ArticleScientific reports2025
Identification of therapeutic targets for Alzheimer's Disease Treatment using bioinformatics and machine learning.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
5 citing papers in PubMed.
- Transcriptomic analysis reveals and validates lipid raft-associated biomarkers and functional networks in Alzheimer's disease.IBRO neuroscience reports · 2026Article
- Differences in miRNAs profile in pregnant women with preeclampsia compared with healthy pregnant women.Human genomics · 2026Article
- Biomarkers and therapeutic strategies targeting microglia in neurodegenerative diseases: current status and future directions.Molecular neurodegeneration · 2025Review
- Analysis and prediction of immune cell infiltration characteristics in COPD: Folium isatidis and its active ingredients are able to combat lung lesions caused by COPD by correcting immune cell infiltration.Frontiers in medicine · 2025Article
- Recent Advances in the Application of Artificial Intelligence in Alzheimer's Disease.Current Alzheimer research · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Alzheimer's disease (AD) is a complex neurodegenerative disorder that currently lacks effective treatment options. This study aimed to identify potential therapeutic targets for the treatment of AD using comprehensive bioinformatics methods and machine learning algorithms. By integrating differential gene expression analysis, weighted gene co-expression network analysis, Mfuzz clustering, single-cell RNA sequencing, and machine learning algorithms including LASSO regression, SVM-RFE, and random forest, five hub genes related to AD, including PLCB1, NDUFAB1, KRAS, ATP2A2, and CALM3 were identified. PLCB1, in particular, exhibited the highest diagnostic value in AD and showed significant correlation with Braak stages and neuronal expression. Furthermore, Noscapine, PX-316, and TAK-901 were selected as potential therapeutic drugs for AD based on PLCB1. This research provides a comprehensive and reliable method for the discovery of AD therapeutic targets and the construction of diagnostic models, offering important insights and directions for future AD treatment strategies and drug development.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.