S. Riccadonna

发表

The MicroArray Quality Control (MAQC)-II study of common practices for the development and validation of microarray-based predictive models

Kevin C. Dorff, Aaron M. Smalter, Stephen C. Harris, 2010, Nature Biotechnology.

Differential Network Analysis and Graph Classification: A Glocal Approach

C. Furlanello, Giuseppe Jurman, S. Riccadonna, 2016, 1602.00467.

DIT-University of Trento Predictive Data Mining for Integrative Functional Genomics

S. Riccadonna, 2009 .

Canberra distance on ranked lists

Cesare Furlanello, Giuseppe Jurman, Samantha Riccadonna, 2009 .

The concordance between RNA-seq and microarray data depends on chemical treatment and transcript abundance

David P. Kreil, Paweł P. Łabaj, C. Furlanello, 2014, Nature Biotechnology.

A comprehensive study design reveals treatment-and transcript abundance – dependent concordance between RNA-seq and microarray data

David P. Kreil, Paweł P. Łabaj, Dalila, 2014 .

A Comparison of MCC and CEN Error Measures in Multi-Class Prediction

Cesare Furlanello, Giuseppe Jurman, Samantha Riccadonna, 2010, PloS one.

Clinical Value of Prognosis Gene Expression Signatures in Colorectal Cancer: A Systematic Review

Cesare Furlanello, Giuseppe Jurman, Samantha Riccadonna, 2012, PloS one.

Systems biology of host-mycobiota interactions: dissecting Dectin-1 and Dectin-2 signalling in immune cells with DC-ATLAS.

D. Cavalieri, S. Riccadonna, L. Beltrame, 2013, Immunobiology.

Supervised classification of combined copy number and gene expression data

C. Furlanello, Giuseppe Jurman, S. Merler, 2007, J. Integr. Bioinform..

Complementary Untargeted and Targeted Metabolomics for Differentiation of Extra Virgin Olive Oils of Different Origin of Purchase Based on Volatile and Phenolic Composition and Sensory Quality

S. Riccadonna, F. Mattivi, D. Masuero, 2019, Molecules.

PA P ER A practical tool for Maximal Information Coe cient analysis

C. Donati, S. Riccadonna, D. Albanese, 2018 .

Transcriptome and Cell Physiological Analyses in Different Rice Cultivars Provide New Insights Into Adaptive and Salinity Stress Responses

P. Fontana, E. Lavezzo, S. Toppo, 2018, Front. Plant Sci..

Biomarker stability of multiresolution proteomic proling

C. Furlanello, Giuseppe Jurman, S. Merler, 2007 .

Discovery of A-type procyanidin dimers in yellow raspberries by untargeted metabolomics and correlation based data analysis

S. Riccadonna, A. Feller, S. Martens, 2016, Metabolomics.

Two-omics data revealed commonalities and differences between Rpv12- and Rpv3-mediated resistance in grapevine

M. Morgante, S. Riccadonna, A. Vecchione, 2020, Scientific Reports.

Projection to latent structures with orthogonal constraints for metabolomics data: Description of the article

Samantha Riccadonna, Matteo Stocchero, Pietro Franceschi, 2018 .

A Comparison of MCC and CEN Error Measures in Multi-Class Prediction

Giuseppe Jurman, S. Riccadonna, Cesare Furlanello, 2012, PloS one.

Nutrimetabolomics: An Integrative Action for Metabolomic Analyses in Human Nutritional Studies.

Pieter Giesbertz, Samantha Riccadonna, Estelle Pujos-Guillot, 2018, Molecular nutrition & food research.

Identification of Biomarkers for Defense Response to Plasmopara viticola in a Resistant Grape Variety

S. Riccadonna, A. Vecchione, M. Stefanini, 2017, Front. Plant Sci..

Algebraic Comparison of Partial Lists in Bioinformatics

Cesare Furlanello, Giuseppe Jurman, Samantha Riccadonna, 2010, PloS one.

Biological network comparison via Ipsen-Mikhailov distance

Cesare Furlanello, Giuseppe Jurman, Samantha Riccadonna, 2011, 1109.0220.

The HIM glocal metric and kernel for network comparison and classification

Cesare Furlanello, Giuseppe Jurman, Samantha Riccadonna, 2012, 2015 IEEE International Conference on Data Science and Advanced Analytics (DSAA).

Stability Indicators in Network Reconstruction

Cesare Furlanello, Giuseppe Jurman, Samantha Riccadonna, 2012, PloS one.

A practical tool for maximal information coefficient analysis

Davide Albanese, Claudio Donati, Samantha Riccadonna, 2017, bioRxiv.

minerva and minepy: a C engine for the MINE suite and its R, Python and MATLAB wrappers

Cesare Furlanello, Giuseppe Jurman, Davide Albanese, 2012, Bioinform..

A Machine Learning Pipeline for Identification of Discriminant Pathways

Cesare Furlanello, Annalisa Barla, Giuseppe Jurman, 2014 .

Evaluating sources of variability in pathway profiling

A. Barla, G. Jurman, C. Furlanello, 2012, 1201.3216.

A Machine Learning Pipeline for Discriminant Pathways Identification

Cesare Furlanello, Annalisa Barla, Giuseppe Jurman, 2011, CIBB.

DTW-MIC Coexpression Networks from Time-Course Data

Cesare Furlanello, Giuseppe Jurman, Samantha Riccadonna, 2012, PloS one.

Pathway Inspector: a pathway based web application for RNAseq analysis of model and non-model organisms

Paolo Fontana, Stefano Toppo, Duccio Cavalieri, 2016, Bioinform..

Stability in GRN Inference.

Cesare Furlanello, Giuseppe Jurman, Samantha Riccadonna, 2018, Methods in molecular biology.

Two apples a day lower serum cholesterol and improve cardiometabolic biomarkers in mildly hypercholesterolemic adults: a randomized, controlled, crossover trial

S. Riccadonna, J. Lovegrove, F. Fava, 2019, The American journal of clinical nutrition.

Parenting dimensions in mothers and fathers of children with Autism Spectrum Disorders

P. Venuti, S. Riccadonna, Yagmur Ozturk, 2014 .

mlpy: Machine Learning Python pdf

Cesare Furlanello, Giuseppe Jurman, Davide Albanese, 2012, ArXiv.

Machine learning methods for predictive proteomics

Cesare Furlanello, Annalisa Barla, Giuseppe Jurman, 2007, Briefings Bioinform..

Nutrimetabolomics: An Integrative Action for Metabolomic Analyses in Human Nutritional Studies

E. Feskens, D. Wishart, S. Riccadonna, 2018 .

Projection to latent structures with orthogonal constraints for metabolomics data

Samantha Riccadonna, Matteo Stocchero, Pietro Franceschi, 2018 .

Nutrimetabolomics: An Integrative Action for Metabolomic Analyses in Human Nutritional Studies

E. Feskens, D. Wishart, S. Riccadonna, 2018, Molecular Nutrition & Food Research.

Data Treatment for LC-MS Untargeted Analysis.

Samantha Riccadonna, Pietro Franceschi, S. Riccadonna, 2018, Methods in molecular biology.

The MicroArray Quality Control (MAQC)-II study of common practices for the development and validation of microarray-based predictive models

Todd H. Stokes, Kevin C. Dorff, Aaron M. Smalter, 2010, Nature Biotechnology.

Machine learning methods for predictive proteomics

Giuseppe Jurman, S. Merler, A. Barla, 2007, Briefings Bioinform..

Clinical Value of Prognosis Gene Expression Signatures in Colorectal Cancer: A Systematic Review

Giuseppe Jurman, R. Sanz-Pamplona, A. Berenguer, 2012, PloS one.