Learning directed-acyclic-graphs from large-scale double-knockout experiments | IEEE Conference Publication | IEEE Xplore

Learning directed-acyclic-graphs from large-scale double-knockout experiments

Publisher: IEEE

Abstract:

In this paper we consider the problem of learning the genetic-interaction-map, i.e., the topology of a directed acyclic graph (DAG) of genetic interactions from noisy dou...View more

Abstract:

In this paper we consider the problem of learning the genetic-interaction-map, i.e., the topology of a directed acyclic graph (DAG) of genetic interactions from noisy double knockout (DK) data. Based on a set of well established biological interaction models we detect and classify the interactions between genes. Furthermore, we propose a novel linear integer optimization framework called Genetic-Interactions-Detector (GENIE) to identify the complex biological dependencies between genes and to compute the DAG topology that matches the DK measurements best where we make use of the well known branch-and-bound (BB) principle. Finally, we show via numeric simulations that the GENIE framework clearly outperforms the conventional techniques.
Date of Conference: 29 August 2016 - 02 September 2016
Date Added to IEEE Xplore: 01 December 2016
ISBN Information:
Electronic ISSN: 2076-1465
Publisher: IEEE
Conference Location: Budapest, Hungary

References

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