What happened
Motivation Data-independent acquisition mass spectrometry (DIA-MS) has become increasingly popular for clinical proteomics due to its sensitivity and reproducibility. Univariate statistical analysis tools, such as limma and MSstats, are widely used for identifying differentially abundant proteins but cannot capture multivariate relationships between proteins that may provide greater discriminatory power as a panel. Machine learning approaches can address this gap, but typically prioritise predictive performance over feature stability, producing biomarker panels for downstream validation that vary depending on data splitting and are poorly suited to clinical translation. Results We developed stably, a Python package that implements stability selection with formal false positive control for DIA proteomics data. Using synthetic data with known ground-truth biomarkers, we show that the Shah and Samworth complementary pairs stability selection framework recovers more true synthetic biomarkers than the Meinshausen and Buhlmann framework at moderate effect sizes typical of proteomics (d = 0.5 - 2.0), while both maintain false positive rates well below their theoretical guarantees. Applied to a publicly available serum proteomics dataset from patients with all stages of pancreatic ductal adenocarcinoma (n=176), stably identified a stable 17-protein biomarker panel in the discovery cohort (n=120), which achieved higher predictive power (AUC = 0.93) in the validation cohort (n=56) than the panel selected by Byeon et al. (2024)(AUC 0.82). stably represents a principled, error-controlled method for biomarker panel discovery for translation into second cohorts. Availability and implementation stably is available on GitHub (https://github.com/byrnedaniel5-eng/stably); the version used in this study is archived on PyPI (https://pypi.org/project/stably/). Update (2026-09-16): stably: error-controlled stability selection for biomarker panel discovery . Motivation Data-independent acquisition mass spectrometry (DIA-MS) has become increasingly popular for clinical proteomics due to its sensitivity and reproducibility. Univariate statistical analysis tools, such as limma and MSstats, are widely used for identifying differentially abundant proteins but cannot capture multivariate relationships between proteins that may provide greater discriminatory power as a panel. Machine learning approaches can address this gap, but typically prioritise predictive performance over feature stability, producing biomarker panels for downstream validation that vary depending on data splitting and are poorly suited to clinical translation. Results We developed stably, a Python package that implements stability selection with formal false positive control for DIA proteomics data. Using synthetic data with known ground-truth biomarkers, we show that the Shah and Samworth complementary pairs stability selection framework recovers more true synthetic biomarkers than the Meinshausen and Buhlmann framework at moderate effect sizes typical of proteomics (d Update (2026-09-17): MMAD-Risk: Multivariate Mixed Survival Analysis for the Prediction of Age-Dependent Disease Risks from Plasma Proteomes . Motivation: Multivariate survival analysis with hundreds of correlated outcomes is computationally challenging. Established approaches either ignore correlations between response variables, rely on black-box deep learning or are limited to small-scale outcomes. Results: We introduce MMAD-Risk, a novel multivariate mixed accelerated failure time (AFT) model that enables scalable analysis of high-dimensional survival analysis data. We train MMAD-Risk using amortized variational inference where we design the variational distribution such that it factorizes across diseases, allowing us to decompose multivariate disease risk prediction into a series of tractable, one-dimensional problems. This allows us to calculate the ELBO analytically, enabling fast computation. The model employs a low-rank decomposition of the effect size matrix B = VW to capture shared disease mechanisms and latent random effects Vz to model comorbidity. MMAD-Risk is trained on the UK Biobank Pharma Proteomics cohort (N {approx} 55000, P {approx} 3000 proteins, D = 271 Update (2026-09-18): Sparse Machine Learning Pipeline with Stabl Identifies Cord Blood Multi-Omic Signatures of Bronchopulmonary Dysplasia . Background: Several omics studies have been completed in recent years, with the goal of identifying biomarkers of complex multifactorial diseases, such as bronchopulmonary dysplasia (BPD). Objective: To evaluate the performance of 3 distinct omics platforms, using a machine learning pipeline with integration of sparse, reliable and adaptive biomarker identification (Stabl). Methods: Using a well-characterized birth cohort, cord blood metabolomics, proteomics and adductomics data were integrated with Least Absolute Shrinkage and Selection Operator (LASSO) regression and Stabl, to evaluate predictive performance for BPD. Results: Sparse multivariable modeling of 45,000 features measured in 217 infants (52 term, 165 extremely preterm <28 weeks; 82 with BPD and 35 with severe BPD/death) identified a perfect signature for preterm birth with both LASSO and Stabl (AUROC=1.0; p<0.001). Analysis of the preterm group yielded excellent predictive power for severe BPD (AUROC=0.83; p=0.005). Stabl identified a set of 12 biomarkers (2 adducts, 3 prote Update (2026-09-21): Discriminating rare disease cases from their controls based on observed and excluded phenotypes . Rare diseases are individually uncommon but collectively prevalent. Their primary clinical challenge lies not in treatment but in diagnosis. In the early stages of clinical management, it is frequently unclear whether the observed phenotypes are associated with a rare disease. Leveraging machine learning methods to mine latent associations between these phenotypes and rare diseases for early diagnosis offers a viable strategy to alleviate this diagnostic dilemma. In the present study, we demonstrated that machine learning can effectively discriminate rare disease cases from their controls using observed and excluded phenotypes as features. Among them, the Random Forest model achieved the best classification performance with a certain degree of generalizability. Based on further analysis of the feature selection results, we conclude that the two factors, specificity and occurrence count, are important for phenotype selection in rare disease discrimination, and comparable importance should be attached to both observed and excluded phenotypes, during feature con Update (2026-09-21): MaSkel and napari-MaSkel: fast morphological skeleton feature extraction from segmentation masks . Summary: Existing workflows for extracting morphological features from network-like biomedical structures often require several heterogeneous tools, which can limit integration and reproducibility. We present MaSkel and napari-MaSkel, open-source Python tools that provide GUI- and CLI-based workflows for 2D and 3D skeletonization and graph-based feature extraction, using a substantially accelerated implementation of the widely used Lee94 thinning algorithm. Availability and Implementation: MaSkel and napari-MaSkel are open-source Python packages available under the MIT license on PyPI and GitHub: https://github.com/bionetslab/maskel/, https://github.com/bionetslab/napari-maskel/. Documentation: https://bionetslab.github.io/maskel/, https://bionetslab.github.io/napari-maskel/. Benchmarking and validation: https://github.com/bionetslab/maskel-evaluations.