  Identifying disease genes from human genome is an important and fundamental
problem in biomedical research. Despite many publications of machine learning
methods applied to discover new disease genes, it still remains a challenge
because of the pleiotropy of genes, the limited number of confirmed disease
genes among whole genome and the genetic heterogeneity of diseases. Recent
approaches have applied the concept of 'guilty by association' to investigate
the association between a disease phenotype and its causative genes, which
means that candidate genes with similar characteristics as known disease genes
are more likely to be associated with diseases. However, due to the imbalance
issues (few genes are experimentally confirmed as disease related genes within
human genome) in disease gene identification, semi-supervised approaches, like
label propagation approaches and positive-unlabeled learning, are used to
identify candidate disease genes via making use of unknown genes for training -
typically in the scenario of a small amount of confirmed disease genes (labeled
data) with a large amount of unknown genome (unlabeled data). The performance
of Disease gene prediction models are limited by potential bias of single
learning models and incompleteness and noise of single biological data sources,
therefore ensemble learning models are applied via combining multiple diverse
biological sources and learning models to obtain better predictive performance.
In this thesis, we propose three computational models for identifying candidate
disease genes.
