ArticleFoods (Basel, Switzerland)2025
Machine Learning-Enabled Rapid Assessment of Plant-Based Protein Digestibility Through Physicochemical Profiles.
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 2 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
2 citing papers in PubMed.
- Artificial Intelligence-Assisted Design of Plant-Protein Meat Analogues: Integrating Nutrition, Functionality, and Fibrillation-Based Texturization.Comprehensive reviews in food science and food safety · 2026Review
- Comparing AI agents and machine learning for predicting mechanical and rheological properties of dense food structures.Current research in food science · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
Plant-based proteins offer sustainable alternatives to animal sources, yet their lower digestibility remains a critical barrier to widespread applications. Current digestibility assessment methods require days of analysis and gram-scale samples, creating significant bottlenecks in protein optimization workflows. This study developed an ensembled deep learning framework that transforms digestibility prediction from a resource-intensive process to a rapid, minimal-sample assessment. By systematically characterizing 23 diverse plant protein isolates across multiple physicochemical dimensions, we trained a feedforward neural network based on augmented data. Our model identified α-helix content, random coil content, and solubility as key digestibility indicators. This insight enabled the construction of a streamlined three-feature model that reduced assessment time by 80% while requiring only one-hundredth of standard sample amounts. When validated against independent published datasets, the model achieved rational prediction accuracy, with an R
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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.