Recent progresses in the application of machine learning approach for predicting protein functional class independent of sequence similarity.
Protein sequence contains clues to its function. Functional prediction from sequence presents a challenge particularly for proteins that have low or no sequence similarity to proteins of known function. Recently, machine learning methods have been explored for predicting functional class of proteins from sequence-derived properties independent of sequence similarity, which showed promising potential for low- and non-homologous proteins. These methods can thus be explored as potential tools to complement alignment- and clustering-based methods for predicting protein function. This article reviews the strategies, current progresses, and underlying difficulties in using machine learning methods for predicting the functional class of proteins. The relevant software and web-servers are described. The reported prediction performances in the application of these methods are also presented, which need to be interpreted with caution as they are dependent on such factors as datasets used and choice of parameters.
Han
Lianyi
L
Department of Computational Science, National University of Singapore, Singapore, Singapore.
Cui
Juan
J
Lin
Honghuang
H
Ji
Zhiliang
Z
Cao
Zhiwei
Z
Li
Yixue
Y
Chen
Yuzong
Y
D000465:Algorithms; D001185:Artificial Intelligence; D019295:Computational Biology / Q000639:trends*; D030562:Databases, Protein; D016571:Neural Networks, Computer; D011506:Proteins / Q000737:chemistry / Q000145:classification* / Q000502:physiology*; D020539:Sequence Analysis, Protein / Q000379:methods*
D011506:Proteins