ArticleFrontiers in bioengineering and biotechnology2026
VerdaML: an explainable ensemble machine learning framework with AI-driven interactive decision support system for biomass yield optimisation and Nitrogen-Stress Risk Classification in verrucodesmus verrucosus photobioreactors.
Article in Frontiers in bioengineering and biotechnology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Optimisation of microalgal photobioreactor cultivation is essential for the sustainable production of bioproducts, and is challenged by the complex interaction of parameters, low experimental signals and the simultaneous maximisation of biomass productivity and lipid accumulation for biofuel production. This paper introduces VerdaML, an explainable, dual-objective machine learning framework to predict biomass yield of Verrucodesmus verrucosus and model nitrogen-stress induction risk as a proxy for lipid-inductive cultivation conditions as well as provide interaction-based decision support. Domain-informed feature engineering was used to recognise process optimality, stress, and interaction effects using an open-access dataset consisting of 1,080 photobioreactor observations. Biomass yield was predicted using robust regression and ensemble learning, while imbalance-aware classification with SMOTE was used to predict the lipid accumulation potential. The Gradient Boosting regression model yielded a near-ideal yield prediction (
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.