Network and Systems Medicine returns at a time when the discipline has advanced from conceptual foundations to demonstrated translational impact. Network pharmacology and systems approaches now provide clinical proof-of-concept, including synergistic interventions in ischemic stroke, mechanistic stratification of hypertension, and nutritional targeting in peripheral artery disease. These examples illustrate a shift from describing biological complexity to addressing causal mechanisms. Supported by initiatives such as REPO4EU and highlighted at RExPO’25, the journal will serve as a diamond open-access platform for sharing computational models, multi-omics integration, and drug repurposing strategies. Its mission is to accelerate collaboration and innovation across stakeholders and ensure that systems and network medicine deliver tangible patient benefit.
Machine learning/artificial intelligence (ML/AI)-based predictive modeling is becoming increasingly popular in healthcare, particularly within the scope of precision medicine. Models based on patient data are effective tools for clinical decision-making, as well as impactful methods for researchers to expand on existing scientific and clinical understanding of biomarkers and biological pathways. We present PredictMod, an open-license platform for users to query, run, and publish electronic medical records and omics-based intervention outcome (responder vs non-responder) prediction models. The platform currently hosts several intervention outcome prediction models that utilize varied data types, conditions, and interventions, offering users insight into the kinds of information required to develop such models. Model-specific graphs and charts such as confusion matrices and feature importance facilitate user understanding of model output. Our model creation pipeline ensures comprehensive documentation using BioCompute Objects. The platform allows researchers in conjunction with clinicians to generate models using data from publications or their datasets and apply them to predict intervention outcomes for new patients or samples. PredictMod is particularly suited for small datasets, which are common in biological research. By providing a framework for pilot studies, the platform facilitates early-stage insights, helping researchers justify larger-scale data collection efforts. With its versatility and accessibility, PredictMod can advance biomedical research and precision clinical medicine by lowering technical barriers and fostering data-driven discovery approaches at the early stages of hypothesis generation using ML/AI.
Chronic lung disease (CLD) i.e., bronchopulmonary dysplasia (BPD) is the most common long-term complication after preterm birth. This clinically heterogeneous disease is characterized by impaired development of the gas exchange area and the bronchial tree. The identification of disease endotypes or indicators of disease onset early after birth would allow for individualized monitoring and treatment. In a cohort of 55 preterm infants phenotypically described by detailed clinical data on pregnancy, birth, and neonatal intensive care unit care until discharge, and a complete assessment of pulmonary and extrapulmonary morbidities, we analyzed 1120 proteins and 213 metabolites in samples obtained in the first weeks of life to characterize biological signatures of BPD. Latent factor analysis highlighted seven factors, three of which linked proteomic and metabolomic data, highlighting a common inflammatory/immune signature but no independent endotypes. We next used abundance patterns of differentially abundant proteins and metabolites and successfully identified biomarker candidates associated with disease severity including PC(O-36:5), CCL22, KIR3DL2, SCGF-alpha, and SCGF-beta. Confirmation of the discriminatory power of these biomarkers in adult CLD patients (n=44) using matched proteomic profiling suggests CCL22, KIR3DL2, and SCGF-beta as shared biomarker candidates of BPD and adult CLD.
In this opinion article, we argue that the requisite scientific knowledge and technological capabilities to elucidate the fundamental causes of chronic diseases—and to develop strategies for their prevention, reversal, and cure—already exist today. However, to implement this vision, a transformative shift in the scientific approach is required ( Figure 1 ). We advocate for a new way to conduct science, inspired by the organizational structure, interdisciplinary synergy, and operational excellence of the Manhattan Project (not its goal of developing a weapon). This model will overcome the current fragmentation of the scientific efforts, which are dispersed across tens of thousands of isolated projects, each with distinct objectives, datasets, methodologies, and analyses. It will also not be distracted and disoriented by the current incentive structure in academic research, which focuses on publications. By adopting the collaborative and goal-oriented framework of the Manhattan Project, humanity can refocus the scientific efforts to effectively confront and consign chronic diseases to the annals of history.
Transcription factors play important roles in maintaining normal biological function, and their dys-regulation can lead to the development of diseases. Identifying candidate transcription factors involved in disease pathogenesis is thus an important task for deriving mechanistic insights from gene expression data. We developed Transcriptional Regulator Identification using Prize-collecting Steiner trees (TRIPS), a workflow for identifying candidate transcriptional regulators from case-control expression data. In the first step, TRIPS combines the results of differential expression analysis with a disease module identification step to retrieve perturbed subnetworks comprising an expanded gene list. TRIPS then solves a prize-collecting Steiner tree problem on a gene regulatory network, thereby identifying candidate transcriptional modules and transcription factors. We compare TRIPS to relevant methods using publicly available disease datasets and show that the proposed workflow can recover known disease-associated transcription factors with high precision. Network perturbation analyses demonstrate the reliability of TRIPS results. We further evaluate TRIPS on Alzheimer’s disease, diabetic kidney disease, and prostate cancer single-cell omics datasets. Overall, TRIPS is a useful approach for prioritizing transcriptional mechanisms for further downstream analyses.### Competing Interest StatementThe authors have declared no competing interest.
