MOTIVATION: The rise of transformer-based architectures has dramatically improved our ability to analyze natural language. However, the power and flexibility of these general-purpose models come at the cost of highly complex model architectures with billions of parameters that are not always needed. RESULTS: In this work, we present CSpace: a concise word embedding of biomedical concepts that outperforms all alternatives in terms of out-of-vocabulary ratio and semantic textual similarity task, and has comparable performance with respect to transformer-based alternatives in the sentence similarity task. This ability can serve as the foundation for semantic search by enabling efficient retrieval of conceptually related terms. Additionally, CSpace incorporates ontological identifiers (MeSH, NCBI gene and taxonomy IDs), enabling computationally efficient disease, gene or condition relatedness measurement, potentially unlocking previously unknown disease-condition associations. AVAILABILITY AND IMPLEMENTATION: Full and compressed models are available on Zenodo at https://doi.org/10.5281/zenodo.14781672, while training code, examples, interactive visualizations and experiments are available at https://doi.org/10.5281/zenodo.15125706 and on the GitHub repository.

MOTIVATION: Precision medicine is a promising field that proposes, in contrast to a one-size-fits-all approach, the tailoring of medical decisions, treatments or products. In this context, it is crucial to introduce innovative methods to stratify a population of patients on the basis of an accurate system-level knowledge of the disease. This is particularly important in very challenging conditions, where the use of standard statistical methods can be prevented by poor data availability or by the need of oversimplifying the processes regulating a complex disease. RESULTS: We define an innovative method for phenotype classification that combines experimental data and a mathematical description of the disease biology. The methodology exploits the mathematical model for inferring additional subject features relevant for the classification. Finally, the algorithm identifies the optimal number of clusters and classifies the samples on the basis of a subset of the features estimated during the model fit. We tested the algorithm in two test cases: an in silico case in the context of dyslipidemia, a complex disease for which a large population of patients has been generated, and a clinical test case, in the context of a lysosomal rare disorder, for which the amount of available data was limited. In both the scenarios, our methodology proved to be accurate and robust, and allowed the inference of an additional phenotype division that the experimental data did not show. AVAILABILITY AND IMPLEMENTATION: The code to reproduce the in silico results has been implemented in MATLAB v.2017b and it is available in the Supplementary Material. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

In this paper we analyze the community of a social network site, Advogato. The peculiar characteristics of Advogato is that users can explicitly express weighted trust relationships among themselves. We conduct a longitudinal analysis of the trust network over a time period of 4 years, exploring the community as it grew from a knit circle of 300 users to an society of almost 6500 individuals. We report the changes over time of standard indexes in social network analysis such as clustering and degrees of separation. We then focus on specific measures about trust such as reciprocity and changes over time of average trust. A decline in trust is observed as the community grows. Following what we believe to be the first empirical analysis of trust evolution over time in a real community, we conclude suggesting how the availability of data about human relationships in social network sites is opening up the possibility of monitoring changes in trust in real time. In order to foster this research line, we released the datasets and the code we used in our analysis.