Computational analysis of gene-expression and transcriptomic datasets using machine learning and statistical methods.
Projects can include preprocessing, normalization, dimensionality reduction, clustering, classification, biomarker analysis, and predictive modeling.
The methods can support biological discovery, sample characterization, expression-pattern analysis, and computational research workflows.
Implementation is selected according to the available data, technical constraints, required level of automation, and the existing software or research environment. The solution can be implemented as a standalone component or integrated into a larger system.
The resulting system is intended to provide a clear computational workflow that can be evaluated, maintained, and extended as the project develops. Model choice, data processing, interfaces, and deployment can therefore be adapted to the requirements of the specific project.