Bedoukian   RussellIPM   RussellIPM   Piezoelectric Micro-Sprayer


Home
Animal Taxa
Plant Taxa
Semiochemicals
Floral Compounds
Semiochemical Detail
Semiochemicals & Taxa
Synthesis
Control
Invasive spp.
References

Abstract

Guide

Alphascents
Pherobio
InsectScience
E-Econex
Counterpart-Semiochemicals
Print
Email to a Friend
Kindly Donate for The Pherobase

« Previous AbstractEvaluation of Pheromone Trap Devices for the Capture of Thaumetopoea pityocampa (Lepidoptera: Thaumetopoeidae) in Southern Europe    Next AbstractChanges in physicochemical characteristics and volatile constituents of yellow- and white-fleshed nectarines during maturation and artificial ripening »

Mol Biosyst


Title:iPoint: an integer programming based algorithm for inferring protein subnetworks
Author(s):Atias N; Sharan R;
Address:"Blavatnik School of Computer Science, Tel Aviv University, Tel Aviv 69978, Israel"
Journal Title:Mol Biosyst
Year:2013
Volume:20130206
Issue:7
Page Number:1662 - 1669
DOI: 10.1039/c3mb25432a
ISSN/ISBN:1742-2051 (Electronic) 1742-2051 (Linking)
Abstract:"Large scale screening experiments have become the workhorse of molecular biology, producing data at an ever increasing scale. The interpretation of such data, particularly in the context of a protein interaction network, has the potential to shed light on the molecular pathways underlying the phenotype or the process in question. A host of approaches have been developed in recent years to tackle this reconstruction challenge. These approaches aim to infer a compact subnetwork that connects the genes revealed by the screen while optimizing local (individual path lengths) or global (likelihood) aspects of the subnetwork. Yosef et al. [Mol. Syst. Biol., 2009, 5, 248] were the first to provide a joint optimization of both criteria, albeit approximate in nature. Here we devise an integer linear programming formulation for the joint optimization problem, allowing us to solve it to optimality in minutes on current networks. We apply our algorithm, iPoint, to various data sets in yeast and human and evaluate its performance against state-of-the-art algorithms. We show that iPoint attains very compact and accurate solutions that outperform previous network inference algorithms with respect to their local and global attributes, their consistency across multiple experiments targeting the same pathway, and their agreement with current biological knowledge"
Keywords:"*Algorithms Computational Biology/methods Humans Huntington Disease/genetics/metabolism Models, Biological Pheromones/metabolism *Programming, Linear Protein Interaction Mapping/*methods Reproducibility of Results Signal Transduction Yeasts/genetics/metab;"
Notes:"MedlineAtias, Nir Sharan, Roded eng Research Support, Non-U.S. Gov't England 2013/02/07 Mol Biosyst. 2013 Jul; 9(7):1662-9. doi: 10.1039/c3mb25432a. Epub 2013 Feb 6"

 
Back to top
 
Citation: El-Sayed AM 2024. The Pherobase: Database of Pheromones and Semiochemicals. <http://www.pherobase.com>.
© 2003-2024 The Pherobase - Extensive Database of Pheromones and Semiochemicals. Ashraf M. El-Sayed.
Page created on 27-12-2024