Marco Chierici
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A promoter-level mammalian expression atlas
Nature 507 (7493), 462-470, 2014
A comprehensive assessment of RNA-seq accuracy, reproducibility and information content by the Sequencing Quality Control Consortium
Nature biotechnology 32 (9), 903-914, 2014
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
Comparison of RNA-seq and microarray-based models for clinical endpoint prediction
W Zhang, Y Yu, F Hertwig, J Thierry-Mieg, W Zhang, D Thierry-Mieg, ...
Genome biology 16, 1-12, 2015
Differential roles of epigenetic changes and Foxp3 expression in regulatory T cell-specific transcriptional regulation
H Morikawa, N Ohkura, A Vandenbon, M Itoh, S Nagao-Sato, H Kawaji, ...
Proceedings of the National Academy of Sciences 111 (14), 5289-5294, 2014
Cellular and gene signatures of tumor-infiltrating dendritic cells and natural-killer cells predict prognosis of neuroblastoma
O Melaiu, M Chierici, V Lucarini, G Jurman, LA Conti, R De Vito, ...
Nature Communications 11 (1), 5992, 2020
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
Phylogenetic convolutional neural networks in metagenomics
D Fioravanti, Y Giarratano, V Maggio, C Agostinelli, M Chierici, G Jurman, ...
BMC bioinformatics 19, 1-13, 2018
Microbial community structure in vineyard soils across altitudinal gradients and in different seasons
PE Corneo, A Pellegrini, L Cappellin, M Roncador, M Chierici, C Gessler, ...
FEMS Microbiology Ecology 84 (3), 588-602, 2013
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
Evaluating reproducibility of AI algorithms in digital pathology with DAPPER
A Bizzego, N Bussola, M Chierici, V Maggio, M Francescatto, L Cima, ...
PLoS computational biology 15 (3), e1006269, 2019
Focal adhesion kinase depletion reduces human hepatocellular carcinoma growth by repressing enhancer of zeste homolog 2
D Gnani, I Romito, S Artuso, M Chierici, C De Stefanis, N Panera, ...
Cell Death & Differentiation 24 (5), 889-902, 2017
Integrating deep and radiomics features in cancer bioimaging
A Bizzego, N Bussola, D Salvalai, M Chierici, V Maggio, G Jurman, ...
2019 IEEE Conference on Computational Intelligence in Bioinformatics and …, 2019
Identification of GALNT14 as a novel neuroblastoma predisposition gene
M De Mariano, R Gallesio, M Chierici, C Furlanello, M Conte, A Garaventa, ...
Oncotarget 6 (28), 26335, 2015
Multi-omics integration for neuroblastoma clinical endpoint prediction
M Francescatto, M Chierici, S Rezvan Dezfooli, A Zandoną, G Jurman, ...
Biology direct 13, 1-12, 2018
Integrative network fusion: a multi-omics approach in molecular profiling
M Chierici, N Bussola, A Marcolini, M Francescatto, A Zandoną, ...
Frontiers in oncology 10, 1065, 2020
Predictability of drug-induced liver injury by machine learning
M Chierici, M Francescatto, N Bussola, G Jurman, C Furlanello
Biology direct 15, 1-10, 2020
A verified genomic reference sample for assessing performance of cancer panels detecting small variants of low allele frequency
W Jones, B Gong, N Novoradovskaya, D Li, R Kusko, TA Richmond, ...
Genome biology 22, 1-38, 2021
A pan-cancer approach to predict responsiveness to immune checkpoint inhibitors by machine learning
M Polano, M Chierici, M Dal Bo, D Gentilini, F Di Cintio, L Baboci, ...
Cancers 11 (10), 1562, 2019
Variability in GWAS analysis: the impact of genotype calling algorithm inconsistencies
K Miclaus, M Chierici, C Lambert, L Zhang, S Vega, H Hong, S Yin, ...
The pharmacogenomics journal 10 (4), 324-335, 2010
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