ReviewFoods (Basel, Switzerland)2021
Diving Deep into the Data: A Review of Deep Learning Approaches and Potential Applications in Foodomics.
Review in Foods (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 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
8 citing papers in PubMed.
- Deep learning in food quality and safety management: A review of architectures, applications, and future directions.Current research in food science · 2026Review
- A benchmark for assessing large language models on molecular-to-food and food-to-molecular prediction tasks.Frontiers in artificial intelligence · 2026Article
- AI-Enabled IoT for Food Computing: Challenges, Opportunities, and Future Directions.Sensors (Basel, Switzerland) · 2025Review
- Foodomics: A sustainable approach for the specific nutrition and diets for human health.Food chemistry: X · 2024Review
- Marker Peptides for Indicating the Spoilage of Milk-Sample Preparation and Chemometric Approaches for Yielding Potential Peptides in a Raw Milk Model.Foods (Basel, Switzerland) · 2024Article
- The Shelf Life of Milk-A Novel Concept for the Identification of Marker Peptides Using Multivariate Analysis.Foods (Basel, Switzerland) · 2024Article
- Current Challenges and Future Directions in Peptidomics.Methods in molecular biology (Clifton, N.J.) · 2024Article
- Metabolomics: A suitable foodomics approach to the geographical origin traceability of Ethiopian Arabica specialty coffees.Food science & nutrition · 2023Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Deep learning is a trending field in bioinformatics; so far, mostly known for image processing and speech recognition, but it also shows promising possibilities for data processing in food analysis, especially, foodomics. Thus, more and more deep learning approaches are used. This review presents an introduction into deep learning in the context of metabolomics and proteomics, focusing on the prediction of shelf-life, food authenticity, and food quality. Apart from the direct food-related applications, this review summarizes deep learning for peptide sequencing and its context to food analysis. The review's focus further lays on MS (mass spectrometry)-based approaches. As a result of the constant development and improvement of analytical devices, as well as more complex holistic research questions, especially with the diverse and complex matrix food, there is a need for more effective methods for data processing. Deep learning might offer meeting this need and gives prospect to deal with the vast amount and complexity of data.
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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.