Publications

(For a full list see below or go to Google Scholar)

1.
Blomquist, M. R. et al. Temporospatial genomic profiling in glioblastoma identifies commonly altered core pathways underlying tumor progression. Neurooncol Adv 2, vdaa078 (2020). Cite Download
1.
Caruso, F. P. et al. A map of tumor–host interactions in glioma at single-cell resolution. GigaScience 9, giaa109 (2020). Cite Download
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.
Roelands, J. et al. Oncogenic states dictate the prognostic and predictive connotations of intratumoral immune response. J Immunother Cancer 8, (2020). 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.
Tagliaferri, D. et al. Retinoic Acid Induces Embryonic Stem Cells (ESCs) Transition to 2 Cell-Like State Through a Coordinated Expression of Dux and Duxbl1. Front. Cell Dev. Biol. 7, 385 (2020). Cite Download
1.
Sa, J. K. et al. Transcriptional regulatory networks of tumor-associated macrophages that drive malignancy in mesenchymal glioblastoma. Genome Biol 21, 216 (2020). Cite Download
1.
Dezső, Z. & Ceccarelli, M. Machine learning prediction of oncology drug targets based on protein and network properties. BMC Bioinformatics 21, 104 (2020). Cite Download
1.
Mauriello, A. et al. High Somatic Mutation and Neoantigen Burden Do Not Correlate with Decreased Progression-Free Survival in HCC Patients not Undergoing Immunotherapy. Cancers (Basel) 11, (2019). 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.
Guerriero, I. et al. Exploring the Molecular Crosstalk between Pancreatic Bud and Mesenchyme in Embryogenesis: Novel Signals Involved. Int J Mol Sci 20, (2019). Cite Download
1.
Bedognetti, D. et al. Toward a comprehensive view of cancer immune responsiveness: a synopsis from the SITC workshop. J Immunother Cancer 7, 131 (2019). 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.
Silvestri, E. et al. 3,5-Diiodo-L-Thyronine Exerts Metabolically Favorable Effects on Visceral Adipose Tissue of Rats Receiving a High-Fat Diet. Nutrients 11, 278 (2019). Cite Download
1.
Zhang, J. et al. The combination of neoantigen quality and T lymphocyte infiltrates identifies glioblastomas with the longest survival. Communications Biology 2, (2019). Cite Download
1.
Giordano, G. et al. JAK/Stat5-mediated subtype-specific lymphocyte antigen 6 complex, locus G6D (LY6G6D) expression drives mismatch repair proficient colorectal cancer. Journal of Experimental & Clinical Cancer Research 38, (2019). Cite Download
1.
Monaco, G. et al. RNA-Seq Signatures Normalized by mRNA Abundance Allow Absolute Deconvolution of Human Immune Cell Types. Cell Reports 26, 1627-1640.e7 (2019). 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.
Silva, T. C. et al. TCGAbiolinksGUI: A graphical user interface to analyze cancer molecular and clinical data. F1000Research 7, 439 (2018). Cite Download
1.
Mall, R., Ullah, E., Kunji, K., Ceccarelli, M. & Bensmail, H. An unsupervised disease module identification technique in biological networks using novel quality metric based on connectivity, conductance and modularity. F1000Research 7, 378 (2018). Cite Download
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.
Frattini, V. et al. A metabolic function of FGFR3-TACC3 gene fusions in cancer. Nature 553, 222–227 (2018). Cite Download
1.
Yuan, J. et al. Single-cell transcriptome analysis of lineage diversity in high-grade glioma. Genome Medicine 10, (2018). Cite Download
1.
Rosenberg, S. et al. A recurrent point mutation in PRKCA is a hallmark of chordoid gliomas. Nature Communications 9, (2018). Cite Download
1.
Pascarella, A. et al. DNAJC17 is localized in nuclear speckles and interacts with splicing machinery components. Scientific Reports 8, (2018). 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.
Roelands, J. et al. A collection of annotated and harmonized human breast cancer transcriptome datasets, including immunologic classification. F1000Research 6, 296 (2017). Cite Download
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.
Ventola, G. M. M. et al. Identification of long non-coding transcripts with feature selection: a comparative study. BMC Bioinformatics 18, (2017). Cite Download
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
1.
Colaprico, A. et al. TCGAbiolinks: an R/Bioconductor package for integrative analysis of TCGA data. Nucleic Acids Res. 44, e71 (2016). Cite
1.
Bedognetti, D., Hendrickx, W., Ceccarelli, M., Miller, L. D. & Seliger, B. Disentangling the relationship between tumor genetic programs and immune responsiveness. Curr. Opin. Immunol. 39, 150–158 (2016). Cite
1.
Ciccarelli, M. et al. The possible role of chromosome X variability in hypertensive familiarity. J Hum Hypertens (2016) http://doi.org/10.1038/jhh.2016.9. Cite
1.
Ceccarelli, M. et al. Molecular Profiling Reveals Biologically Discrete Subsets and Pathways of Progression in Diffuse Glioma. Cell 164, 550–563 (2016). Cite
1.
Tagliaferri, D. et al. Retinoic Acid Specifically Enhances Embryonic Stem Cell Metastate Marked by Zscan4. PLoS ONE 11, e0147683 (2016). Cite
1.
Porreca, I. et al. ‘Stockpile’ of Slight Transcriptomic Changes Determines the Indirect Genotoxicity of Low-Dose BPA in Thyroid Cells. PLoS ONE 11, e0151618 (2016). Cite
1.
Malanga, D. et al. The Akt1/IL-6/STAT3 pathway regulates growth of lung tumor initiating cells. Oncotarget (2015) http://doi.org/10.18632/oncotarget.5626. Cite
1.
Anjum, S., Morganella, S., D’Angelo, F., Iavarone, A. & Ceccarelli, M. VEGAWES: variational segmentation on whole exome sequencing for copy number detection. BMC Bioinformatics 16, 315 (2015). Cite