Research
The main research topics of Anacleto Lab can be summarized as follows:
Machine Learning for Personalized Medicine
Gene function prediction
- Hierarchical ensembles of learning machines for gene function prediction
- Cost-sensitive and parametrized Hopfield Networks for gene function prediction
- Network-based gene function prediction through kernelized score functions
Drug repositioning and drug-target prediction
Disease gene prioritization
Unsupervised search for patterns in biomolecular data
- Stability-based methods for the assessment of the reliability of clusters discovered in complex bio-molecular data
- Ensemble clustering methods for the analysis of patterns in bio-molecular data
Integration of multiple sources of bio-molecular data
- Network combination algorithms for the massive integration of omics data
- Supervised ensemble methods for biomolecular data integration
Phenotype/outcome prediction and biomarker selection
Big data analysis in computational biology