MOTIVATION: The identification of predictive biomarker signatures from omics and multi-omics data for clinical applications is an active area of research. Recent developments in assay technologies and machine learning (ML) methods have led to significant improvements in predictive performance. However, most high-performing ML methods suffer from complex architectures and lack interpretability. RESULTS: We present the application of a novel symbolic-regression-based algorithm, the QLattice, on a selection of clinical omics datasets. This approach generates parsimonious high-performing models that can both predict disease outcomes and reveal putative disease mechanisms, demonstrating the importance of selecting maximally relevant and minimally redundant features in omics-based machine-learning applications. The simplicity and high-predictive power of these biomarker signatures make them attractive tools for high-stakes applications in areas such as primary care, clinical decision-making and patient stratification. AVAILABILITY AND IMPLEMENTATION: The QLattice is available as part of a python package (feyn), which is available at the Python Package Index (https://pypi.org/project/feyn/) and can be installed via pip. The documentation provides guides, tutorials and the API reference (https://docs.abzu.ai/). All code and data used to generate the models and plots discussed in this work can be found in https://github.com/abzu-ai/QLattice-clinical-omics. SUPPLEMENTARY INFORMATION: Supplementary material is available at Bioinformatics online.

A significant contribution of ICT to healthcare is constituted by systems automating and enhancing the management of research and clinical data. More specifically, PACS (Picture Archiving ad Communication Systems) have improved the efficiency of diagnostic images and clinical data management. Their evolution (image biobanks) are now enabling new collaborations and analysis possibilities similarly to—and beyond—the biobanks (their biologic samples analogous and complement). In this work we describe and evaluate the network performance of a biobanking system for diagnostic images, based on the XNAT open source platform, as implemented and operated by Bio Check Up Srl. The point of view of the user is adopted, in assessing the performance in three setups: local (virtual machines communicating in a single host), LAN (organization-local access), and VPN (remote secure access through the Internet). Both upload and download usage cases are considered, with both a medium-sized and big-sized set of diagnostic images. Several metrics are extracted from traffic traces captured in the experimental campaign, and discussed. Results show that the current setup is well provisioned for satisfying the planned number of concurrent users, and point to further experimental campaigns.

Intrinsic disorder is more abundant in eukaryotic than prokaryotic proteins.Methods predicting intrinsic disorder are based on the amino acid sequence of a protein.Therefore, there must exist an underlying difference in the sequences between eukaryotic and prokaryotic proteins causing the (predicted) difference in intrinsic disorder.By comparing proteins, from complete eukaryotic and prokaryotic proteomes, we show that the difference in intrinsic disorder emerges from the linker regions connecting Pfam domains.Eukaryotic proteins have more extended linker regions, and in addition, the eukaryotic linkers are significantly more disordered, 38% vs. 12-16% disordered residues.Next, we examined the underlying reason for the increase in disorder in eukaryotic linkers, and we found that the changes in abundance of only three amino acids cause the increase.Eukaryotic proteins contain 8.6% serine; while prokaryotic proteins have 6.5%, eukaryotic proteins also contain 5.4% proline and 5.3% isoleucine compared with 4.0% proline and � 7.5% isoleucine in the prokaryotes.All these three differences contribute to the increased disorder in eukaryotic proteins.It is tempting to speculate that the increase in serine frequencies in eukaryotes is related to regulation by kinases, but direct evidence for this is lacking.The differences are observed in all phyla, protein families, structural regions and type of protein but are most pronounced in disordered and linker regions.The observation that differences in the abundance of three amino acids cause the difference in disorder between eukaryotic and prokaryotic proteins raises the question: Are amino acid frequencies different in eukaryotic linkers because the linkers are more disordered or do the differences cause the increased disorder? Author SummaryIntrinsic disorder is essential for various functions in eukaryotic cells and is a signature of eukaryotic proteins.Here, we try to understand the origin of the difference in disorder between eukaryotic and prokaryotic proteins.We show that eukaryotic proteins contain more extended linker regions and that these linker regions are significantly more disordered.Further, we show, for the first time, that the difference in disorder originates from a systematic difference in amino acid frequencies between eukaryotic and prokaryotic

MOTIVATION: Knowledge of the correct protein subcellular localization is necessary for understanding the function of a protein. Unfortunately large-scale experimental studies are limited in their accuracy. Therefore, the development of prediction methods has been limited by the amount of accurate experimental data. However, recently large-scale experimental studies have provided new data that can be used to evaluate the accuracy of subcellular predictions in human cells. Using this data we examined the performance of state of the art methods and developed SubCons, an ensemble method that combines four predictors using a Random Forest classifier. RESULTS: SubCons outperforms earlier methods in a dataset of proteins where two independent methods confirm the subcellular localization. Given nine subcellular localizations, SubCons achieves an F1-Score of 0.79 compared to 0.70 of the second best method. Furthermore, at a FPR of 1% the true positive rate (TPR) is over 58% for SubCons compared to less than 50% for the best individual predictor. AVAILABILITY AND IMPLEMENTATION: SubCons is freely available as a webserver (http://subcons.bioinfo.se) and source code from https://bitbucket.org/salvatore_marco/subcons-web-server. The golden dataset as well is available from http://subcons.bioinfo.se/pred/download. CONTACT: [email protected]. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Cerebral vein analysis provides a chance to study, from an unusual viewpoint, an entire class of brain diseases, including neurodegenerative disorders and traumatic brain injuries. Manual segmentation approaches can be used to assess vascular anatomy, but they are observer-dependent and time-consuming; therefore, automated approaches are desirable, as they also improve reproducibility. In this paper, a new, fully automated algorithm, based on structural, morphological, and relaxometric information, is proposed to segment the entire cerebral venous system from MR images. The algorithm for multi-parametric automated segmentation of brain VEiNs (MAVEN) is based on a combined investigation of multi-parametric information that allows for rejection of false positives and detection of thin vessels. The method is tested on gradient echo brain data sets acquired at 1.5, 3, and 7 T. It is compared to previous methods against manual segmentation, and its inter-scan reproducibility is assessed. The achieved accuracy and reproducibility are good, meaning that MAVEN outperforms previous methods on both quantitative and qualitative analyses. It is usable at all the field strengths explored, showing comparable accuracy scores, with no need for algorithm parameter adjustments, and thus, it is a promising candidate for the characterization of the venous tree topology.