Formal Concept Analysis Applied to Transcriptomic Data

Mehwish Alam (1) , Adrien Coulet (1) , Amedeo Napoli (1) , Malika Smaïl-Tabbone (1)

1 ORPAILLEUR - Knowledge representation, reasonning

Identifying functions or pathways shared by genes responsible for cancer is still a challenging task. This paper describes the preparation work for applying Formal Concept Analysis (FCA) to biological data. After gene transcription experiments, we integrate various annotations of selected genes in a database along with relevant domain knowledge. The database subsequently allows to build formal contexts in a flexible way. We present here a preliminary experiment using these data on a core context with the addition of domain knowledge by context apposition. The resulting concept lattices are pruned and we discuss some interesting concepts. Our study shows how data integration and FCA can help the domain expert in the exploration of complex data.

Keywords: Transcriptomic Data; Formal Concept Analysis; Knowledge Discovery; Data Integration; Transcriptomic Data.

Domains: Artificial Intelligence [cs.AI]

Mehwish Alam, Adrien Coulet, Amedeo Napoli, Malika Smaïl-Tabbone. Formal Concept Analysis Applied to Transcriptomic Data. What can FCA do for Artificial Intelligence (FCA4AI) (ECAI 2012), Aug 2012, Montpellier, France. ⟨hal-00760993⟩