MetaboPredict
Advanced tool for Metabolomics data analysis, suitable for both qualitative and quantitative analyses, assisting in the classification of known samples and the prediction of unknown ones.
A versatile software solution designed to facilitate the analysis of metabolomics data
It supports both targeted and untargeted approaches, enabling users to identify distinct metabolic patterns within datasets.
Scientific approach & methodology
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Targeted vs. Untargeted
MetaboPredict supports both targeted and untargeted metabolomics workflows:- Targeted focuses on predefined metabolites driven by specific hypotheses.
- Untargeted explores the complete metabolic profile, revealing hidden or unexpected insights.
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Sample Classification & Prediction
Easily classify both known and unknown samples using robust models trained on annotated data. Supervised and unsupervised approaches are fully supported. -
Ensemble Modeling
Combine multiple models into a single ensemble classifier for better prediction accuracy, especially with independent test datasets. -
Data-Driven Hypothesis Generation
Discover novel patterns without predefined targets. Great for biomarker discovery and early-phase research.
Key features
- Intuitive Interface & Customizable PipelinesMetaboPredict offers a user-friendly graphical interface that allows users to build analysis pipelines without the need for coding. The modular system supports:
- Exploratory data analysis
- Supervised and unsupervised learning
- Easy export and visualization of results
1 - Step-by-Step Guided AnalysisThe software provides a guided analysis process, making it accessible to both beginners and experts. Users can customize parameters easily, allowing for focused and reproducible insights. Pre-set defaults are available for quick results, while advanced users can fine-tune settings as needed.2
- Interactive 3D VisualizationsMetaboPredict enables the visualization of results through fully interactive 3D plots, which can be exported as HTML files. These plots can be opened in any browser, allowing users to rotate and explore data points from various angles without the need for additional software.3
- Handling of Multiclass ProblemsUnlike traditional tools limited to binary classification, MetaboPredict seamlessly handles multiclass problems. Its flexible architecture prioritizes the most relevant variables and supports robust ensemble learning models for prediction, making it suitable for complex or noisy datasets.4
Applications of MetaboPredict in
Research and Clinical settings
Research and Clinical settings
MetaboPredict is designed to support complex metabolomic studies by offering robust, flexible tools for pattern recognition, classification, and hypothesis-driven analysis.
In-depth look at the key areas where the platform is especially impactful:
In-depth look at the key areas where the platform is especially impactful:
MetaboPredict facilitates the discovery of potential biomarkers by helping researchers detect specific metabolic patterns that are significantly associated with particular biological conditions. This is achieved by:
- Comparing groups (e.g., healthy vs. diseased) to highlight statistically relevant metabolites.
- Ranking features (metabolites) based on their contribution to classification accuracy.
- Using supervised learning to isolate discriminant features for diagnostic or prognostic applications.
The software enables the classification of biological samples into predefined categories (e.g., disease vs. control, or among subtypes of a condition). Its built-in machine learning algorithms help:
- Train predictive models based on annotated datasets.
- Assign unknown samples to probable classes with confidence.
- Visualize class separation using PCA or other dimension-reduction techniques.
MetaboPredict supports personalized medicine initiatives by helping predict how patients or cell lines might respond to treatments, based on their metabolic profiles. Key functionalities include:
- Identifying metabolic patterns associated with responders vs. non-responders.
- Modeling outcomes based on treatment variables using ensemble classifiers.
- Evaluating predictive performance using external validation datasets.
By uncovering relationships between metabolites, MetaboPredict allows users to infer functional biological insights through pathway-level exploration, such as:
- Linking identified features to known metabolic pathways.
- Interpreting results in the context of biochemical networks.
- Exporting relevant features for integration with pathway analysis tools (e.g., KEGG, MetaboAnalyst).
Visual pipeline & output integration
MetaboPredict offers a modular, no-code workflow that guides users through each step of metabolomics data analysis — from data import to predictive modeling.
- Modular Workflow InterfaceEach stage is clearly visualized in a customizable pipeline:
- Data Selection & Alignment
- Exploratory Analysis (e.g. PCA, clustering)
- Model Training & Classification
- Biomarker Discovery & Prediction
- Exportable Results & Integration
- Interactive 3D plots (HTML-based, browser-ready)
- Exportable tables for classification, feature rankings, and predictions
- Reusable models and ensemble classifiers
- Compatible with tools like R, Excel, MetaboAnalyst
MetaboPredict – Frequently Asked Questions
If you have questions about our no-code metabolomics analysis platform, how it handles targeted and untargeted datasets, or the types of insights and visual outputs it can deliver, the answers below will guide you.
MetaboPredict is an advanced yet intuitive software for metabolomics data analysis, supporting both targeted and untargeted approaches. It guides users through a customizable pipeline for preprocessing, exploratory analysis, and the training of supervised or unsupervised machine learning models.
MetaboPredict is used for analyzing metabolomics data through customizable pipelines that support both targeted and untargeted workflows.
It enables machine learning–based modeling, ensemble classification, and prediction of unknown samples with exportable visual results.
It enables machine learning–based modeling, ensemble classification, and prediction of unknown samples with exportable visual results.
The platform accepts targeted datasets with predefined metabolites as well as untargeted profiles aimed at discovery, applying the most suitable statistical and machine-learning pipelines to each type.
No. A step-by-step, modular interface lets you run exploratory analyses, train prediction models and generate visual reports without writing a single line of code.
MetaboPredict offers unsupervised tools such as PCA and clustering, supervised classification and regression algorithms, and even ensemble modelling for greater predictive robustness.
Interactive 3-D plots, feature rankings, model performance metrics and ready-to-share HTML reports can all be downloaded or integrated into existing analysis pipelines.
Yes. Dedicated workflows guide you from raw data through biomarker discovery, disease-state classification and treatment-response prediction.
Typical use cases include identification of diagnostic biomarkers, stratification of disease states, evaluation of therapeutic responses and exploration of metabolic pathways in precision medicine.
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