Gene selection and classification by entropy-based recursive feature elimination. - Cesare Furlanello; Maria Serafini; Stefano Merler; Giuseppe Jurman (IJCNN, 2003)

Entropy-based gene ranking without selection bias for the predictive classification of microarray data. - Cesare Furlanello; Maria Serafini; Stefano Merler; Giuseppe Jurman (BMC Bioinform., 2003)

An accelerated procedure for recursive feature ranking on microarray data. - Cesare Furlanello; Maria Serafini; Stefano Merler; Giuseppe Jurman (Neural Networks, 2003)

Exact Bagging with k-Nearest Neighbour Classifiers. - Bruno Caprile; Stefano Merler; Cesare Furlanello; Giuseppe Jurman (Multiple Classifier Systems, 2004)

Machine Learning on Historic Air Photographs for Mapping Risk of Unexploded Bombs. - Stefano Merler; Cesare Furlanello; Giuseppe Jurman (ICIAP, 2005)

Semisupervised Profiling of Gene Expressions and Clinical Data. - Silvano Paoli; Giuseppe Jurman; Davide Albanese; Stefano Merler; Cesare Furlanello (WILF, 2005)

Semisupervised Learning for Molecular Profiling. - Cesare Furlanello; Maria Serafini; Stefano Merler; Giuseppe Jurman (IEEE ACM Trans. Comput. Biol. Bioinform., 2005)

Strategies for containing an influenza pandemic: the case of Italy. - Stefano Merler; Giuseppe Jurman; Cesare Furlanello; Caterina Rizzo; Antonino Bella; Marco Massari; Marta Luisa Ciofi degli Atti (BIONETICS, 2006)

Proteome Profiling without Selection Bias. - Annalisa Barla; Bettina Irler; Stefano Merler; Giuseppe Jurman; Silvano Paoli; Cesare Furlanello (CBMS, 2006)

Terminated Ramp-Support Vector Machines: A nonparametric data dependent kernel. - Stefano Merler; Giuseppe Jurman (Neural Networks, 2006)

Combining feature selection and DTW for time-varying functional genomics. - Cesare Furlanello; Stefano Merler; Giuseppe Jurman (IEEE Trans. Signal Process., 2006)

Deriving the Kernel from Training Data. - Stefano Merler; Giuseppe Jurman; Cesare Furlanello (MCS, 2007)

Supervised classification of combined copy number and gene expression data. - Samantha Riccadonna; Giuseppe Jurman; Stefano Merler; Silvano Paoli; Alessandro Quattrone; Cesare Furlanello (J. Integr. Bioinform., 2007)

Machine learning methods for predictive proteomics. - Annalisa Barla; Giuseppe Jurman; Samantha Riccadonna; Stefano Merler; Marco Chierici; Cesare Furlanello (Briefings Bioinform., 2008)

Algebraic stability indicators for ranked lists in molecular profiling. - Giuseppe Jurman; Stefano Merler; Annalisa Barla; Silvano Paoli; Antonio Galea; Cesare Furlanello (Bioinform., 2008)

Integrating gene expression profiling and clinical data. - Silvano Paoli; Giuseppe Jurman; Davide Albanese; Stefano Merler; Cesare Furlanello (Int. J. Approx. Reason., 2008)

An introduction to spectral distances in networks. - Giuseppe Jurman; Roberto Visintainer; Cesare Furlanello (WIRN, 2010)

A machine learning pipeline for quantitative phenotype prediction from genotype data. - Giorgio Guzzetta; Giuseppe Jurman; Cesare Furlanello (BMC Bioinform., 2010)

The Structure of Thin Lie Algebras with Characteristic Two. - Marina Avitabile; Giuseppe Jurman; Sandro Mattarei (Int. J. Algebra Comput., 2010)

A Machine Learning Pipeline for Discriminant Pathways Identification. - Annalisa Barla; Giuseppe Jurman; Roberto Visintainer; Margherita Squillario; Michele Filosi; Samantha Riccadonna; Cesare Furlanello (CIBB, 2011)

A glocal distance for network comparison - Giuseppe Jurman; Roberto Visintainer; Samantha Riccadonna; Michele Filosi; Cesare Furlanello (CoRR, 2012)

mlpy: Machine Learning Python - Davide Albanese; Roberto Visintainer; Stefano Merler; Samantha Riccadonna; Giuseppe Jurman; Cesare Furlanello (CoRR, 2012)

A combinatorial model of malware diffusion via Bluetooth connections - Stefano Merler; Giuseppe Jurman (CoRR, 2012)

minerva and minepy: a C engine for the MINE suite and its R, Python and MATLAB wrappers. - Davide Albanese; Michele Filosi; Roberto Visintainer; Samantha Riccadonna; Giuseppe Jurman; Cesare Furlanello (Bioinform., 2013)

Fast randomization of large genomic datasets while preserving alteration counts. - Andrea Gobbi; Francesco Iorio; Kevin J. Dawson; David C. Wedge; David Tamborero; Ludmil B. Alexandrov; Núria López-Bigas; Mathew Garnett; Giuseppe Jurman; Julio Saez-Rodriguez (Bioinform., 2014)

Entropy Dynamics of Community Alignment in the Italian Parliament Time-Dependent Network. - Gabriele Lami; Marco Cristoforetti; Giuseppe Jurman; Cesare Furlanello; Tommaso Furlanello (CoRR, 2014)

A promoter-level mammalian expression atlas. - Fantom Consortium; RIKEN PMII; RIKEN CLST (DGT); Alistair R. R. Forrest; Hideya Kawaji; Michael Rehli; J. Kenneth Baillie; Michiel J. L. de Hoon; Vanja Haberle; Timo Lassmann; Ivan V. Kulakovskiy; Marina Lizio; et al. (Nat., 2014)

The HIM glocal metric and kernel for network comparison and classification. - Giuseppe Jurman; Roberto Visintainer; Michele Filosi; Samantha Riccadonna; Cesare Furlanello (DSAA, 2015)

Graph metrics as summary statistics for Approximate Bayesian Computation with application to network model parameter estimation. - Damien Fay; Andrew W. Moore 002; Kenneth N. Brown; Michele Filosi; Giuseppe Jurman (J. Complex Networks, 2015)

Community dynamics in connected time-dependent multilayer networks. - Marco Cristoforetti; Marco Guerini; Giuseppe Jurman; Cesare Furlanello (CoRR, 2015)

Convolutional Neural Network for Stereotypical Motor Movement Detection in Autism. - Nastaran Mohammadian Rad; Andrea Bizzego; Seyed Mostafa Kia; Giuseppe Jurman; Paola Venuti; Cesare Furlanello (CoRR, 2015)

Stereotypical Motor Movement Detection in Dynamic Feature Space. - Nastaran Mohammadian Rad; Seyed Mostafa Kia; Calogero Zarbo; Giuseppe Jurman; Paola Venuti; Cesare Furlanello (ICDM Workshops, 2016)

Efficient randomization of biological networks while preserving functional characterization of individual nodes. - Francesco Iorio; Marti Bernardo-Faura; Andrea Gobbi; Thomas Cokelaer; Giuseppe Jurman; Julio Saez-Rodriguez (BMC Bioinform., 2016)

Towards meaningful physics from generative models. - Marco Cristoforetti; Giuseppe Jurman; Andrea I. Nardelli; Cesare Furlanello (CoRR, 2017)

Towards a scientific blockchain framework for reproducible data analysis. - Cesare Furlanello; Manlio De Domenico; Giuseppe Jurman; Nicole Bussola (CoRR, 2017)

A multiobjective deep learning approach for predictive classification in Neuroblastoma. - Valerio Maggio; Marco Chierici; Giuseppe Jurman; Cesare Furlanello (CoRR, 2017)

Phylogenetic convolutional neural networks in metagenomics. - Diego Fioravanti; Ylenia Giarratano; Valerio Maggio; Claudio Agostinelli; Marco Chierici; Giuseppe Jurman; Cesare Furlanello (BMC Bioinform., 2018)

Deep learning for automatic stereotypical motor movement detection using wearable sensors in autism spectrum disorders. - Nastaran Mohammadian Rad; Seyed Mostafa Kia; Calogero Zarbo; Twan van Laarhoven; Giuseppe Jurman; Paola Venuti; Elena Marchiori; Cesare Furlanello (Signal Process., 2018)

Integrating deep and radiomics features in cancer bioimaging. - Andrea Bizzego; Nicole Bussola; D. Salvalai; Marco Chierici; Valerio Maggio; Giuseppe Jurman; Cesare Furlanello (CIBCB, 2019)

Seasonal Linear Predictivity in National Football Championships. - Giuseppe Jurman (Big Data, 2019)

High Resolution Forecasting of Heat Waves impacts on Leaf Area Index by Multiscale Multitemporal Deep Learning. - Andrea Gobbi; Marco Cristoforetti; Giuseppe Jurman; Cesare Furlanello (CoRR, 2019)

In-field grape berries counting for yield estimation using dilated CNNs. - Luca Coviello; Marco Cristoforetti; Giuseppe Jurman; Cesare Furlanello (CoRR, 2019)

MASS-UMAP: Fast and accurate analog ensemble search in weather radar archive. - Gabriele Franch; Giuseppe Jurman; Luca Coviello; Marta Pendesini; Cesare Furlanello (CoRR, 2019)

Evaluating reproducibility of AI algorithms in digital pathology with DAPPER. - Andrea Bizzego; Nicole Bussola; Marco Chierici; Valerio Maggio; Margherita Francescatto; Luca Cima; Marco Cristoforetti; Giuseppe Jurman; Cesare Furlanello (PLoS Comput. Biol., 2019)

MASS-UMAP: Fast and Accurate Analog Ensemble Search in Weather Radar Archives. - Gabriele Franch; Giuseppe Jurman; Luca Coviello; Marta Pendesini; Cesare Furlanello (Remote. Sens., 2019)

AI Slipping on Tiles: Data Leakage in Digital Pathology. - Nicole Bussola; Alessia Marcolini; Valerio Maggio; Giuseppe Jurman; Cesare Furlanello (ICPR Workshops (1), 2020)

Machine learning can predict survival of patients with heart failure from serum creatinine and ejection fraction alone. - Davide Chicco; Giuseppe Jurman (BMC Medical Informatics Decis. Mak., 2020)

Multilayer Flows in Molecular Networks Identify Biological Modules in the Human Proteome. - Giuseppe Mangioni; Giuseppe Jurman; Manlio De Domenico (IEEE Trans. Netw. Sci. Eng., 2020)

Cyst segmentation on kidney tubules by means of U-Net deep-learning models. - Simone Monaco; Nicole Bussola; Sara Buttò; Diego Sona; Daniele Apiletti; Giuseppe Jurman; Elisa Viola; Marco Chierici; Christodoulos Xinaris; Vincenzo Viola (IEEE BigData, 2021)

An Ensemble Learning Approach for Enhanced Classification of Patients With Hepatitis and Cirrhosis. - Davide Chicco; Giuseppe Jurman (IEEE Access, 2021)

Arterial Disease Computational Prediction and Health Record Feature Ranking Among Patients Diagnosed With Inflammatory Bowel Disease. - Davide Chicco; Giuseppe Jurman (IEEE Access, 2021)

The Benefits of the Matthews Correlation Coefficient (MCC) Over the Diagnostic Odds Ratio (DOR) in Binary Classification Assessment. - Davide Chicco; Valery Starovoitov; Giuseppe Jurman (IEEE Access, 2021)

The Matthews Correlation Coefficient (MCC) is More Informative Than Cohen's Kappa and Brier Score in Binary Classification Assessment. - Davide Chicco; Matthijs J. Warrens; Giuseppe Jurman (IEEE Access, 2021)

The Matthews correlation coefficient (MCC) is more reliable than balanced accuracy, bookmaker informedness, and markedness in two-class confusion matrix evaluation. - Davide Chicco; Niklas Tötsch; Giuseppe Jurman (BioData Min., 2021)

The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation. - Davide Chicco; Matthijs J. Warrens; Giuseppe Jurman (PeerJ Comput. Sci., 2021)

Towards a potential pan-cancer prognostic signature for gene expression based on probesets and ensemble machine learning. - Davide Chicco; Abbas Alameer; Sara Rahmati; Giuseppe Jurman (BioData Min., 2022)

Mitigating Health Data Poverty: Generative Approaches versus Resampling for Time-series Clinical Data. - Raffaele Marchesi; Nicolo Micheletti; Giuseppe Jurman; Venet Osmani (CoRR, 2022)

A brief survey of tools for genomic regions enrichment analysis. - Davide Chicco; Giuseppe Jurman (Frontiers Bioinform., 2022)

The ABC recommendations for validation of supervised machine learning results in biomedical sciences. - Davide Chicco; Giuseppe Jurman (Frontiers Big Data, 2022)

An Invitation to Greater Use of Matthews Correlation Coefficient in Robotics and Artificial Intelligence. - Davide Chicco; Giuseppe Jurman (Frontiers Robotics AI, 2022)

Automatically detecting Crohn's disease and Ulcerative Colitis from endoscopic imaging. - Marco Chierici; Nicolae Puica; Matteo Pozzi; Antonello Capistrano; Marcello Dorian Donzella; Antonio Colangelo; Venet Osmani; Giuseppe Jurman (BMC Medical Informatics Decis. Mak., 2022)

histolab: A Python library for reproducible Digital Pathology preprocessing with automated testing. - Alessia Marcolini; Nicole Bussola; Ernesto Arbitrio; Mohamed Amgad; Giuseppe Jurman; Cesare Furlanello (SoftwareX, 2022)

Differential diagnosis of systemic lupus erythematosus and Sjögren's syndrome using machine learning and multi-omics data. - Jordi Martorell-Marugan; Marco Chierici; Giuseppe Jurman; Marta E. Alarcón-Riquelme; Pedro Carmona-Saez (Comput. Biol. Medicine, 2023)

Ten simple rules for providing bioinformatics support within a hospital. - Davide Chicco; Giuseppe Jurman (BioData Min., 2023)

The Matthews correlation coefficient (MCC) should replace the ROC AUC as the standard metric for assessing binary classification. - Davide Chicco; Giuseppe Jurman (BioData Min., 2023)

Signature literature review reveals AHCY, DPYSL3, and NME1 as the most recurrent prognostic genes for neuroblastoma. - Davide Chicco; Tiziana Sanavia; Giuseppe Jurman (BioData Min., 2023)

Endoscopy-based IBD identification by a quantized deep learning pipeline. - Massimiliano Datres; Elisa Paolazzi; Marco Chierici; Matteo Pozzi; Antonio Colangelo; Marcello Dorian Donzella; Giuseppe Jurman (BioData Min., 2023)

Sepsis Survival Minimal Clinical Records. - Davide Chicco; Giuseppe Jurman (UCI Machine Learning Repository, 2023)

A statistical comparison between Matthews correlation coefficient (MCC), prevalence threshold, and Fowlkes-Mallows index. - Davide Chicco; Giuseppe Jurman (J. Biomed. Informatics, 2023)

Scoring Tumor-Infiltrating Lymphocytes in breast DCIS: A guideline-driven artificial intelligence approach. - Matteo Pozzi; Natalie Klubickova; Michela Campora; Frédérique Meeuwsen; Joey Spronck; Carlijn M. Lems; Michelle Stegeman; Leslie Tessier; Mattia Barbareschi; Jeroen van der Laak; Giuseppe Jurman; Francesco Ciompi (COMPAY@MICCAI, 2024)

Explainable deep neural networks for predicting sample phenotypes from single-cell transcriptomics. - Jordi Martorell-Marugan; Raúl López-Domínguez; Juan Antonio Villatoro-García; Daniel Toro-Domínguez; Marco Chierici; Giuseppe Jurman; Pedro Carmona-Saez (Briefings Bioinform., 2024)

The Venus score for the assessment of the quality and trustworthiness of biomedical datasets. - Davide Chicco; Alessandro Fabris; Giuseppe Jurman (BioData Min., 2025)

Deconver: A Deconvolutional Network for Medical Image Segmentation. - Pooya Ashtari; Shahryar Noei; Fateme Nateghi Haredasht; Jonathan H. Chen; Giuseppe Jurman; Aleksandra Pizurica; Sabine Van Huffel (CoRR, 2025)

AI-based modular warning machine for risk identification in proximity healthcare. - Chiara Razzetta; Shahryar Noei; Federico Barbarossa; Edoardo Spairani; Monica Roascio; Elisa Barbi; Giulia Ciacci; Sara Sommariva; Sabrina Guastavino; Michele Piana; Matteo Lenge; Gabriele Arnulfo; et al. (CoRR, 2025)