World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
36
Citations
16483
World Ranking
10962
National Ranking
348

Overview

Giuseppe Jurman is affiliated with the Fondazione Bruno Kessler in Italy, contributing extensively to several intersecting fields of scientific research. Their work primarily spans Biochemistry, Genetics and Molecular Biology, Medicine, and Computer Science, reflecting a multidisciplinary approach to complex biological and computational problems.

The main subfields of Giuseppe Jurman's research include Molecular Biology, Artificial Intelligence, Computer Vision and Pattern Recognition, Genetics, and Biophysics. This diverse expertise supports a broad investigation into biological systems and computational methods, emphasizing the integration of machine learning techniques with molecular and medical data analysis.

The scientist's recent papers address key topics in binary classification evaluation and machine learning applications in healthcare. Notable publications include:

  • The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation, 2020, BMC Genomics
  • The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation, 2021, PeerJ Computer Science
  • The Matthews correlation coefficient (MCC) is more reliable than balanced accuracy, bookmaker informedness, and markedness in two-class confusion matrix evaluation, 2021, BioData Mining
  • Machine learning can predict survival of patients with heart failure from serum creatinine and ejection fraction alone, 2020, BMC Medical Informatics and Decision Making
  • The Matthews correlation coefficient (MCC) should replace the ROC AUC as the standard metric for assessing binary classification, 2023, BioData Mining

Key research topics covered in their work include gene expression and cancer classification, machine learning in healthcare, cell image analysis techniques, bioinformatics and genomic networks, imbalanced data classification techniques, artificial intelligence in healthcare, and AI in cancer detection.

The scientist frequently publishes in venues focused on computational biology and data mining, with submissions to:

  • BioData Mining (8 publications)
  • bioRxiv (Cold Spring Harbor Laboratory) (8 publications)
  • IEEE Access (4 publications)
  • Scientific Reports (4 publications)
  • Zenodo (CERN European Organization for Nuclear Research) (4 publications)

Collaboration has been a significant part of their research output, with frequent coauthors including Marco Chierici, Davide Chicco, Cesare Furlanello, Luca Coviello, and Nicole Bussola. These collaborations indicate a sustained network of researchers working within overlapping domains of computational science and biology.

Best Publications

  • The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation

    Davide Chicco;Giuseppe Jurman

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

    Leming Shi;Gregory Campbell;Wendell D. Jones;Fabien Campagne

  • 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

  • Repeatability of published microarray gene expression analyses.

    John P A Ioannidis;David B Allison;Catherine A Ball;Issa Coulibaly

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

    Charles Wang;Binsheng Gong;Pierre R. Bushel;Jean Thierry-Mieg

  • Machine learning can predict survival of patients with heart failure from serum creatinine and ejection fraction alone

    Davide Chicco;Giuseppe Jurman

  • The Matthews correlation coefficient (MCC) should replace the ROC AUC as the standard metric for assessing binary classification

    Unknown

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

    Giuseppe Jurman;Samantha Riccadonna;Cesare Furlanello

  • minerva and minepy

    Davide Albanese;Michele Filosi;Roberto Visintainer;Samantha Riccadonna

  • Entropy-based gene ranking without selection bias for the predictive classification of microarray data

    Cesare Furlanello;Maria Serafini;Stefano Merler;Giuseppe Jurman

  • Algebraic stability indicators for ranked lists in molecular profiling

    Giuseppe Jurman;Stefano Merler;Annalisa Barla;Silvano Paoli

  • Phylogenetic convolutional neural networks in metagenomics

    Diego Fioravanti;Diego Fioravanti;Ylenia Giarratano;Valerio Maggio;Claudio Agostinelli

  • An accelerated procedure for recursive feature ranking on microarray data

    C. Furlanello;M. Serafini;S. Merler;G. Jurman

  • The Benefits of the Matthews Correlation Coefficient (MCC) Over the Diagnostic Odds Ratio (DOR) in Binary Classification Assessment

    Davide Chicco;Valery Starovoitov;Giuseppe Jurman

  • Deep learning for automatic stereotypical motor movement detection using wearable sensors in autism spectrum disorders

    Nastaran Mohammadian Rad;Nastaran Mohammadian Rad;Nastaran Mohammadian Rad;Seyed Mostafa Kia;Calogero Zarbo;Twan van Laarhoven

  • Machine learning methods for predictive proteomics

    Annalisa Barla;Giuseppe Jurman;Samantha Riccadonna;Stefano Merler

  • Precipitation Nowcasting with Orographic Enhanced Stacked Generalization: Improving Deep Learning Predictions on Extreme Events

    Gabriele Franch;Daniele Nerini;Marta Pendesini;Luca Coviello

  • mlpy: Machine Learning Python

    Davide Albanese;Roberto Visintainer;Stefano Merler;Samantha Riccadonna

  • Functional analysis of multiple genomic signatures demonstrates that classification algorithms choose phenotype-related genes

    W. Shi;M. Bessarabova;D. Dosymbekov;Z. Dezso

  • A verified genomic reference sample for assessing performance of cancer panels detecting small variants of low allele frequency

    Wendell Jones;Binsheng Gong;Natalia Novoradovskaya;Dan Li

  • Functional Analysis of Multiple Genomic Signatures Demonstrates that Classification Algorithms Choose Phenotype-Related Genes

    Weihua Shi;M. Bessarabova;D. Dosymbekov;Z. Dezso

  • Evaluating reproducibility of AI algorithms in digital pathology with DAPPER

    Andrea Bizzego;Andrea Bizzego;Nicole Bussola;Nicole Bussola;Marco Chierici;Valerio Maggio

  • The HIM glocal metric and kernel for network comparison and classification

    Giuseppe Jurman;Roberto Visintainer;Michele Filosi;Samantha Riccadonna

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

    Davide Albanese;Michele Filosi;Roberto Visintainer;Samantha Riccadonna

Frequent Co-Authors

Cesare Furlanello
Cesare Furlanello Fondazione Bruno Kessler
Leming Shi
Leming Shi Fudan University
Weida Tong
Weida Tong National Center for Toxicological Research
Yuri Nikolsky
Yuri Nikolsky F1 Genomics
Julio Saez-Rodriguez
Julio Saez-Rodriguez Heidelberg University
Pierre R. Bushel
Pierre R. Bushel National Institutes of Health
Paola Venuti
Paola Venuti University of Trento
Joaquín Dopazo
Joaquín Dopazo Institute of Biomedicine of Seville

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