01660nam a2200253 a 450000100080000000500110000800800410001902400380006010000160009824501150011426001650022930000160039449000450041050000140045552007030046965000180117265000330119065300310122365300140125465300370126865300430130565300410134870000170138915798922020-01-15       2008    bl uuuu     u00u1 u    #d7 a10.1007/978-3-540-85557-6_162DOI1 aHIGA, R. H.  aPrediction of protein-protein binding hot spotsba combination of classifiers approach.h[electronic resource]  aIn: BRAZILIAN SYMPOSIUM ON BIOINFORMATICS, 3., 2008, Santo André, SP. Advances in bioinformatics and computational biology: proceedings. Berlin: Springerc2008  ap. 165-168.  a(Lecture notes in bioinformatics, 5167).  aBSB 2008.  aIn this work we approach the problem of predicting protein binding hot spot residues through a combination of classifiers. We consider a comprehensive set of structural and chemical properties reported in the literature for characterizing hot spot residues. Each component classifier considers a specific set of properties as feature set and their output are combined by the mean rule. The proposed combination of classifiers achieved a performance of 56.6%, measured by the F-Measure with corresponding Recall of 72.2% and Precision of 46.6%. This performance is higher than those reported by Darnel et al. [4] for the same data set, when compared through a t-test with a significance level of 5%.  aBinding sites  aProtein-protein interactions  aCombination of classifiers  aHot spots  aInterações proteína-proteína  aPropriedades estruturais de proteínas  aPropriedades químicas de proteínas1 aTOZZI, C. L.