MOTIVATION: High-throughput omics methods increasingly result in large datasets including metabolomics data, which are often difficult to analyse. RESULTS: To help researchers to handle and analyse those datasets by mapping and investigating metabolomics data of multiple sampling conditions (e.g. different time points or treatments) in the context of pathways, PathwayNexus has been developed, which presents the mapping results in a matrix format, allowing users to easily observe the relations between the compounds and the pathways. It also offers functionalities like ranking, sorting, clustering, pathway views, and further analytical tools. Its primary objective is to condense large sets of pathways into smaller, more relevant subsets that align with the specific interests of the user. AVAILABILITY AND IMPLEMENTATION: The methodology presented here is implemented in PathwayNexus, an open-source add-on for Vanted available at www.cls.uni-konstanz.de/software/pathway-nexus. CONTACT: [email protected]. SUPPLEMENTARY INFORMATION: Website: www.cls.uni-konstanz.de/software/pathway-nexus.

A vast amount of toxicological data can be obtained from feature analysis of cells treated in vitro. However, this requires microscopic image segmentation of cells. To this end, we propose a new strategy, namely Supervised Normalized Cut Segmentation (SNCS), to segment cells that partially overlap and have a large amount of curved edges. SNCS approach is a machine learning based method, where loosely annotated images are used first to train and optimise parameters, and then the optimal parameters are inserted into a Normalized Cut segmentation process. Furthermore, we compare our segmentation results using SNCS to another four classical and two state-of-the-art methods. The overall experimental result shows the usefulness and effectiveness of our method over the six comparison methods.