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Genome-wide association study (GWAS), as one primary approach for genetic studies, has been successfully applied to a variety of complex diseases, leading to the discovery of substantial disease-associated loci. These discovered associations provide unprecedented opportunities for deepening our understanding of complex diseases, such as disease-associated risk variants, genes, and pathways. However,...
The emergence of high-throughput technologies leads to abundant protein-protein interaction (PPI) data and microarray gene expression profiles, and provides a great opportunity for the identification of novel protein complexes using computational methods. By combining these two types of data, we propose a novel Graph Fragmentation Algorithm (GFA) for protein complex identification. Adapted from a...
With the accelerating advancement of biomedical research, it has been widely accepted that genetic variation plays a critical role in the pathogenesis of human inherited diseases. As an important type of genetic variation, nonsynonymous single nucleotide polymorphisms (nsSNPs) that occur in protein coding regions lead to amino acid substitutions in proteins, affecting structures and functions of proteins,...
The combined use of gene expression profiles and protein-protein interaction (PPI) networks has recently shed light on breast cancer research by selecting a small number of subnetworks as disease markers and then using them for the classification of metastasis. Based on previously identified subnetwork markers, we compare three ensemble learning approaches (AdaBoost, LogitBoost and random forest)...
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