Subset selection of protein attributes by Fuzzy Integrals

MURILLO JAVIER; SPETALE FLAVIO EZEQUIEL; ARCE DEBORA; TAPIA ELIZABETH; GUILLAUME SERGE; BULACIO PILAR

Rosario

Congreso; IV Congreso Argentino de Bioinformática & IV Conferencia de SOIBIO; 2013

Electronic protein annotation is traditionally performed by similarity searches against databases of protein sequences and functional domains. Though robust, these methods usually leave a great portion of unannotated sequences which may be solved by supervised machine learning methods and corresponding training datasets. New proposals for attribute selection aim at selecting subsets of attributes based on their collective behavior. Under this baseline, collective attribute selection based on Choquet Fuzzy Integral (FI) is proposed. The proposal is evaluated with attributes from Arabidopsis data and the biological interpretation of results is discussed.