Motivation: Exploration of large-scale biological datasets remains a central challenge in computational biology. While many tools are available, they are often developed in isolation, leading to fragmented workflows, duplicated efforts, and limited reproducibility. There is a pressing need for flexible, standardized solutions that unify exploratory data analysis and biomarker discovery across diverse platforms.

Results: We present HDAnalyzeR, a user-friendly and extensible R package for the streamlined analysis of high-dimensional biological data. HDAnalyzeR provides modular, reproducible workflows that support a range of analyses, from quality control and dimensionality reduction to differential expression and enrichment analysis. The package features built-in visualization, metadata-aware modeling, and seamless integration with interactive apps and learning resources. We also present two case studies, where HDAnalyzeR dramatically reduced analysis time and code complexity while providing biologically meaningful insights, such as classification of blood cancer types with AUC = 1.0 and identification of thousands of solid tumor-associated genes. HDAnalyzeR is designed to support both beginner users and experienced bioinformaticians, promoting transparency, reproducibility, and publication-quality output.

Availability and implementation: HDAnalyzeR is freely available both as an open-source R package at https://github.com/kantonopoulos/HDAnalyzeR and a web application at https://hdanalyzer.serve.scilifelab.se.

Cellular signaling is driven by complex, dynamic phosphorylation networks that control growth and survival, and their dysregulation underlies diseases such as cancer. Although modern mass spectrometry enables large-scale quantification of phosphoproteomic responses over time, these measurements remain descriptive and cannot by themselves predict how signaling will evolve under perturbations. Here, we extend a biologically informed recurrent neural network framework (LEMBAS), to learn time-resolved phosphoproteomic trajectories. We introduce two interpretable modules; a phosphosite mapping that links signaling nodes to measured phosphorylation sites and a monotonic time mapping that aligns continuous experimental times to discrete signaling steps. Using synthetic benchmarks and an EGF-stimulation dataset with inhibitor treatments, the model accurately interpolates unseen time points and predicts drug-induced phosphoproteomic responses in a zero-shot setting, outperforming naïve and fully connected baselines. Importantly, the model identifies both canonical and non-canonical signaling effects, including modulation of the transcription factor FOXO3:S7 (from the PI3K/AKT pathway) by drugs affecting PTPN11 (from the RAS/ERK pathway). By combining mechanistic priors with deep learning, our framework provides a scalable approach to interpret and predict dynamic drug responses from phosphoproteomic data.

The human blood proteome provides a holistic readout of health states through the assessment of thousands of circulating proteins. In this study, we present a pan-disease resource to enable the study of diverse disease phenotypes within a harmonized proteomics dataset. By profiling protein concentrations across 59 diseases and healthy cohorts, we identified proteins associated with age, sex, and body mass index, as well as disease-specific signatures. This study highlights shared and distinct protein patterns across conditions, demonstrating the power of a unified proteomics approach to uncover biological insights. The dataset, covering 8262 individuals and up to 5416 proteins, serves as an online resource for exploring disease-specific protein profiles and advancing precision medicine research.

The human blood proteome offers a powerful window into health and disease, allowing for precision medicine efforts, involving early detection of disease, stratification of patients, and monitoring of therapeutic regimes. Here, we report on the analysis of 28 diseases and 2,254individuals using two next generation blood profiling platforms, Proximity Extension Assay and SomaScan, with analysis of over 5,400 and 9,500 protein targets, respectively. The study enables cross-platform comparison of disease-associated profiles and predictive models to find patients with a particular disease. We also cover the longitudinal analysis of children through puberty and the dramatic changes in women during pregnancy. The study includes the analysis of a longevity cohort with healthy old-aged individuals, and we report on a revised model for biological age based on a selected subset of the circulating proteome. The data is visualized in a new version of the open access Blood Resource as part of the Human Protein Atlas allowing comparisons of the two analytical platforms to explore the proteome profiles across the blood from healthy individuals and patients with diseases.