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What is Warm start

Handbook of Research on Computational Methodologies in Gene Regulatory Networks
optimization is a starting of the optimization algorithm in a state where it is close to the optimum.
Published in Chapter:
Integrating Various Data Sources for Improved Quality in Reverse Engineering of Gene Regulatory Networks
Mika Gustafsson (Linköping University, Sweden) and Michael Hörnquist (Linköping University, Sweden)
DOI: 10.4018/978-1-60566-685-3.ch020
In this chapter we outline a methodology to reverse engineer GRNs from various data sources within an ODE framework. The methodology is generally applicable and is suitable to handle the broad error distribution present in microarrays. The main effort of this chapter is the exploration of a fully data driven approach to the integration problem in a “soft evidence” based way. Integration is here seen as the process of incorporation of uncertain a priori knowledge and is therefore only relied upon if it lowers the prediction error. An efficient implementation is carried out by a linear programming formulation. This LP problem is solved repeatedly with small modifications, from which we can benefit by restarting the primal simplex method from nearby solutions, which enables a computational efficient execution. We perform a case study for data from the yeast cell cycle, where all verified genes are putative regulators and the a priori knowledge consists of several types of binding data, text-mining and annotation knowledge.
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