ProFPred: a two-step protein function prediction model based on sequence and evolutionary information

Ruiquan Ge, Guanwen Feng, Pu Wang, Qiguang Miao

Abstract: In post-genomic era, the understanding of protein function has been seriously behind the development of sequencing technology. Experimental verification for protein function is difficult, time consuming and expensive. Meanwhile, the new proteins vary in function. It is difficult for traditional methods to fully and accurately understand its functions. In this work, we present a novel method ProFPred to predict protein function based on sequence and evolutionary information to deal with small samples data corresponding to new diseases or discoveries. Experimental results demonstrate that our method can achieve better or comparable performances compared with current state-of-the-art methods.