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The causation of cancer often involves the joint deregulation of multiple biological processes. Thus, it is interesting to extract multi-function features of cancer genes and study their functional coordination involved in tumorigenesis. Here, based on Gene Ontology, we proposed a heuristics strategy to extract multi-function features which are significantly overrepresented with cancer genes. We showed...
Since diseases might be related with each other, systematically assessing their relationships could provide us novel insight into their mechanisms. One of the most important methods to study diseases' relationships is to calculate their phenotype similarity scores based on the text and clinical synopsis parts of their records in the OMIM database. However, as demonstrated in this paper, the similarity...
Aging is a complex process associated with a number of molecular events at multidimensional levels. Understanding the characteristics of aging is important for elaborating the molecular mechanism of many diseases such as Alzheimer. In this paper, we systematically analyzed topological features of proteins encoded by human aging genes versus those encoded by non-aging genes in protein-protein interaction...
The Gene Ontology (GO) was widely used to annotate gene products, which provide a new way to analyze the relationship between the gene products. Many approaches based on GO were proposed to measure the functional similarity of gene products. In order to improve the accuracy, we proposed a novel method, MUI, to measure functional similarity of gene products combining the information of the interactions...
Selecting differentially expressed genes (DEGs) is one of the most important tasks in microarray applications. However, the sample sizes typically used in current cancer studies may only partially reflect the widely altered gene expressions in cancers. By analyzing three large cancer datasets, we show that, in each cancer, a wide range of functional modules are altered and have high disease classification...
Selecting feature genes for disease prediction is one of the most important applications of microarray technology. However, gene lists obtained in different studies for a same clinical type of patients often differ widely and have few genes in common. Recent researches suggest that gene lists ranked by fold change are more reproducible than by t-test. Here, based on the resampling method, we use training...
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