responsesSemantic proximity Read ↗

Identification of Differentially Expressed Genes through Integrated Study of Alzheimer’s Disease Affected Brain RegionsSemantic proximity Read ↗

Uncovering Molecular Biomarkers That Correlate Cognitive Decline with the Changes of Hippocampus' Gene Expression Profiles in Alzheimer's DiseaseSemantic proximity Read ↗

Integrative Transcriptomics and Machine Learning Reveal a Robust Biomarker Signature for Alzheimer’s DiseaseSemantic proximity Read ↗

Transcriptional Alterations Related to Neuropathology and Clinical Manifestation of Alzheimer’s DiseaseSemantic proximity Read ↗

A Machine Learning Approach to Unmask Novel Gene Signatures and Prediction of Alzheimer’s Disease Within Different Brain RegionsSemantic proximity Read ↗

Analysis of Blood Gene Expression Data Toward Early Detection of Alzheimer’s DiseaseSemantic proximity Read ↗

Transcriptional risk scores in Alzheimer's disease: From pathology to cognitionSemantic proximity Read ↗

Altered neuronal gene expression in brain regions differentially affected by Alzheimer's disease: a reference data setSemantic proximity Read ↗

Unified AI framework to uncover deep interrelationships between gene expression and Alzheimer’s disease neuropathologiesSemantic proximity Read ↗

A Novel Blood Test for the Early Detection of Alzheimer's DiseaseSemantic proximity Read ↗

Investigating Gene Expression Patterns in Dementia Patients: A Potential Early Biomarker for Alzheimer’s DiseaseSemantic proximity Read ↗

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