ArrayMining: a modular web-application for microarray analysis combining ensemble and consensus methods with cross-study normalization. - Enrico Glaab; Jonathan M. Garibaldi; Natalio Krasnogor (BMC Bioinform., 2009)

Learning Pathway-based Decision Rules to Classify Microarray Cancer Samples. - Enrico Glaab; Jonathan M. Garibaldi; Natalio Krasnogor (GCB, 2010)

TopoGSA: network topological gene set analysis. - Enrico Glaab; Anaïs Baudot; Natalio Krasnogor; Alfonso Valencia (Bioinform., 2010)

Extending pathways and processes using molecular interaction networks to analyse cancer genome data. - Enrico Glaab; Anaïs Baudot; Natalio Krasnogor; Alfonso Valencia (BMC Bioinform., 2010)

Analysing functional genomics data using novel ensemble, consensus and data fusion techniques. - Enrico Glaab (2011)

EnrichNet: network-based gene set enrichment analysis. - Enrico Glaab; Anaïs Baudot; Natalio Krasnogor; Reinhard Schneider 002; Alfonso Valencia (Bioinform., 2012)

PathVar: analysis of gene and protein expression variance in cellular pathways using microarray data. - Enrico Glaab; Reinhard Schneider 002 (Bioinform., 2012)

RepExplore: addressing technical replicate variance in proteomics and metabolomics data analysis. - Enrico Glaab; Reinhard Schneider 002 (Bioinform., 2015)

Building a virtual ligand screening pipeline using free software: a survey. - Enrico Glaab (Briefings Bioinform., 2016)

Using prior knowledge from cellular pathways and molecular networks for diagnostic specimen classification. - Enrico Glaab (Briefings Bioinform., 2016)

Predictive and interpretable models via the stacked elastic net. - Armin Rauschenberger; Enrico Glaab; Mark A. van de Wiel (Bioinform., 2021)

Predicting correlated outcomes from molecular data. - Armin Rauschenberger; Enrico Glaab (Bioinform., 2021)

Pharmacophore Model for SARS-CoV-2 3CLpro Small-Molecule Inhibitors and in Vitro Experimental Validation of Computationally Screened Inhibitors. - Enrico Glaab; Ganesh Babu Manoharan; Daniel Abankwa (J. Chem. Inf. Model., 2021)

Crowdsourcing digital health measures to predict Parkinson's disease severity: the Parkinson's Disease Digital Biomarker DREAM Challenge. - Solveig K. Sieberts; Jennifer Schaff; Marlena Duda; Bálint Á Pataki; Ming Sun; Phil Snyder; Jean-Francois Daneault; Federico Parisi; Gianluca Costante; Udi Rubin; Peter Banda; Yooree Chae; et al. (npj Digit. Medicine, 2021)

Ten quick tips for biomarker discovery and validation analyses using machine learning. - Ramón Díaz-Uriarte; Elisa Gómez de Lope; Rosalba Giugno; Holger Fröhlich; Petr V. Nazarov; Isabel A. Nepomuceno-Chamorro; Armin Rauschenberger; Enrico Glaab (PLoS Comput. Biol., 2022)

Penalized regression with multiple sources of prior effects. - Armin Rauschenberger; Zied Landoulsi; Mark A. van de Wiel; Enrico Glaab (Bioinform., 2023)

Bioinformatics approaches for studying molecular sex differences in complex diseases. - Rebecca Ting Jiin Loo; Mohamed Soudy; Francesco Nasta; Mirco Macchi; Enrico Glaab (Briefings Bioinform., 2024)

Graph Representation Learning Strategies for Omics Data: A Case Study on Parkinson's Disease. - Elisa Gómez de Lope; Saurabh Deshpande; Ramón Viñas Torné 001; Pietro Liò; Enrico Glaab; Stéphane P. A. Bordas (CoRR, 2024)

Integrating digital gait data with metabolomics and clinical data to predict outcomes in Parkinson's disease. - Cyril Brzenczek; Quentin Klopfenstein; Tom Hähnel; Holger Fröhlich; Enrico Glaab; Geeta Acharya; Gloria Aguayo; Myriam Alexandre; Muhammad Ali; Wim Ammerlann; Giuseppe Arena; Michele Bassis; et al. (npj Digit. Medicine, 2024)

Multi-cohort machine learning identifies predictors of cognitive impairment in Parkinson's disease. - Rebecca Ting Jiin Loo; Lukas Pavelka; Graziella Mangone; Fouad Khoury; Marie Vidailhet; Jean-Christophe Corvol; Enrico Glaab; Geeta Acharya; Gloria Aguayo; Myriam Alexandre; Muhammad Ali; Wim Ammerlann; et al. (npj Digit. Medicine, 2025)