Abstract Summary The growing availability of biomolecular networks has led to a need for analysis methods that are able to extract biologically meaningful information from these complex data structures. Here we present MILES (MIning Labeled Enriched Subgraphs), a Java-based subgraph mining tool for discovering motifs that are associated to a given set of nodes of interest, such as a list of genes or proteins, in biomolecular networks. It provides a unique extension to the widely used enrichment analysis methodologies by integrating network structure and functional annotations in order to discern novel biological subgraphs which are enriched in the targets of interest. The tool can handle various types of input data, including (un)directed, (un)connected and multi-label networks, and is thus compatible with most types of biomolecular networks. Availability and implementation MILES is available as a platform-independent Java application at https://github.com/pmoris/miles-subgraph-miner alongside a user manual, example datasets and the source code. Supplementary information Supplementary data are available at Bioinformatics online.

MOTIVATION: The T-cell receptor (TCR) is responsible for recognizing epitopes presented on cell surfaces. Linking TCR sequences to their ability to target specific epitopes is currently an unsolved problem, yet one of great interest. Indeed, it is currently unknown how dissimilar TCR sequences can be before they no longer bind the same epitope. This question is confounded by the fact that there are many ways to define the similarity between two TCR sequences. Here we investigate both issues in the context of TCR sequence unsupervised clustering. RESULTS: We provide an overview of the performance of various distance metrics on two large independent datasets with 412 and 2835 TCR sequences respectively. Our results confirm the presence of structural distinct TCR groups that target identical epitopes. In addition, we put forward several recommendations to perform unsupervised T-cell receptor sequence clustering. AVAILABILITY AND IMPLEMENTATION: Source code implemented in Python 3 available at https://github.com/pmeysman/TCRclusteringPaper. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

The advent of social networks is one of the most exciting events in recent years. Its continuous development generated a huge information source which has been attracting attention of many researchers. Link prediction is an important problem in social network analysis. A link between two individuals is established base on many elements. Existing links give the individuals more chances to meet new people who can become their friends in future. In addition, personal features are necessary condition to form a stable link. Most present studies thoroughly mined network topology but used a little own information of users to recommend potential links. This affects to the quality of prediction. In this paper, we concentrate on the extraction of more features from user to improve the quality of predicted links. We proposed a measure calculating the similarity of individuals. The measure considers not only network topology but also the personal features. The higher similarity score, the more ability a new link appears. The experimentation shows that the simple combination of information as above brings considerable results.