Efficient Ensemble Schemes for Protein Secondary Structure Prediction

Abstract

This paper proposes an efficient ensemble system to tackle the protein secondary structure prediction problem with neural networks as base classifiers. The experimental results show that the multi-layer system can lead to better results. When deploying more accurate classifiers, the higher accuracy of the ensemble system can be obtained.

Prediction of β-Hairpins in Proteins Using Physicochemical Properties and Structure Information

“…Hence, there is a great need for accurate protein structure prediction methods to bridge the gap [2][3][4][5][6][7]. In proteins, protein secondary structure elements are observed to associate in a particular geometric arrangement to form supersecondary structures.…”