(For a full list see below)
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Garofano, L. et al. Pathway-based classification of glioblastoma uncovers a mitochondrial subtype with therapeutic vulnerabilities. Nat Cancer (2021) http://doi.org/10.1038/s43018-020-00159-4. Cite
1.
de Falco, A. et al. Adaptive One-Class gaussian processes allow accurate prioritization of oncology drug targets. Bioinformatics btaa968 (2020) http://doi.org/10.1093/bioinformatics/btaa968. Cite Download
1.
Caruso, F. P., Scala, Giovanni, Cerulo, L. & Ceccarelli, M. A review of COVID-19 biomarkers and drug targets: resources and tools. Briefings in Bioinformatics (2020) http://doi.org/10.1093/bib/bbaa328. Cite Download
1.
Turan, T. et al. A balance score between immune stimulatory and suppressive microenvironments identifies mediators of tumour immunity and predicts pan-cancer survival. Br J Cancer (2020) http://doi.org/10.1038/s41416-020-01145-4. Cite
1.
Noviello, T. M. R., Ceccarelli, M. & Cerulo, L. Deep learning predicts non-coding RNA functions from only raw sequence data. http://biorxiv.org/lookup/doi/10.1101/2020.05.27.118778 (2020) doi:10.1101/2020.05.27.118778. Cite Download
1.
Sayaman, R. W. et al. Germline genetic contribution to the immune landscape of cancer. http://biorxiv.org/lookup/doi/10.1101/2020.01.30.926527 (2020) doi:10.1101/2020.01.30.926527. Cite Download
1.
Caruso, F. P. et al. A MAP of tumor-host interactions in glioma at single cell resolution. http://biorxiv.org/lookup/doi/10.1101/827758 (2019) doi:10.1101/827758. Cite Download
1.
Di Iorio, B. R. et al. Treatment of metabolic acidosis with sodium bicarbonate delays progression of chronic kidney disease: the UBI Study. J. Nephrol. (2019) http://doi.org/10.1007/s40620-019-00656-5. Cite Download
1.
Roelands, J. et al. Genomic landscape of tumor-host interactions with differential prognostic and predictive connotations. http://biorxiv.org/lookup/doi/10.1101/546069 (2019) doi:10.1101/546069. Cite Download
1.
Bertucci, F. et al. The immunologic constant of rejection classification refines the prognostic value of conventional prognostic signatures in breast cancer. British Journal of Cancer (2018) http://doi.org/10.1038/s41416-018-0309-1. Cite Download
1.
Ferreira, L. A. M. et al. Circulating microRNAs expression profile in newly diagnosed and imatinib treated chronic phase – chronic myeloid leukemia. Leukemia & Lymphoma 1–7 (2018) http://doi.org/10.1080/10428194.2018.1499905. Cite
1.
Silvestri, E. et al. 3,5-Diiodo-L-Thyronine Affects Structural and Metabolic Features of Skeletal Muscle Mitochondria in High-Fat-Diet Fed Rats Producing a Co-adaptation to the Glycolytic Fiber Phenotype. Frontiers in Physiology 9, (2018). Cite
1.
Mall, R. et al. RGBM: regularized gradient boosting machines for identification of the transcriptional regulators of discrete glioma subtypes. Nucleic Acids Research (2018) http://doi.org/10.1093/nar/gky015. Cite Download
1.
Angelova, M. et al. Evolution of Metastases in Space and Time under Immune Selection. Cell (2018) http://doi.org/10.1016/j.cell.2018.09.018. Cite
1.
De Marco, C. et al. Specific gene expression signatures induced by the multiple oncogenic alterations that occur within the PTEN/PI3K/AKT pathway in lung cancer. PLOS ONE 12, e0178865 (2017). Cite
1.
Silva, T. C. et al. TCGAbiolinksGUI: A graphical user interface to analyze cancer molecular and clinical data. (2017) http://doi.org/10.1101/147496. Cite Download
1.
Mall, R. et al. Differential Community Detection in Paired Biological Networks. (2017) http://doi.org/10.1101/147538. Cite
1.
Pancione, M. et al. Emerging insight into MAPK inhibitors and immunotherapy in colorectal cancer. Curr. Med. Chem. (2017) http://doi.org/10.2174/0929867324666170227114356. Cite
1.
Hendrickx, W. et al. Identification of genetic determinants of breast cancer immune phenotypes by integrative genome-scale analysis. OncoImmunology e1253654 (2017) http://doi.org/10.1080/2162402X.2016.1253654. Cite
1.
Mall, R., Cerulo, L., Bensmail, H., Iavarone, A. & Ceccarelli, M. Detection of statistically significant network changes in complex biological networks. BMC Systems Biology 11, (2017). Cite
1.
Mall, R. et al. RGBM: Regularized Gradient Boosting Machines For The Identification of Transcriptional Regulators Of Discrete Glioma Subtypes. bioRxiv (2017) http://doi.org/10.1101/132670. Cite
1.
Porreca, I. et al. Pesticide toxicogenomics across scales: in vitro transcriptome predicts mechanisms and outcomes of exposure in vivo. Scientific Reports 6, 38131 (2016). Cite
1.
Agnihotri, S. et al. The genomic landscape of schwannoma. Nat. Genet. 48, 1339–1348 (2016). Cite
1.
Petrizzo, A. et al. Identification and Validation of HCC-specific Gene Transcriptional Signature for Tumor Antigen Discovery. Sci Rep 6, 29258 (2016). Cite
1.
Silva, T. C. et al. TCGA Workflow: Analyze cancer genomics and epigenomics data using Bioconductor packages. F1000Research 5, 1542 (2016). Cite