ArticleFoods (Basel, Switzerland)2025
Bioinformatics and Deep Learning Approach to Discover Food-Derived Active Ingredients for Alzheimer's Disease Therapy.
Article in Foods (Basel, Switzerland), 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
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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.
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Who cites it
5 citing papers in PubMed.
- Machine Learning Approaches for Compound-Target Interaction Prediction: A Review.Foods (Basel, Switzerland) · 2026Review
- Integrated computational and experimental analysis explores FOLH1 expression patterns across cancers and nominates melatonin as a potential modulator in prostate cancer models.PLoS computational biology · 2026Article
- Artificial intelligence-based screening of phytochemicals for targeted cancer therapy.Natural products and bioprospecting · 2026Review
- Fusion-ADiNet: a multi-level framework for enhanced diabetes and Alzheimer's disease detection using chimp-whale fusion estimation.Scientific reports · 2025Article
- Integrative bioinformatics and machine learning identify iron metabolism genes MAP4, GPT, and HIRIP3 as diagnostic biomarkers and therapeutic targets in Alzheimer's disease.Frontiers in cellular neuroscience · 2025Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Alzheimer's disease (AD) prevention is a critical challenge for aging societies, necessitating the exploration of food ingredients and whole foods as potential therapeutic agents. This study aimed to identify natural compounds (NCs) with therapeutic potential in AD using an innovative bioinformatics-integrated deep neural analysis approach, combining computational predictions with molecular docking and in vitro experiments for comprehensive evaluation. We employed the bioinformatics-integrated deep neural analysis of NCs for Disease Discovery (BioDeepNat) application in the data collected from chemical databases. Random forest regression models were utilized to predict the IC
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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.