Autori:
E. Fersini, C. Manfredotti, E. Messina, F. Archetti
Titolo: Relational Clustering for Gene Expression
Profiles and Drug Activity Patterns Analysis
QD Quaderni – Department of informatics, systems and communication- Research Report n. 1 gennaio 2008
Collana: Tesi e Ricerca
Genere: Informatica
The combined analysis of the micro array and drug-activity datasets has the potential of revealing valuable knowledge about various relations among gene expressions and drug activity patterns in malignant cells. However, the huge amount of biological data needs appropriate data mining models in order to extract interesting patterns and useful information. The ultimate goal of the paper is to define a model which, given the gene expression profile related to a specific tumor tissue, could help in selecting a set of most responsive drugs. This is accomplished through a clustering algorithm that associates to a cell line the set of drugs whose responses are most probably related to its gene expression profile. We propose an integrated framework based on a Constraint-based Algorithm, called Relational K-Means, which groups cell lines using drug response information and taking into account cell-to-cell relationships derived from their gene expression profiles.
