Premium
Recent progresses in the application of machine learning approach for predicting protein functional class independent of sequence similarity
Author(s) -
Han Lianyi,
Cui Juan,
Lin Honghuang,
Ji Zhiliang,
Cao Zhiwei,
Li Yixue,
Chen Yuzong
Publication year - 2006
Publication title -
proteomics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.26
H-Index - 167
eISSN - 1615-9861
pISSN - 1615-9853
DOI - 10.1002/pmic.200500938
Subject(s) - sequence (biology) , computer science , similarity (geometry) , complement (music) , protein function prediction , protein sequencing , machine learning , class (philosophy) , artificial intelligence , function (biology) , computational biology , cluster analysis , web server , bioinformatics , data mining , protein function , biology , peptide sequence , image (mathematics) , the internet , genetics , world wide web , complementation , gene , phenotype
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.