FBS Linkage ontology and technique to support engineering change management

© 2014, Springer-Verlag London. Engineering changes are essential for any product development, and their management has become a crucial discipline. Research in engineering change management has brought about some methods and tools to support dealing with changes. This work extends the change prediction method through incorporation of a function–behaviour–structure (FBS) scheme. These additional levels of detail provide the rationales for change propagation and allow a more proactive management of changes. First, we develop the ontology of this method based on a comprehensive comparison of three seminal functional reasoning schemes. Then, we demonstrate the FBS Linkage technique by applying it to a diesel engine. Finally, we evaluate the method.

Gene function prediction from synthetic lethality networks via ranking on demand.

MOTIVATION: Synthetic lethal interactions represent pairs of genes whose individual mutations are not lethal, while the double mutation of both genes does incur lethality. Several studies have shown a correlation between functional similarity of genes and their distances in networks based on synthetic lethal interactions. However, there is a lack of algorithms for predicting gene function from synthetic lethality interaction networks. RESULTS: In this article, we present a novel technique called kernelROD for gene function prediction from synthetic lethal interaction networks based on kernel machines. We apply our novel algorithm to Gene Ontology functional annotation prediction in yeast. Our experiments show that our method leads to improved gene function prediction compared with state-of-the-art competitors and that combining genetic and congruence networks leads to a further improvement in prediction accuracy.