Giuseppe Jurman

Giuseppe Jurman is a mathematician, with a PhD in Algebra, working at MPBA on various aspects of computational biology. His main research interests are statistical machine learning, mathematical modeling for high-throughtput data and network analysis. He is also an expert in scientific programming with Python and other computing languages.

1. Chicco, Davide; Tötsch, Niklas; Jurman, Giuseppe,
   The Matthews correlation coefficient (MCC) is more reliable than balanced accuracy, bookmaker informedness, and markedness in two-class confusion matrix evaluation,
   in «BIODATA MINING»,
   vol. 14,
   n. 1,
   2021
2. Chicco, Davide; Jurman, Giuseppe,
   An ensemble learning approach for enhanced classification of patients with hepatitis and cirrhosis,
   in «IEEE ACCESS»,
   vol. 9,
   2021
   , pp. 24485 -
   24498
3. Chicco, Davide; Starovoitov, Valery; Jurman, Giuseppe,
   The benefits of the Matthews correlation coefficient ({MCC}) over the diagnostic odds ratio ({DOR}) in binary classification assessment,
   in «IEEE ACCESS»,
   vol. 9,
   2021
   , pp. 47112 -
   47124
4. Jones, Wendell; Gong, Binsheng; Novoradovskaya, Natalia; Li, Dan; Kusko, Rebecca; Richmond, Todd A.; Johann, Donald J.; Bisgin, Halil; Sahraeian, Sayed Mohammad Ebrahim; Bushel, Pierre R.; Pirooznia, Mehdi; Wilkins, Katherine; Chierici, Marco; Bao, Wenjun; Basehore, Lee Scott; Lucas, Anne Bergstrom; Burgess, Daniel; Butler, Daniel J.; Cawley, Simon; Chang, Chia-Jung; Chen, Guangchun; Chen, Tao; Chen, Yun-Ching; Craig, Daniel J.; del Pozo, Angela; Foox, Jonathan; Francescatto, Margherita; Fu, Yutao; Furlanello, Cesare; Giorda, Kristina; Grist, Kira P.; Guan, Meijian; Hao, Yingyi; Happe, Scott; Hariani, Gunjan; Haseley, Nathan; Jasper, Jeff; Jurman, Giuseppe; Kreil, David Philip; Łabaj, Paweł; Lai, Kevin; Li, Jianying; Li, Quan-Zhen; Li, Yulong; Li, Zhiguang; Liu, Zhichao; López, Mario Solís; Miclaus, Kelci; Miller, Raymond; Mittal, Vinay K.; Mohiyuddin, Marghoob; Pabón-Peña, Carlos; Parsons, Barbara L.; Qiu, Fujun; Scherer, Andreas; Shi, Tieliu; Stiegelmeyer, Suzy; Suo, Chen; Tom, Nikola; Wang, Dong; Wen, Zhining; Wu, Leihong; Xiao, Wenzhong; Xu, Chang; Yu, Ying; Zhang, Jiyang; Zhang, Yifan; Zhang, Zhihong; Zheng, Yuanting; Mason, Christopher E.; Willey, James C.; Tong, Weida; Shi, Leming; Xu, Joshua,
   A verified genomic reference sample for assessing performance of cancer panels detecting small variants of low allele frequency,
   in «GENOME BIOLOGY»,
   vol. 22,
   n. 1,
   2021
   , pp. 111 -
5. Bussola, Nicole; Marcolini, Alessia; Maggio, Valerio; Jurman, Giuseppe; Furlanello, Cesare,
   AI Slipping on Tiles: Data Leakage in Digital Pathology,
   Proceedings of International Conference on Pattern Recognition (ICPR 2021),
   Springer,
   2021
   , pp. 167 -
   182
6. Chicco, Davide; Jurman, Giuseppe,
   The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation,
   in «BMC GENOMICS»,
   vol. 21,
   n. 1,
   2020
   , pp. 6 -
7. Chicco, Davide; Jurman, Giuseppe,
   Machine learning can predict survival of patients with heart failure from serum creatinine and ejection fraction alone,
   in «BMC MEDICAL INFORMATICS AND DECISION MAKING»,
   vol. 20,
   n. 1,
   2020
   , pp. 16 -
8. Chierici, Marco; Francescatto, Margherita; Bussola, Nicole; Jurman, Giuseppe; Furlanello, Cesare,
   Predictability of drug-induced liver injury by machine learning,
   in «BIOLOGY DIRECT»,
   vol. 15,
   n. 1,
   2020
9. Franch, Gabriele; Nerini, Daniele; Pendesini, Marta; Coviello, Luca; Jurman, Giuseppe; Furlanello, Cesare,
   Precipitation Nowcasting with Orographic Enhanced Stacked Generalization: Improving Deep Learning Predictions on Extreme Events,
   in «ATMOSPHERE»,
   vol. 11,
   n. 3,
   2020
   , pp. 267 -
10. Chierici, Marco; Bussola, Nicole; Marcolini, Alessia; Francescatto, Margherita; Zandonà, Alessandro; Trastulla, Lucia; Agostinelli, Claudio; Jurman, Giuseppe; Furlanello, Cesare,
    Integrative Network Fusion: a multi-omics approach in molecular profiling,
    in «FRONTIERS IN ONCOLOGY»,
    vol. 10,
    2020
    , pp. 1065 -

Pages