We thank Joel Parker for providing the algorithm for iterative SigClust analysis.
Without loss of generality, we suggest a simple and well-known algorithm for providing numerical examples for the analysis performed in this article.
We are grateful to the authors of the competitive algorithms for providing their code for the experimental study.
We perform rigorous performance optimization of the underlying algorithms for providing instant suggestions to the user.
We then discuss policies for tuning these algorithms to provide the highest reliability, for example by changing the order in which sites are contacted and offered trades.
The MetAssign algorithm thus provides the basis for the modular development of a general probabilistic framework for the interpretation of LC MS data in metabolomics.
The model is based on a flexible cognitive algorithm that provides the framework for an adaptive movement kernel.
The first analysis was focused on the identification of which levenshtein values (for the clustering algorithm) provide the best results for the mining process.
Algorithm 1 provides the pseudo-code for a conventional coarse-fine sensing implementation.
Algorithm 1 provides the pseudo code for ASModel.
XL developed the MAP algorithm and provided the bioinformatics support for the analyses of the ChIP-chip data.
