Author

Marta Lucchetta

Danish Cancer SocietyORCID 0000-0002-2641-4792Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Bioinformatics· 3 Systems Biology· 1

Signature Assays, Organisms & Methods

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Approach:statistical model· 4 Assay:bulk RNA-seq· 3 Research Focus:differential expression· 3 Organism:human· 3 Research Focus:benchmarking· 2 Research Focus:batch correction / integration· 1 Research Focus:gene regulatory network inference· 1 Assay:protein structure· 1

Frequent Co-Authors

Elena PapaleoiD· 2 shared Mohamed Mounir· 2 shared Antonio ColapricoiD· 1 shared Catharina OlseniD· 1 shared David B. BlumenthaliD· 1 shared Gianluca BontempiiD· 1 shared Houtan NoushmehriD· 1 shared Isabelle da Piedade· 1 shared

Activity by Year

2019-2024 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 18 mutual co-author links

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2 shared papersBioinformatics

Elena PapaleoORCID

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4 papers

Recent·Most cited

Emergence of power-law distributions in protein-protein interaction networks through study bias

David B. Blumenthal, Marta Lucchetta, Linda Kleist, Sándor P. Fekete, Markus List, Martin H. Schaefer

This study shows that power-law distributions in PPI networks can arise from study and technical biases, not biological properties.

eLife · Dec 11, 2024Peer-reviewedSystems Biology

Finding disease modules for cancer and COVID-19 in gene co-expression networks with the Core&Peel method

Marta Lucchetta, Marco Pellegrini

The paper introduces the Core&Peel method to identify disease modules in gene co-expression networks, showing its effectiveness in detecting relevant pathways for cancer and COVID-19.

Scientific Reports · Oct 19, 2020Peer-reviewedBioinformatics

Analyses of cancer data in the Genomic Data Commons Data Portal with new functionalities in the TCGAbiolinks R/Bioconductor package

Mohamed Mounir, Marta Lucchetta, Tiago C. Silva, Catharina Olsen, Gianluca Bontempi, Xi Chen, Houtan Noushmehr, Antonio Colaprico, Elena Papaleo

The paper introduces new features in the TCGAbiolinks R/Bioconductor package to improve cancer data analysis, including better differential expression analysis, tumor purity estimation, and integration of normal samples from GTEx.

PLoS Computational Biology · Mar 5, 2019Peer-reviewedBioinformatics

Different molecular signatures in lung cancer types from integrative bioinformatic analyses of RNASeq data

Marta Lucchetta, Isabelle da Piedade, Mohamed Mounir, Marina Vabistsevits, Thilde Terkelsen, Elena Papaleo

The paper analyzes RNA-Seq data from lung cancer subtypes to identify molecular signatures and immune-related features associated with LUAD and LUSC.

bioRxiv · Jan 21, 2019 · PreprintBioinformatics