  Most network-based protein (or gene) function prediction methods are based on
the assumption that the labels of two adjacent proteins in the network are
likely to be the same. However, assuming the pairwise relationship between
proteins or genes is not complete, the information a group of genes that show
very similar patterns of expression and tend to have similar functions (i.e.
the functional modules) is missed. The natural way overcoming the information
loss of the above assumption is to represent the gene expression data as the
hypergraph. Thus, in this paper, the three un-normalized, random walk, and
symmetric normalized hypergraph Laplacian based semi-supervised learning
methods applied to hypergraph constructed from the gene expression data in
order to predict the functions of yeast proteins are introduced. Experiment
results show that the average accuracy performance measures of these three
hypergraph Laplacian based semi-supervised learning methods are the same.
However, their average accuracy performance measures of these three methods are
much greater than the average accuracy performance measures of un-normalized
graph Laplacian based semi-supervised learning method (i.e. the baseline method
of this paper) applied to gene co-expression network created from the gene
expression data.
