Protecting the privacy of human subjects.
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Biomedical subjects
Publications and source records attributed to Bernardo A Huberman.
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We present a method for creating a network of gene co-occurrences from the literature and partitioning it into communities of related genes. The way in which our method identifies communities makes it likely that the component genes of each community will be related by their function. The method processes a large database of article abstracts, synthesizing information from many sources to shed light on groups of genes that have been shown to interact. It is a tool to be used by researchers in the biomedical sciences to swiftly search for known interactions and to provide insight into unexplored connections. The partitioning procedure is designed to be particularly applicable to large networks in which individual nodes may play a role in more than one community. In this paper, we explain the details of the method, in particular the partitioning process. We also apply the method to produce communities of genes related to colon cancer and show that the results are useful.
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We present a statistical method that can swiftly identify, from the literature, sets of genes known to be associated with given diseases. It offers a comprehensive way to treat alias symbols, a statistical method for computing the relevance of the gene to the query, and a novel way to disambiguate gene symbols from other abbreviations. The method is illustrated by finding genes related to breast cancer.