ArticleCommunications biology2022
Machine learning prediction and tau-based screening identifies potential Alzheimer's disease genes relevant to immunity.
Article in Communications biology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 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
22 citing papers in PubMed, 49 citations in OpenAlex.
- Target identification and assessment in the era of AI.Nature reviews. Drug discovery · 2026Review
- Bridging data and discovery: a survey on knowledge graphs in AI for science.National science review · 2026Review
- Discovery of novel diagnostic biomarkers of hepatocellular carcinoma associated with immune infiltration.Annals of medicine · 2025Article
- Artificial intelligence-driven multi-omics approaches in Alzheimer's disease: Progress, challenges, and future directions.Acta pharmaceutica Sinica. B · 2025Review
- Role of the human cytochrome b561 family in iron metabolism and tumors (Review).Oncology letters · 2025Review
- Artificial Intelligence and Neuroscience: Transformative Synergies in Brain Research and Clinical Applications.Journal of clinical medicine · 2025Review
- KG2ML: integrating knowledge graphs and positive unlabeled learning for identifying disease-associated genes.Frontiers in bioinformatics · 2025Article
- Health and aging trajectories: shared and competing risks and resiliencies for chronic diseases associated with aging. A NIH-wide workshop.Frontiers in public health · 2025Article
- LIMO-GCN: a linear model-integrated graph convolutional network for predicting Alzheimer disease genes.Briefings in bioinformatics · 2024Article
- Pathogenic tau induces an adaptive elevation in mRNA translation rate at early stages of disease.Aging cell · 2024Article
- Machine learning assists prediction of genes responsible for plant specialized metabolite biosynthesis by integrating multi-omics data.BMC genomics · 2024Article
- The Alzheimer's Knowledge Base: A Knowledge Graph for Alzheimer Disease Research.Journal of medical Internet research · 2024Article
- Machine learning models identify predictive features of patient mortality across dementia types.Communications medicine · 2024Article
- Screening Techniques for Drug Discovery in Alzheimer's Disease.ACS omega · 2024Review
- DDK-Linker: a network-based strategy identifies disease signals by linking high-throughput omics datasets to disease knowledge.Briefings in bioinformatics · 2024Article
- ExEmPLAR (Extracting, Exploring, and Embedding Pathways Leading to Actionable Research): a user-friendly interface for knowledge graph mining.Bioinformatics (Oxford, England) · 2024Article
- Association between Alzheimer's disease pathologic products and age and a pathologic product-based diagnostic model for Alzheimer's disease.Frontiers in aging neuroscience · 2024Article
- A multi-layered network model identifies Akt1 as a common modulator of neurodegeneration.Molecular systems biology · 2023Article
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Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
13 authors at 3 institutions in 3 countries.
Funding
Abstract
With increased research funding for Alzheimer's disease (AD) and related disorders across the globe, large amounts of data are being generated. Several studies employed machine learning methods to understand the ever-growing omics data to enhance early diagnosis, map complex disease networks, or uncover potential drug targets. We describe results based on a Target Central Resource Database protein knowledge graph and evidence paths transformed into vectors by metapath matching. We extracted features between specific genes and diseases, then trained and optimized our model using XGBoost, termed MPxgb(AD). To determine our MPxgb(AD) prediction performance, we examined the top twenty predicted genes through an experimental screening pipeline. Our analysis identified potential AD risk genes: FRRS1, CTRAM, SCGB3A1, FAM92B/CIBAR2, and TMEFF2. FRRS1 and FAM92B are considered dark genes, while CTRAM, SCGB3A1, and TMEFF2 are connected to TREM2-TYROBP, IL-1β-TNFα, and MTOR-APP AD-risk nodes, suggesting relevance to the pathogenesis of AD.
Indexed as
Identifiers
What Socratic 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 Socratic graph.