Predicting novel metabolic pathways through subgraph mining

Mining metabolic pathways through gene expression

MOTIVATION An observed metabolic response is the result of the coordinated activation and interaction between multiple genetic pathways. However, the complex structure of metabolism has meant that a compete understanding of which pathways are required to produce an observed metabolic response is not fully understood. In this article, we propose an approach that can identify the genetic pathways...

Classifying Android Malware through Subgraph Mining

Predicting novel pathways in genome-scale metabolic networks.

Elementary-modes analysis has become a well-established theoretical tool in metabolic pathway analysis. It allows one to decompose complex metabolic networks into the smallest functional entities, which can be interpreted as biochemical pathways. This analysis has, in medium-size metabolic networks, led to the successful theoretical prediction of hitherto unknown pathways. For illustration, we ...

Subgraph Mining

The amount of available data is increasing very fast. With this data, the desire for data mining is also growing. More and larger databases have to be searched to find interesting (and frequent) elements and connections between them. Most often the data of interest is very complex. It is common to model complex data with the help of graphs consisting of nodes and edges that are often labeled to...

Inference of pathways from metabolic networks by subgraph extraction

In this work, we present different algorithmic approaches to the inference of metabolic pathways from metabolic networks. Metabolic pathway inference can be applied to uncover the biological function of sets of co-expressed, enzyme-coding genes. We compare the kWalks algorithm based on random walks and an alternative approach relying on k-shortest paths. We study the influence of various parame...