SUMMARY: Omics profiling has proven of great use for unbiased and comprehensive identification of key features that define biological phenotypes and underlie medical conditions. While each omics profile assists characterization of specific molecular components relevant for the studied phenotype, their joint evaluation can offer deeper insights into the overall mechanistic functioning of biological systems. Here, we introduce an approach where, starting from representative traits (e.g. differentially expressed elements) obtained for each omics profile, we construct and analyze joint interaction networks. The resulting networks rely on the existing knowledge of confident interactions among biological entities. We use these maps to identify and describe central elements, which connect multiple entities characteristic of the studied phenotypes and we leverage MONET network decomposition tool in order to highlight functionally connected network modules. In order to enable broad usage of this approach, we developed the NOODAI software platform, which enables integrative omics analysis through a user-friendly interface. The analysis outcomes are presented both as raw output tables as well as informative summary plots and written reports. Since the MONET tool enables the use of algorithms with strong performance in identifying disease-relevant modules, NOODAI software platform can be of a high value for analyzing clinical multi-omics datasets. AVAILABILITY AND IMPLEMENTATION: NOODAI is freely accessible at https://omics-oracle.com. Source code is available under GPL3 at: https://github.com/TotuTiberiu/NOODAI with the DOI: 10.5281/zenodo.17203984.

The objective of this study is to showcase a pipeline able to perform fully automated grading of retinal inflammation based on a standardised, clinically-validated grading scheme, namely the Tugal-Tuktun scale. The application of such scale has so far been hindered by the the amount of time required to (manually) apply it in clinical settings. Our dataset includes 3,205 fluorescein angiography images, from 148 patients and 242 eyes, from the uveitis department of Jules Gonin Eye Hospital. The data were automatically extracted from a medical device (Heidelberg Spectralis), in hospital settings. Images were graded by a medical expert. We focused specifically on one type of inflammation, namely retinal vasculitis. Our pipeline comprises both learning-based models (Pasa model with F1 score = 0.81, AUC = 0.86), and a computer vision intensity-based approach to serve as a baseline (F1 score = 0.57, AUC = 0.66). A recall of up to 0.833, computed on an independent test set, is comparable to the scores obtained by available state-of-the-art approaches. Here we present the first fully automated pipeline for the grading of retinal vasculitis from raw medical images that is applicable to a real-world clinical data.

SUMMARY: We define a disease module as a partition of a molecular network whose components are jointly associated with one or several diseases or risk factors thereof. Identification of such modules, across different types of networks, has great potential for elucidating disease mechanisms and establishing new powerful biomarkers. To this end, we launched the 'Disease Module Identification (DMI) DREAM Challenge', a community effort to build and evaluate unsupervised molecular network modularization algorithms. Here, we present MONET, a toolbox providing easy and unified access to the three top-performing methods from the DMI DREAM Challenge for the bioinformatics community. AVAILABILITY AND IMPLEMENTATION: MONET is a command line tool for Linux, based on Docker and Singularity containers; the core algorithms were written in R, Python, Ada and C++. It is freely available for download at https://github.com/BergmannLab/MONET.git. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.