MOTIVATION: Gene-disease associations are fundamental for understanding disease etiology and developing effective interventions and treatments. Identifying genes not yet associated with a disease due to a lack of studies is a challenging task in which prioritization based on prior knowledge is an important element. The computational search for new candidate disease genes may be eased by positive-unlabeled learning, the machine learning (ML) setting in which only a subset of instances are labeled as positive while the rest of the dataset is unlabeled. In this work, we propose a set of effective network-based features to be used in a novel Markov diffusion-based multi-class labeling strategy for putative disease gene discovery. RESULTS: The performances of the new labeling algorithm and the effectiveness of the proposed features have been tested on 10 different disease datasets using three ML algorithms. The new features have been compared against classical topological and functional/ontological features and a set of network- and biological-derived features already used in gene discovery tasks. The predictive power of the integrated methodology in searching for new disease genes has been found to be competitive against state-of-the-art algorithms. AVAILABILITY AND IMPLEMENTATION: The source code of NIAPU can be accessed at https://github.com/AndMastro/NIAPU. The source data used in this study are available online on the respective websites. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Abstract The evolution of the vocabulary of a language is characterized by two different random processes: abrupt lexical replacements, when a complete new word emerges to represent a given concept (which was at the basis of the Swadesh foundation of glottochronology in the 1950s), and gradual lexical modifications that progressively alter words over the centuries, considered here in detail for the first time. The main discriminant between these two processes is their impact on cognacy within a family of languages or dialects, since the former modifies the subsets of cognate terms and the latter does not. The automated cognate detection, which is here performed following a new approach inspired by graph theory, is a key preliminary step that allows us to later measure the effects of the slow modification process. We test our dual approach on the family of Malagasy dialects using a cladistic analysis, which provides strong evidence that lexical replacements and gradual lexical modifications are two random processes that separately drive the evolution of languages.

Antimicrobial resistance (AMR) is a major public health problem of the 21st century. The ability of some bacteria to develop resistance to specific antibiotics is the cause of an increased morbidity, mortality and health expenditure. Several surveillance programms have been introduced in the last decades to monitor the spread of antimicrobial resistance. The present work has been conducted within the JPIAMR-project MAGIcIAN whose aim is to support the sustainable introduction of novel class and last-resort antimicrobial drugs minimising the emergence of AMR. Within this project we developed a multi-level model to describe the spread of the sexually transmitted disease of gonorrhoea, caused by the Neisseria gonorrhoeae bacterium, a multidrug resistant bacteria who has progressively developed resistance to many treatment options. The multi-level model includes a dynamic sexual contact network, that describes the dynamic of sexual partnerships, a transmission model that describes the probability of infection during intercourse, and a within-host model, that describes the dynamic of gonorrhoea infection within an individual. The novelty of the proposed model is in including communities having different sexual orientations and behaviour and the possibility of these communities to interact in a dynamic framework. In this work, we calibrate the model using data coming from several clinics located in Amsterdam.

As far as we know, three non-mutually exclusive reasons have been given to account for the complex scaling behaviour observed in IP traffic on small time scales, namely: data fragmentation due to protocol layering; complex interactions in the network between traffic flows and data and storage elements; the TCP mechanism. Focusing on the TCP mechanism, we simulated the most important features of a single TCP source and applied a multifractal scaling analysis to the packet traces injected into the network. No competing sources affected the time spacing between packets and therefore the TCP source could feed the network according to its own mechanism. On the basis of our simulation results we were able to conclude that the TCP mechanism alone produces two different behaviours in the outgoing traffic, depending on the time scales: at small time scales it displays a multifractal behaviour, even if it was not present in the original series of packet delays. At larger time scales, the outgoing traffic shows a self-similar behaviour with only one Holder exponent.