ArticlePlant methods2026
ADAM: advanced design and AI-driven modeling for plant tissue culture media optimization.
Article in Plant methods, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
- From Prediction to Creation: Generative Plant Design.Plants (Basel, Switzerland) · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundOptimization of biotechnological processes is traditionally limited by time-consuming trial-and-error approaches and the complexity of simultaneously optimizing multiple, often conflicting objectives. This applies particularly to plant tissue culture medium design, which therefore serves as the application case in this study. Recent advances in machine learning and evolutionary algorithms offer powerful alternatives, yet 80% of published studies rely on licensed software, and systematic data-driven optimization frameworks remain scarce. This creates significant barriers to adoption in both academic and commercial plant biotechnology.
resultsWe introduce ADAM (Advanced Design and AI-Driven Modeling for Plant Tissue Culture Media Optimization), an open-access, web-based platform that transforms protocol development into a data-driven computational process. ADAM implements a complete ML-EA workflow through five integrated modules: 1. Design of Experiments (five different concepts) for systematic parameter exploration, 2. Data Preparation with automated quality control, and 3. Model Building using nine machine learning algorithms with automated selection. The platform enables Optimization (4.) through four advanced evolutionary algorithms (genetic algorithm, particle swarm optimization, NSGA-II, SMS-EMOA) for single- and multi-objective problems, with Evaluation (5.) tools to compare original versus optimized solutions. Validation across two plant tissue culture applications showed that ADAM's models matched or exceeded the predictive performance of manually optimized approaches in the original studies. The platform successfully identified multiple optimal culture conditions balancing conflicting objectives, providing experimentally testable predictions that reduce the trial-and-error cycle.
conclusionsDeployed as a browser-based application requiring neither specialized hardware nor software licenses, ADAM democratizes advanced AI optimization for plant biotechnology, eliminating traditional barriers to entry while maintaining the rigor and flexibility required for scientific research.
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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.