Work

Automatic Parameter Learning for Multiple Local Network Alignment

Year: 2009

Type: article

Abstract: We developed Graemlin 2.0, a new multiple network aligner with (1) a new multi-stage approach to local network alignment; (2) a novel scoring function that can use arbitrary features of a multiple network alignment, such as protein deletions, protein duplications, protein mutations, and interaction losses; (3) a parameter learning algorithm that uses a training set of known network alignments to learn parameters for our scoring function and thereby adapt it to any set of networks; and (4) an algorithm that uses our scoring function to find approximate multiple network alignments in linear time. We tested Graemlin 2.0's accuracy on protein interaction networks from IntAct, DIP, and the Stanford Network Database. We show that, on each of these datasets, Graemlin 2.0 has higher sensitivity and specificity than existing network aligners. Graemlin 2.0 is available under the GNU public license at http://graemlin.stanford.edu .

Source: Journal of Computational Biology

DOI: 10.1089/cmb.2009.0099

Authors: Jason Flannick, Antal Novak, Chuong B. Do, Balaji S. Srinivasan, Serafim Batzoglou

Institution: Stanford University

Language: English

Topic: Bioinformatics and Genomic Networks

Subfield: Molecular Biology

Field: Biochemistry, Genetics and Molecular Biology

Domain: Life Sciences

Open Access status: green

Funders: National Science Foundation, National Institutes of Health, U.S. Public Health Service, U.S. National Library of Medicine

Awards: G3219800225, G5155140671, EMSW21-VIGRE: Vertical Integration of Mathematics, Statistics and Applied Mathematics., Protein Interaction Networks: Integration and Alignment, Biomedical Informatics Training Program at Stanford