A protein secondary structure prediction framework based on the Extreme Learning Machine

Abstract

In this paper we propose an Extreme Learning Machine (ELM) based protein secondary structure prediction framework which can provide good performance at extremely fast speed. To achieve better performance, in this framework: (i) the three secondary structures are independently predicted by a binary ELM classifier first; (ii) a probability based combination (PBC) method is then proposed to combine these binary prediction results into the expected three-classification results and (iii) a helix postprocessing (HPP) method is finally proposed to further improve the overall performance of the framework based on biological features. Experiments conducted on the real data sets CB513 and RS126 demonstrate that our algorithm can achieve as good prediction accuracy as other popular methods; however, at very fast learning speed.