BACKGROUND: Interpreting non-targeted metabolomics data remains a challenging task. Signals from non-targeted metabolomics studies stem from a combination of biological causes, complex interactions between them and experimental bias/noise. The resulting data matrix usually contain huge number of variables and only few samples, and classical techniques using nonlinear mapping could result in computational complexity and overfitting. Independent Component Analysis (ICA) as a linear method could potentially bring more meaningful results than Principal Component Analysis (PCA). However, a major problem with most ICA algorithms is the output variations between different runs and the result of a single ICA run should be interpreted with reserve. RESULTS: ICA was applied to simulated and experimental mass spectrometry (MS)-based non-targeted metabolomics data, under the hypothesis that underlying sources are mutually independent. Inspired from the Icasso algorithm, a new ICA method, MetICA was developed to handle the instability of ICA on complex datasets. Like the original Icasso algorithm, MetICA evaluated the algorithmic and statistical reliability of ICA runs. In addition, MetICA suggests two ways to select the optimal number of model components and gives an order of interpretation for the components obtained. CONCLUSIONS: Correlating the components obtained with prior biological knowledge allows understanding how non-targeted metabolomics data reflect biological nature and technical phenomena. We could also extract mass signals related to this information. This novel approach provides meaningful components due to their independent nature. Furthermore, it provides an innovative concept on which to base model selection: that of optimizing the number of reliable components instead of trying to fit the data. The current version of MetICA is available at https://github.com/daniellyz/MetICA.

The Internet and distributed object technologies have made it possible for different business enterprises to draw upon the best of their resources for conducting joint business as a virtual e-enterprise (VEE). To enable virtual e-enterprises, the integration of legacy applications and the modeling and enactment of concurrent business processes are necessary. The authors combine the features of the messaging approach and the distributed object approach to system integration. Business Object Documents (BOD) are used for transmitting business operations and data among application systems. Message transmission is supported by two underlying communication infrastructures: CORBA and Java RMI. The separation of messaging from communication infrastructure allows the underlying infrastructure to be changed without impacting application systems. Also, business processes are modeled as sequences or network structures of BOD transmissions. The process models are replicated at all sites and used by an extended information infrastructure to enable distributed, concurrent enactment of processes.

With the emergence of e-business as the next killer application for the Web, automating bargaining-type negotiations between clients (i.e., buyers and sellers) has become increasingly important. With IDEAL (Internet based Dealmaker for e-business), we have developed an architecture and framework, including a negotiation protocol, for automated negotiations among multiple IDEAL servers. The main components of IDEAL are a constraint satisfaction processor (CSP) to evaluate a proposal, an Event-Trigger-Rule (ETR) server for managing and triggering the execution of rules which make up the negotiation strategy (rules can be updated at run-time to deal with the dynamic nature of negotiations), and a cost-benefit analysis to help in the selection of alternative strategies. We have implemented a fully functional prototype system of IDEAL to demonstrate automated negotiations among buyers and suppliers participating in a supply chain.