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Looking at Cerebellar Malformations through Text-Mined Interactomes of Mice and Humans

Overview of attention for article published in PLoS Computational Biology, November 2009
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Title
Looking at Cerebellar Malformations through Text-Mined Interactomes of Mice and Humans
Published in
PLoS Computational Biology, November 2009
DOI 10.1371/journal.pcbi.1000559
Pubmed ID
Authors

Ivan Iossifov, Raul Rodriguez-Esteban, Ilya Mayzus, Kathleen J. Millen, Andrey Rzhetsky

Abstract

We have generated and made publicly available two very large networks of molecular interactions: 49,493 mouse-specific and 52,518 human-specific interactions. These networks were generated through automated analysis of 368,331 full-text research articles and 8,039,972 article abstracts from the PubMed database, using the GeneWays system. Our networks cover a wide spectrum of molecular interactions, such as bind, phosphorylate, glycosylate, and activate; 207 of these interaction types occur more than 1,000 times in our unfiltered, multi-species data set. Because mouse and human genes are linked through an orthological relationship, human and mouse networks are amenable to straightforward, joint computational analysis. Using our newly generated networks and known associations between mouse genes and cerebellar malformation phenotypes, we predicted a number of new associations between genes and five cerebellar phenotypes (small cerebellum, absent cerebellum, cerebellar degeneration, abnormal foliation, and abnormal vermis). Using a battery of statistical tests, we showed that genes that are associated with cerebellar phenotypes tend to form compact network clusters. Further, we observed that cerebellar malformation phenotypes tend to be associated with highly connected genes. This tendency was stronger for developmental phenotypes and weaker for cerebellar degeneration.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 42 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 2 5%
Korea, Republic of 1 2%
Germany 1 2%
Canada 1 2%
Unknown 37 88%

Demographic breakdown

Readers by professional status Count As %
Researcher 14 33%
Student > Ph. D. Student 13 31%
Other 2 5%
Student > Postgraduate 2 5%
Student > Master 2 5%
Other 5 12%
Unknown 4 10%
Readers by discipline Count As %
Agricultural and Biological Sciences 23 55%
Computer Science 5 12%
Biochemistry, Genetics and Molecular Biology 3 7%
Medicine and Dentistry 3 7%
Neuroscience 2 5%
Other 3 7%
Unknown 3 7%