GAIA provides core features for machine learning and deep learning modelling.
How That Works?
GAIA proposes a broad range of AI algorithms, covering among others classification, regression, clustering methods as well as neural networks, to extract meaningful insights from complex data. GAIA is built on top of best data-science libraries such as Scikit-Learn, Keras, TensorFlow, PyTorch and more.
Applications & Benefits
- Biomarker discovery using multivariate analysis
- Pattern extraction from complex data (e.g. imagery data)
- Pattern prediction for diagnostic, prognostic purpose
- Classification and cell subtype determination (e.g. cancer)
- Identification of therapeutic targets, drug response predictions
- … and more.
- Any data. For example: Omics, clinical, biological, environmental data
- Varying analysis time depending on the size of the data and the type of analysis (few seconds for basic multivariate analysis, to several hours for deep-learning on GPU digital labs).
- Gencovery team support
- Gencovery ecosystem support (we make the connection with the right expert)
- You keep IP on your proprietary data, and any related models or insights generated in Constellab™.
- You keep IP on your custom pipelines, know-how, and bricks created in Constellab™