Giuseppe Jurman
Giuseppe Jurman
Vahvistettu sähköpostiosoite verkkotunnuksessa - Kotisivu
A promoter-level mammalian expression atlas
Fantom Consortium
Nature 507 (7493), 462, 2014
The MAQC-II project: a comprehensive study of common practices for the development and validation of microarray-based predictive models.
L Shi, G Campbell, W Jones, F Campagne, Z Wen, S Walker, Z Su, T Chu, ...
Repeatability of published microarray gene expression analyses
JPA Ioannidis, DB Allison, CA Ball, I Coulibaly, X Cui, AC Culhane, ...
Nature genetics 41 (2), 149, 2009
The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation
D Chicco, G Jurman
BMC genomics 21 (1), 1-13, 2020
The concordance between RNA-seq and microarray data depends on chemical treatment and transcript abundance
C Wang, B Gong, PR Bushel, J Thierry-Mieg, D Thierry-Mieg, J Xu, ...
Nature biotechnology 32 (9), 926-932, 2014
A comparison of MCC and CEN error measures in multi-class prediction
G Jurman, S Riccadonna, C Furlanello
PloS one 7 (8), e41882, 2012
Entropy-based gene ranking without selection bias for the predictive classification of microarray data
C Furlanello, M Serafini, S Merler, G Jurman
BMC bioinformatics 4 (1), 1-20, 2003
Minerva and minepy: a C engine for the MINE suite and its R, Python and MATLAB wrappers
D Albanese, M Filosi, R Visintainer, S Riccadonna, G Jurman, ...
Bioinformatics, bts707, 2012
Algebraic stability indicators for ranked lists in molecular profiling
G Jurman, S Merler, A Barla, S Paoli, A Galea, C Furlanello
Bioinformatics 24 (2), 258-264, 2008
Clinical value of prognosis gene expression signatures in colorectal cancer: a systematic review
R Sanz-Pamplona, A Berenguer, D Cordero, S Riccadonna, X Sole, ...
PloS one 7 (11), e48877, 2012
An accelerated procedure for recursive feature ranking on microarray data
C Furlanello, M Serafini, S Merler, G Jurman
Neural Networks 16 (5-6), 641-648, 2003
mlpy: Machine learning Python
D Albanese, R Visintainer, S Merler, S Riccadonna, G Jurman, ...
arXiv preprint arXiv:1202.6548, 2012
Canberra distance on ranked lists
G Jurman, S Riccadonna, R Visintainer, C Furlanello
Proceedings of advances in ranking NIPS 09 workshop, 22-27, 2009
Machine learning methods for predictive proteomics
A Barla, G Jurman, S Riccadonna, S Merler, M Chierici, C Furlanello
Briefings in bioinformatics 9 (2), 119-128, 2008
Gene expression profiling identifies potential relevant genes in alveolar rhabdomyosarcoma pathogenesis and discriminates PAX3‐FKHR positive and negative tumors
C De Pitta, L Tombolan, G Albiero, F Sartori, C Romualdi, G Jurman, ...
International journal of cancer 118 (11), 2772-2781, 2006
PD-L1 is a therapeutic target of the bromodomain inhibitor JQ1 and, combined with HLA class I, a promising prognostic biomarker in neuroblastoma
O Melaiu, M Mina, M Chierici, R Boldrini, G Jurman, P Romania, ...
Clinical Cancer Research 23 (15), 4462-4472, 2017
Machine learning can predict survival of patients with heart failure from serum creatinine and ejection fraction alone
D Chicco, G Jurman
BMC medical informatics and decision making 20 (1), 16, 2020
Functional analysis of multiple genomic signatures demonstrates that classification algorithms choose phenotype-related genes
W Shi, M Bessarabova, D Dosymbekov, Z Dezso, T Nikolskaya, ...
The pharmacogenomics journal 10 (4), 310-323, 2010
Phylogenetic convolutional neural networks in metagenomics
D Fioravanti, Y Giarratano, V Maggio, C Agostinelli, M Chierici, G Jurman, ...
BMC bioinformatics 19 (2), 1-13, 2018
Deep learning for automatic stereotypical motor movement detection using wearable sensors in autism spectrum disorders
NM Rad, SM Kia, C Zarbo, T van Laarhoven, G Jurman, P Venuti, ...
Signal Processing 144, 180-191, 2018
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Artikkelit 1–20