Data Integration in the Life Sciences: 8th International by James M. Ostell (auth.), Olivier Bodenreider, Bastien Rance

By James M. Ostell (auth.), Olivier Bodenreider, Bastien Rance (eds.)

This e-book constitutes the refereed complaints of the eighth foreign convention on facts Integration within the existence Sciences, DILS 2012, held in collage Park, MD, united states, on June 28-29, 2012. The eleven revised papers incorporated during this quantity have been rigorously reviewed and chosen. The papers hide the next subject matters: foundations of knowledge integration, new paradigms for facts integration, and integrating medical data.

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By James M. Ostell (auth.), Olivier Bodenreider, Bastien Rance (eds.)

This e-book constitutes the refereed complaints of the eighth foreign convention on facts Integration within the existence Sciences, DILS 2012, held in collage Park, MD, united states, on June 28-29, 2012. The eleven revised papers incorporated during this quantity have been rigorously reviewed and chosen. The papers hide the next subject matters: foundations of knowledge integration, new paradigms for facts integration, and integrating medical data.

Show description

Read or Download Data Integration in the Life Sciences: 8th International Conference, DILS 2012, College Park, MD, USA, June 28-29, 2012. Proceedings PDF

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This abundance of data has led to an era of comparative genomics, in which genes can be compared across diverse taxa to provide insights into evolutionary similarities as well as key divergences. Already the study of genes in Arabidopsis has helped to inform human research and vice versa. Increasingly, every new genome must be understood in light of previously sequenced and analyzed genomes. We will consider orthologous genes from three model organisms: Saccharomyces cerevisiae (yeast), Caenorhabditis elegans (nematode) and Drosophila melanogaster (fruit fly).

With more than 3 layers, however, the patterns become more difficult to comprehend. , candidate subgraphs, that can lead to valuable patterns. We start with the premise that an area of the graph that is rich or dense with annotations is an interesting region to identify candidate subgraphs. For example, for a set of genes, if each is annotated with a set of GO terms and/or a set of PO terms, then the set of genes and GO terms, or the set of genes and PO terms, form a clique. We thus exploit cliques, or dense subgraphs (DSG) representing cliques with missing edges.

21–36, 2012. c Springer-Verlag Berlin Heidelberg 2012 22 J. Benik et al. Fig. 1. GO and PO annotations for gene CRY2 (middle); GO fragment (right); Graph Summary(GS) for genes CRY2 and PHOT1 (inset) Over the past 25 years, knowledge of the Arabidopsis genome has increased exponentially, together with that of other model organisms. This abundance of data has led to an era of comparative genomics, in which genes can be compared across diverse taxa to provide insights into evolutionary similarities as well as key divergences.

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