Combining phylogenetic profiling-based and machine learning-based techniques to predict functional related proteins.
Annotating protein functions and linking proteins with similar functions are important in systems biology. The rapid growth rate of newly sequenced genomes calls for the development of computational methods to help experimental techniques. Phylogenetic profiling (PP) is a method that exploits the evolutionary co-occurrence pattern to identify functional related proteins. However, PP-based methods delivered satisfactory performance only on prokaryotes but not on eukaryotes. This study proposed a two-stage framework to predict protein functional linkages, which successfully enhances a PP-based method with machine learning. The experimental results show that the proposed two-stage framework achieved the best overall performance in comparison with three PP-based methods.
Department of Electrical Engineering, National Cheng Kung University, Tainan, Taiwan.
Tzu-Wen
TW
Lin
Jian-Wei
JW
Wu
Darby Tien-Hao
DT
Chang
D000465:Algorithms; D019540:Area Under Curve; D001185:Artificial Intelligence; D019295:Computational Biology / Q000379:methods; D004926:Escherichia coli / Q000235:genetics / Q000378:metabolism; D056890:Eukaryota / Q000235:genetics / Q000378:metabolism; D058977:Molecular Sequence Annotation; D010802:Phylogeny; D011506:Proteins / Q000737:chemistry / Q000235:genetics* / Q000378:metabolism*; D015203:Reproducibility of Results; D012441:Saccharomyces cerevisiae / Q000235:genetics / Q000378:metabolism
D011506:Proteins