Author

Luca De Sano

University of Milano-Bicocca

Listed by name: these papers carry no ORCID for this author, so different people with the same name may be mixed together.

Biology Disciplines

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Bioinformatics· 7

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Approach:statistical model· 5 Research Focus:clustering· 2 Research Focus:gene regulatory network inference· 2 Approach:machine learning (classical)· 2 Research Focus:software / tool· 2 Research Focus:trajectory inference· 2 Research Focus:variant calling· 2 Assay:whole-exome sequencing· 2

Frequent Co-Authors

Daniele RamazzottiiD· 7 shared Alex GraudenziiD· 5 shared Giulio CaravagnaiD· 5 shared Marco AntoniottiiD· 5 shared Bud Mishra· 3 shared Giancarlo MauriiD· 3 shared Alessandra Pirola· 1 shared Bo WangiD· 1 shared

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2015-2019 · peak 2/yr

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10 top collaborators · 23 mutual co-author links

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7 shared papersBioinformatics

Daniele RamazzottiORCID

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7 papers

Recent·Most cited

cyTRON and cyTRON/JS: two Cytoscape-based applications for the inference of cancer evolution models

Lucrezia Patruno, Edoardo Galimberti, Daniele Ramazzotti, Giulio Caravagna, Luca De Sano, Marco Antoniotti, Alex Graudenzi

The paper introduces two Cytoscape-based tools, cyTRON and cyTRON/JS, for inferring cancer evolution models from genomic and proteomics data.

bioRxiv · Jul 20, 2019 · PreprintBioinformatics

Learning mutational graphs of individual tumour evolution from single-cell and multi-region sequencing data

Daniele Ramazzotti, Alex Graudenzi, Luca De Sano, Marco Antoniotti, Giulio Caravagna

TRaIT infers mutational graphs from single-cell and multi-region sequencing data to model tumour evolution, improving accuracy and robustness.

BMC Bioinformatics · Apr 25, 2019Peer-reviewedBioinformatics

SIMLR: A Tool For Large-Scale Single-Cell Analysis By Multi-Kernel Learning

Bo Wang, Daniele Ramazzotti, Luca De Sano, Junjie Zhu, Emma Pierson, Serafim Batzoglou

SIMLR is a tool for single-cell RNA-seq analysis that improves clustering and visualization through multi-kernel learning.

PROTEOMICS · Dec 19, 2017Peer-reviewedBioinformatics

OncoScore: a novel, Internet-based tool to assess the oncogenic potential of genes

Rocco Piazza, Daniele Ramazzotti, Roberta Spinelli, Alessandra Pirola, Luca De Sano, Pierangelo Ferrari, Vera Magistroni, Nicoletta Cordani, Nitesh Devinarayan Sharma, Carlo B Gambacorti-Passerini

OncoScore is a text-mining tool that ranks genes by their association with cancer, achieving high accuracy in prioritizing cancer-related genes.

Scientific Reports · Apr 7, 2017Peer-reviewedBioinformatics

Algorithmic Methods to Infer the Evolutionary Trajectories in Cancer Progression

Giulio Caravagna, Alex Graudenzi, DANIELE RAMAZZOTTI, Rebeca Sanz-Pamplona, Luca De Sano, Giancarlo Mauri, Victor Moreno, Marco Antoniotti, Bud Mishra

The paper introduces PiCnIc, a machine learning pipeline to infer cancer progression models from NGS data, demonstrating its application to colorectal cancer and suggesting novel hypotheses.

Proceedings of the National Academy of Sciences · Jun 28, 2016Peer-reviewedBioinformatics

TRONCO: an R package for the inference of cancer progression models from heterogeneous genomic data

Luca De Sano, Giulio Caravagna, DANIELE RAMAZZOTTI, Alex Graudenzi, Giancarlo Mauri, Bud Mishra, Marco Antoniotti

TRONCO infers cancer progression models from genomic data, providing population-level and individual-level insights into clonal evolution and mutation accumulation.

Bioinformatics · Feb 9, 2016Peer-reviewedBioinformatics

Design of the TRONCO BioConductor Package for TRanslational ONCOlogy

Marco Antoniotti, Giulio Caravagna, Luca De Sano, Alex Graudenzi, Giancarlo Mauri, Bud Mishra, Daniele Ramazzotti

TRONCO is an R package for cancer progression modeling using cross-sectional genomic data, offering improved algorithms and tools for analysis and visualization.

bioRxiv · Oct 22, 2015 · PreprintBioinformatics