Daniele Ramazzotti

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Alex GraudenziDept. of Informatics, Systems and Communication, Univ. of Milan-BicoccaVerified email at unimib.it
Marco AntoniottiProfessor of Computer Science, Università degli Studi di Milano-BicoccaVerified email at unimib.it
Giulio CaravagnaCancer Data Science Laboratory, University of TriesteVerified email at units.it
Rocco PiazzaUniversity of Milano-BicoccaVerified email at unimib.it
Bud MishraProfessor of Computer Science and MathematicsVerified email at nyu.edu
Luca De SanoUniversità degli Studi di Milano BicoccaVerified email at campus.unimib.it
Giancarlo MauriProfessor emeritus of Computer Science - University of Milano-BicoccaVerified email at unimib.it
Davide MasperoSingle Cell Genomics Team - Centre Nacional d’Anàlisi Genòmica (CNAG-CRG), Barcelona, SpainVerified email at cnag.crg.eu
Fabrizio AngaroniHuman TechnopoleVerified email at fht.org
Bo WangChief AI Scientist,UHN; CIFAR AI Chair, Vector Institute; Assistant Professor, U of TorontoVerified email at vectorinstitute.ai
Serafim BatzoglouSeerVerified email at seer.bio
Junjie ZhuApple Inc.Verified email at stanford.edu
Carlo Gambacorti PasseriniProfessorVerified email at unimib.it
Loes Olde LoohuisAssistant professor at UCLAVerified email at mednet.ucla.edu
Emma PiersonCornell TechVerified email at cornell.edu
Avantika LalGenentechVerified email at gene.com
Leo Anthony CeliMassachusetts Institute of TechnologyVerified email at mit.edu
Guido SanguinettiReader in Informatics, University of EdinburghVerified email at inf.ed.ac.uk
Andrea SottorivaHuman TechnopoleVerified email at fht.org
Mauro CastelliNOVA IMS, Universidade Nova de LisboaVerified email at novaims.unl.pt

Daniele Ramazzotti

Department of Medicine and Surgery, University of Milano-Bicocca, Milano, Italy

Verified email at unimib.it - Homepage

Computer Science Cancer Genomics Machine Learning


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Visualization and analysis of single-cell RNA-seq data by kernel-based similarity learning B Wang, J Zhu, E Pierson, D Ramazzotti, S Batzoglou Nature Methods 14 (4), 414-416, 2017	596	2017
Assessment of network module identification across complex diseases S Choobdar, ME Ahsen, J Crawford, M Tomasoni, T Fang, D Lamparter, ... Nature Methods 16 (9), 843-852, 2019	250*	2019
Secondary analysis of electronic health records MITC Data Springer International Publishing, 2016	247*	2016
Detecting repeated cancer evolution from multi-region tumor sequencing data G Caravagna, Y Giarratano, D Ramazzotti, I Tomlinson, TA Graham, ... Nature methods 15 (9), 707-714, 2018	146	2018
Multi-omic tumor data reveal diversity of molecular mechanisms that correlate with survival D Ramazzotti, A Lal, B Wang, S Batzoglou, A Sidow Nature Communications 9 (1), 4453, 2018	137	2018
CAPRI: efficient inference of cancer progression models from cross-sectional data D Ramazzotti, G Caravagna, LO Loohuis, A Graudenzi, I Korsunsky, ... Bioinformatics 31 (18), 3016-3026, 2015	94	2015
Exposing the probabilistic causal structure of discrimination F Bonchi, S Hajian, B Mishra, D Ramazzotti International Journal of Data Science and Analytics, 2015	90	2015
Algorithmic methods to infer the evolutionary trajectories in cancer progression G Caravagna, A Graudenzi, D Ramazzotti, R Sanz-Pamplona, L De Sano, ... Proceedings of the National Academy of Sciences 113 (28), E4025-E4034, 2016	89	2016
SIMLR: A Tool for Large‐Scale Genomic Analyses by Multi‐Kernel Learning B Wang, D Ramazzotti, L De Sano, J Zhu, E Pierson, S Batzoglou PROTEOMICS, 2017	88*	2017
Inferring tree causal models of cancer progression with probability raising LO Loohuis, G Caravagna, A Graudenzi, D Ramazzotti, G Mauri, ... PloS one 9 (10), e108358, 2014	67	2014
Mutational signatures and heterogeneous host response revealed via large-scale characterization of SARS-CoV-2 genomic diversity A Graudenzi, D Maspero, F Angaroni, R Piazza, D Ramazzotti iScience, 102116, 2021	62	2021
Phenotypic plasticity and genetic control in colorectal cancer evolution J Househam, T Heide, GD Cresswell, I Spiteri, C Kimberley, L Zapata, ... Nature, 1-10, 2022	55	2022
Learning the structure of Bayesian Networks: A quantitative assessment of the effect of different algorithmic schemes S Beretta, M Castelli, I Gonçalves, R Henriques, D Ramazzotti Complexity 2018, 2018	54	2018
TRONCO: an R package for the inference of cancer progression models from heterogeneous genomic data L De Sano, G Caravagna, D Ramazzotti, A Graudenzi, G Mauri, B Mishra, ... Bioinformatics 32 (12), 1911-1913, 2016	45	2016
The co-evolution of the genome and epigenome in colorectal cancer T Heide, J Househam, GD Cresswell, I Spiteri, C Lynn, M Mossner, ... Nature, 1-11, 2022	39	2022
VERSO: a comprehensive framework for the inference of robust phylogenies and the quantification of intra-host genomic diversity of viral samples D Ramazzotti, F Angaroni, D Maspero, C Gambacorti-Passerini, ... Patterns, 100212, 2021	39*	2021
OncoScore: a novel, Internet-based tool to assess the oncogenic potential of genes R Piazza, D Ramazzotti, R Spinelli, A Pirola, L De Sano, P Ferrari, ... Scientific reports 7, 46290, 2017	39	2017
Learning mutational graphs of individual tumour evolution from single-cell and multi-region sequencing data D Ramazzotti, A Graudenzi, L De Sano, M Antoniotti, G Caravagna BMC Bioinformatics 20 (1), 210, 2019	38	2019
De novo mutational signature discovery in tumor genomes using SparseSignatures A Lal, K Liu, R Tibshirani, A Sidow, D Ramazzotti PLOS Computational Biology 17 (6), e1009119, 2021	34*	2021
Improved survival of cancer patients admitted to the intensive care unit between 2002 and 2011 at a US Teaching Hospital CM Sauer, J Dong, LA Celi, D Ramazzotti Cancer Research and Treatment: Official Journal of Korean Cancer Association …, 2019	32	2019

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