G. Valentini

Giorgio Valentini, Oleg Okun, G. Valentini, 2008, 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence).

Hierarchical Ensemble Methods for Protein Function Prediction

Giorgio Valentini, G. Valentini, 2014, ISRN bioinformatics.

Abdominal Computed Tomography Imaging Findings in Hospitalized COVID-19 Patients: A Year-Long Experience and Associations Revealed by Explainable Artificial Intelligence

G. Valentini, P. Robinson, E. Casiraghi, 2021, J. Imaging.

Fuzzy ensemble clustering based on random projections for DNA microarray data analysis

Roberto Avogadri, Giorgio Valentini, G. Valentini, 2009, Artif. Intell. Medicine.

Unsupervised Stability-Based Ensembles to Discover Reliable Structures in Complex Bio-molecular Data

Giorgio Valentini, Alberto Bertoni, A. Bertoni, 2009, CIBB.

Discovering multi–level structures in bio-molecular data through the Bernstein inequality

Giorgio Valentini, Alberto Bertoni, A. Bertoni, 2008, BMC Bioinformatics.

Randomized maps for assessing the reliability of patients clusters in DNA microarray data analyses

Giorgio Valentini, Alberto Bertoni, A. Bertoni, 2006, Artif. Intell. Medicine.

Prediction of Human Phenotype Ontology terms by means of hierarchical ensemble methods

G. Valentini, P. Robinson, M. Schubach, 2017, BMC Bioinformatics.

Generalizable Long COVID Subtypes: Findings from the NIH N3C and RECOVER Programs

Michael A. Gargano, Andrew E. Williams, Guy Karlebach, 2022, medRxiv.

Protein function prediction as a graph-transduction game

Giorgio Valentini, Marcello Pelillo, Sebastiano Vascon, 2020, Pattern Recognit. Lett..

Committee-Based Active Learning to Select Negative Examples for Predicting Protein Functions

Giuliano Grossi, Alessandro Petrini, Marco Frasca, 2018, CIBB.

Ensemble Clustering with a Fuzzy Approach

G. Valentini, R. Avogadri, 2008 .

Ensemble methods : a review

Giorgio Valentini, Matteo Re, G. Valentini, 2012 .

Data integration issues and opportunities in biological XML data management

Giorgio Valentini, David Manset, Rafael Berlanga, 2009 .

An experimental bias-variance analysis of SVM ensembles based on resampling techniques

Giorgio Valentini, G. Valentini, 2005, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

A GPU-based algorithm for fast node label learning in large and unbalanced biomolecular networks

Giorgio Valentini, Giuliano Grossi, Marco Mesiti, 2018, BMC Bioinformatics.

Learning node labels with multi-category Hopfield networks

Giorgio Valentini, Simone Bassis, Marco Frasca, 2016, Neural Computing and Applications.

Computational Intelligence and Machine Learning Methods in Bioinformatics

Giorgio Valentini, Francesco Masulli, Roberto Tagliaferri, 2008 .

Explainable Machine Learning for Early Assessment of COVID-19 Risk Prediction in Emergency Departments

Dario Malchiodi, Alessandro Rizzi, Peter N. Robinson, 2020, IEEE Access.

Gene expression data analysis of human lymphoma using support vector machines and output coding ensembles

G. Valentini, 2002, Artif. Intell. Medicine.

Cyclooxygenase inhibitor use is associated with increased COVID-19 severity

Andrew E. Williams, Guy Karlebach, C. Mungall, 2021, medRxiv.

Ensembles of Learning Machines

Giorgio Valentini, Francesco Masulli, F. Masulli, 2002, WIRN.

True Path Rule Hierarchical Ensembles for Genome-Wide Gene Function Prediction

Giorgio Valentini, G. Valentini, 2011, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

Ensembles in Machine Learning Applications

Giorgio Valentini, Oleg Okun, Matteo Re, 2011, Studies in Computational Intelligence.

Imbalance-Aware Machine Learning for Predicting Rare and Common Disease-Associated Non-Coding Variants

Giorgio Valentini, Peter N. Robinson, Matteo Re, 2017, Scientific Reports.

GraPE: fast and scalable Graph Processing and Embedding pdf

Giorgio Valentini, Elena Casiraghi, Chris Mungall, 2021, ArXiv.

Imbalance-Aware Machine Learning for Predicting Rare and Common Disease-Associated Non-Coding Variants

G. Valentini, P. Robinson, M. Schubach, 2017, Scientific Reports.

Model order selection for bio-molecular data clustering

Giorgio Valentini, Alberto Bertoni, A. Bertoni, 2007, BMC Bioinformatics.

Automated image analysis to assess hygienic behaviour of honeybees

G. Valentini, E. Casiraghi, S. Biffani, 2022, PloS one.

An expectation–maximization framework for comprehensive prediction of isoform-specific functions

Guy Karlebach, C. Mungall, G. Valentini, 2023, Bioinform..

UNIPred: Unbalance-Aware Network Integration and Prediction of Protein Functions

Giorgio Valentini, Alberto Bertoni, Marco Frasca, 2015, J. Comput. Biol..

Complex Data Imputation by Auto-Encoders and Convolutional Neural Networks - A Case Study on Genome Gap-Filling

Elena Casiraghi, Guido Walter Di Donato, Lorenzo Di Tucci, 2020, Comput..

Effectiveness of error correcting output coding methods in ensemble and monolithic learning machines

F. Masulli, G. Valentini, 2003 .

Parallel non-linear dichotomizers

Giorgio Valentini, Francesco Masulli, F. Masulli, 2000, Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks. IJCNN 2000. Neural Computing: New Challenges and Perspectives for the New Millennium.

Effectiveness of Error Correcting Output Codes in Multiclass Learning Problems

Giorgio Valentini, Francesco Masulli, F. Masulli, 2000, Multiple Classifier Systems.

Effectiveness of error correcting output coding methods in ensemble and monolithic learning machines

Giorgio Valentini, Francesco Masulli, F. Masulli, 2003, Formal Pattern Analysis & Applications.

Ensemble methods based on bias-variance analysis

G. Valentini, 2003 .

True Path Rule Hierarchical Ensembles

Giorgio Valentini, G. Valentini, 2009, MCS.

NEURObjects: an object-oriented library for neural network development

Giorgio Valentini, Francesco Masulli, F. Masulli, 2002, Neurocomputing.

Bio-molecular cancer prediction with random subspace ensembles of support vector machines

Giorgio Valentini, Alberto Bertoni, Raffaella Folgieri, 2005, Neurocomputing.

An automated pipeline for multi-species protein function prediction from the UniProt Knowledgebase

G. Valentini, M. Mesiti, M. Ré, 2014 .

On the Quality of Classification Models for Inferring ABAC Policies from Access Logs

Elisa Bertino, Marco Mesiti, Stefano Valtolina, 2019, 2019 IEEE International Conference on Big Data (Big Data).

A Novel Ensemble Technique for Protein Subcellular Location Prediction

Giorgio Valentini, Alessandro Rozza, Gabriele Lombardi, 2011, Ensembles in Machine Learning Applications.

Prediction of Gene Function Using Ensembles of SVMs and Heterogeneous Data Sources

Giorgio Valentini, Matteo Ré, G. Valentini, 2009, Applications of Supervised and Unsupervised Ensemble Methods.

Ensembling Descendant Term Classifiers to Improve Gene - Abnormal Phenotype Predictions

Giorgio Valentini, Peter N. Robinson, Marco Mesiti, 2017, CIBB.

On the limitations of large language models in clinical diagnosis

C. Mungall, G. Valentini, E. Casiraghi, 2023, medRxiv.

HEMDAG: a family of modular and scalable hierarchical ensemble methods to improve Gene Ontology term prediction

Giorgio Valentini, Elena Casiraghi, Alessandro Petrini, 2021, Bioinform..

A stability-based algorithm to validate hierarchical clusters of genes

Roberto Avogadri, Giorgio Valentini, Alessandro Beghini, 2009, Int. J. Knowl. Eng. Soft Data Paradigms.

Notes on hierarchical ensemble methods for DAG-structured taxonomies pdf

Giorgio Valentini, G. Valentini, 2014, ArXiv.

HCGene: a software tool to support the hierarchical classification of genes

Nicolò Cesa-Bianchi, Giorgio Valentini, N. Cesa-Bianchi, 2008, Bioinform..

Functional Inference in FunCat through the Combination of Hierarchical Ensembles with Data Fusion Methods

G. Valentini, M. Ré, 2010 .

Within network learning on big graphs using secondary memory-based random walk kernels

Giorgio Valentini, Jianyi Lin, Marco Mesiti, 2016, COMPLEX NETWORKS.

Multi-species protein function prediction: towards web-based visual analytics

Giorgio Valentini, Marco Mesiti, Marco Frasca, 2016, iiWAS.

On the automated function prediction of big multi-species networks

Marco Mesiti, Matteo Re, Giorgio Valentini, 2014 .

Think globally and solve locally: secondary memory-based network learning for automated multi-species function prediction

Giorgio Valentini, Marco Mesiti, Matteo Re, 2014, GigaScience.

Support vector machines for candidate nodules classification

Giorgio Valentini, Elena Casiraghi, Paola Campadelli, 2005, Neurocomputing.

An Open-Source Knowledge Graph Ecosystem for the Life Sciences

Sanya Bathla Taneja, Adrianne L. Stefanski, Charles Tapley Hoyt, 2023, arXiv.org.

GRAPE for fast and scalable graph processing and random-walk-based embedding

C. Mungall, G. Valentini, E. Casiraghi, 2021, Nature Computational Science.

Predicting nutrition and environmental factors associated with female reproductive disorders using a knowledge graph and random forests

C. Mungall, G. Valentini, M. Haendel, 2023, medRxiv.

Applications of Supervised and Unsupervised Ensemble Methods

Giorgio Valentini, Oleg Okun, G. Valentini, 2009, Applications of Supervised and Unsupervised Ensemble Methods.

An extensive analysis of disease-gene associations using network integration and fast kernel-based gene prioritization methods

Giorgio Valentini, Alfonso E. Romero, Matteo Ré, 2014, Artif. Intell. Medicine.

RANKS: a flexible tool for node label ranking and classification in biological networks

Giuliano Armano, Marco Mesiti, Jianyi Lin, 2016, Bioinform..

A neural network algorithm for semi-supervised node label learning from unbalanced data

Giorgio Valentini, Alberto Bertoni, Marco Frasca, 2013, Neural Networks.

A Fast Ranking Algorithm for Predicting Gene Functions in Biomolecular Networks

Giorgio Valentini, Marco Mesiti, Matteo Ré, 2012, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

Random Walking on Functional Interaction Networks to Rank Genes Involved in Cancer

Giorgio Valentini, Matteo Ré, G. Valentini, 2012, AIAI.

COSNet: A Cost Sensitive Neural Network for Semi-supervised Learning in Graphs

Giorgio Valentini, Alberto Bertoni, Marco Frasca, 2011, ECML/PKDD.

Genes Prioritization with Respect to Cancer Gene Modules Using Functional Interaction Network Data

G. Valentini, M. Ré, 2022 .

Quantitative evaluation of dependence among outputs in ECOC classifiers using mutual information based measures

Giorgio Valentini, Francesco Masulli, F. Masulli, 2001, IJCNN'01. International Joint Conference on Neural Networks. Proceedings (Cat. No.01CH37222).

Bagged ensembles of Support Vector Machines for gene expression data analysis

Giorgio Valentini, Marco Muselli, Francesca Ruffino, 2003, Proceedings of the International Joint Conference on Neural Networks, 2003..

Disease-Genes Must Guide Data Source Integration in the Gene Prioritization Process

Dario Malchiodi, Marco Mesiti, Miguel A. Andrade-Navarro, 2017, CIBB.

COSNet: An R package for label prediction in unbalanced biological networks

Giorgio Valentini, Marco Frasca, G. Valentini, 2017, Neurocomputing.

Fuzzy Ensemble Clustering for DNA Microarray Data Analysis

Roberto Avogadri, Giorgio Valentini, G. Valentini, 2007, WILF.

A Novel Approach to the Problem of Non-uniqueness of the Solution in Hierarchical Clustering

Giorgio Valentini, Eraldo Paulesu, N. Alberto Borghese, 2013, IEEE Transactions on Neural Networks and Learning Systems.

Bio-molecular diagnosis through random subspace ensembles of learning machines

Giorgio Valentini, Alberto Bertoni, Raffaella Folgieri, 2006 .

Random Aggregated and Bagged Ensembles of SVMs: An Empirical Bias?Variance Analysis

Giorgio Valentini, G. Valentini, 2004, Multiple Classifier Systems.

Multitask Hopfield Networks

Giorgio Valentini, Giuliano Grossi, Marco Frasca, 2019, ECML/PKDD.

An unbalance-aware network integration method for gene function prediction

A. Bertoni, G. Valentini, M. Frasca, 2013 .

Metformin is associated with reduced COVID-19 severity in patients with prediabetes

G. Valentini, N. Harris, M. Haendel, 2022, Diabetes Research and Clinical Practice.

A Hierarchical Ensemble Method for DAG-Structured Taxonomies

Giorgio Valentini, Peter N. Robinson, Sebastian Köhler, 2015, MCS.

GRAPE for fast and scalable graph processing and random-walk-based embedding

C. Mungall, G. Valentini, E. Casiraghi, 2021, Nature Computational Science.

Prediction of Human Phenotype Ontology terms by means of hierarchical ensemble methods

Giorgio Valentini, Peter N. Robinson, Max Schubach, 2017, BMC Bioinformatics.

The Aquila Digital Community The Aquila Digital Community

Silvio C. E. Tosatto, Karin M. Verspoor, David A. Lee, 2022 .

Integration of heterogeneous data sources for gene function prediction using decision templates and ensembles of learning machines

Giorgio Valentini, Matteo Ré, G. Valentini, 2010, Neurocomputing.

Ensembles Based on Random Projections to Improve the Accuracy of Clustering Algorithms

Giorgio Valentini, Alberto Bertoni, A. Bertoni, 2005, WIRN/NAIS.

Boosting and Classification of Electronic Nose Data

Giorgio Valentini, Francesco Masulli, Matteo Pardo, 2002, Multiple Classifier Systems.

Bias-Variance Analysis and Ensembles of SVM

Giorgio Valentini, Thomas G. Dietterich, G. Valentini, 2002, Multiple Classifier Systems.