Claudia Angelini List of Papers

Papers on ISI Journals

  1. 1. S. Gatto, M. Gagliardi, M. Franzese, S. Leppert, M. Papa, M. Cammisa, G. Grillo, G. Velasco, C. Francastel, S. Toubiana, M. D'Esposito, C Angelini, M. R Matarazzo. ICF-specific DNMT3B dysfunction interferes with intragenic regulation of mRNA transcription and alternative splicing . Nucleic Acid Research (2017)
  2. 2. V. Costa, D. Righelli, F. Russo, P. De Berardinis, C Angelini, L. D'Apice. Distinct antigen delivery systems induce dendritic cells divergent transcriptional response: new insights from a comparative and reproducible computational analysis. International Journal of Molecular Sciences, 18 (3), E494, (2017)
  3. 3. C. D’Aniello, E. Habibi, F. Cermola,D. Paris, F. Russo, A. Fiorenzano, G. Di Napoli, D. J. Melck, G.Cobellis, C. Angelini, A. Fico, R. Blelloch, A. Motta, H. G. Stunnenberg, D. De Cesare, E.J. Patriarca, and G. Minchiotti, Vitamin C and L-Proline Antagonistic Effects Capture Alternative States in the Pluripotency Continuum Stem Cell Reports, 8(1), pp. 1-10, (2017)
  4. 4. E Di Costanzo, V Ingangi, C Angelini, MF Carfora, MV Carriero, R Natalini, A Macroscopic Mathematical Model For Cell Migration Assays Using A Real-Time Cell Analysis PLoS ONE 11(9): e0162553 (2016).
  5. 5. A. Fiorenzano, E. Pascale, C. D'Aniello, D. Acampora, C. Bassalert, F. Russo, G. Andolfi, M. Biffoni, F. Francescangeli, A. Zeuner, C Angelini, C. Chazaud, E. J. Patriarca, A. Fico, G.Minchiotti. Cripto is essential to capture mouse epiblast stem cell and human embryonic stem cell pluripotency, Nature Communications 7, Article number: 12589 (2016)
  6. 6. A. Iuliano, A. Occhipinti, I. De Feis, C. Angelini, P. Lio'. Cancer markers selection using network-based Cox Regression: a methodological and computational practice., Frontiers in Physiology (2016).
  7. 7. G.F. Fulcoli, M. Franzese, X. Liu, Z. Zhang, C. Angelini,A. Baldini. Rebalancing gene haploinsufficiency in a mouse model of DiGeorge syndrome by targeting chromatin., Nature Communications, 7, Article number: 11688, (2016).
  8. 8. V. Riso, M. Cammisa, H. Kukreja, Z. Anvar, G. Verde, A. Sparago, B. Acurzio, S. Lad, E. Lonardo, A. Sankar, K. Helin, R. Feil, A. Fico, C. Angelini, G. Grimaldi, A. Riccio . ZFP57 maintains the parent-of-origin-specific expression of the imprinted genes and differentially affects non-imprinted targets in mouse embryonic stem cells, Nucleic Acids Research (2016).
  9. 8. M. Pinelli, A. Carissimo, L. Cutillo, CH Lai, M. Mutarelli, MN Moretti, MV Singh, M Karali, D. Carrella, M. Pizzo, F. Russo, S. Ferrari, D. Ponzin, C. Angelini , S. Banfi, D. di Bernardo. An atlas of gene expression and gene co-regulation in the human retina. Nucleic Acids Res. 2016
  10. 10. R. De Fez, R. Esposito, C. Angelini, C. Bianco Overproduction of indole-3-acetic acid in free-living rhizobia induces transcriptional changes resembling those occurring in nodule bacteroids. Molecular Plant-Microbe Interactions (2016)
  11. 11. A. Tarallo, C. Angelini, R. Sanges, M. Yagi, C. Agnisola, G. D’ Onofrio. On the genome base composition of teleosts: the effect of environment and lifestyle. BMC Genomics (2016)
  12. 12. F. Russo, D. Righelli, C. Angelini. Advancements in RNASeqGUI towards a Reproducible Analysis of RNA-Seq Experiments. BioMed Research International (2016).
  13. 13. Z. Anvar, M. Cammisa, V. Riso, I. Baglivo, H. Kukreja, A. Sparago, M. Girardot, S. Lad, I. De Feis, F. Cerrato, C. Angelini, R. Feil, P.V. Pedone, G. Grimaldi and A. Riccio, ZFP57 recognizes multiple and closely spaced sequence motif variants to maintain repressive epigenetic marks in mouse embryonic stem cells, Nucleic Acids Research (2015)
  14. 14. M. Scarpato, C. Angelini, E. Cocca, Pallotta M, Morescalchi M.A., Capriglione, T., Short interspersed DNA elements and miRNAs: a novel hidden gene regulation layer in zebrafish , Chomosome Research, (2015)
  15. 15. C. Angelini, R. Heller, R. Volkinshtein and D. Yekutieli Is this the right normalization? A diagnostic tool for ChIP-seq normalization. BMC Bioinformatics (2015).
  16. 16. F. Russo and C. Angelini. RNASeqGUI: A GUI for analyzing RNA-seq data. Bioinformatics, 30 (17): 2514-2516, (2014).
  17. 17. C. Angelini, D. De Canditiis, I. De Feis. Computational approaches for Isoform detection and estimation: Good and bad news. BMC Bioinformatics, 15:135, (2014)
  18. 18. M. Scarpato, R. Esposito, D. Evangelista, M. Aprile, M.R. Ambrosio, C. Angelini, A. Ciccodicola, and V. Costa. AnaLysis of Expression on human chromosome 21, ALE-HSA21: a pilot integrated web resource. Database, The Journal of Biological Databases and Curation. article id. bau009 . (2014).
  19. 19. S. Comes, M. Gagliardi, Nicola Laprano, Annalisa Fico, Amelia Cimmino, Andrea Palamidessi, D. De Cesare, S. De Falco, C. Angelini, G. Scita, E. J. Patriarca, M.R. Matarazzo, and G. Minchiotti. L-Proline Induces a Mesenchymal-like Invasive Program in Embryonic Stem Cells by Remodeling H3K9 and H3K36 Methylation. Stem Cell Reports, vol 1 issue 4, 307-321, (2013).
  20. 20. P. Liò, C. Angelini , I. De Feis and V.A. Nguyen. Statistical Approaches to Use a Model Organism for Regulatory Sequences Annotation of Newly Sequenced Species, PLoS ONE (2012).
  21. 21. L. Bernà, A. Chaurasia, C. Angelini, C. Federico, S. Saccone and G. D'Onofrio. The footprint of metabolism in the organization of mammalian genomes. BMC Genomics, 13:174, (2012).
  22. 22. C. Angelini, D. De Canditiis, M. Pensky. Clustering Time-Course Microarray Data Using Functional Bayesian Infinite Mixture Model. Journal of Applied Statistics, 39, 129-149, (2012).
  23. 23. L.Murino, C.Angelini, I.De Feis, G.Raiconi, R.Tagliaferri. Beyond classical consensus clustering: the Least Squares approach to multiple solutions. Pattern Recognition Letters 32,1604–1612, (2011).
  24. 24. V. Costa, C. Angelini, L. D'Apice, M. Mutarelli, A. Casamassimi, M. Rienzo, L. Sommese, M.A. Gallo, M. Aprile, R. Esposito, L. Leone, A. Donizetti, S. Crispi, B. Sarubbi, R. Calabrò, M. Picardi, P. Salvatore, P. De Berardinis, C. Napoli, A. Ciccodicola. Massive-scale RNA-Seq analysis of non ribosomal transcriptome in human trisomy 21, PLoS ONE (2011).
  25. 25. M. Pava-Ripoll, C. Angelini, W. Fang, S. Wang, F. Posada and R. Leger, The rhizosphere competent entomopathogen Metarhizium anisopliae expresses a specific subset of genes in plant root exudate, Microbiology, vol. 157, pp. 47-55, (2011).
  26. 26. V. Costa, L. Sommese, A. Casamassimi, R. Colicchio, C. Angelini, V. Marchesano, L. Milone, B. Farzati, A. Giovane, C. Fiorito, M. Rienzo, M. Picardi, B. Avallone, M. M. Corsi, B. Sarubbi, R. Calabrò, P. Salvatore, A. Ciccodicola and C. Napoli. Impairment of circulating endothelial progenitors in Down syndrome. BMC Medical Genomics, 3:40, (2010).
  27. 27. V. Costa, C. Angelini, I. De Feis, A. Ciccodicola. Uncovering the complexity of transcriptomes with RNA-Seq. Journal of Biomedicine and Biotechnology vol. 2010, Article ID 853916, (2010).
  28. 28. C. Angelini, D. De Canditiis, M. Pensky. Bayesian models for the two-sample time-course microarray experiments. Computational Statistics & Data Analysis, 53, pp. 1547-1565, (2009).
  29. 29. C. Angelini, L. Cutillo, D. De Canditiis, M. Mutarelli, M. Pensky. BATS: A Bayesian user friendly Software for analyzing time series microarray experiments. BMC Bioinformatics, vol 9, art 415, (2008).
  30. 30. M. Mutarelli, L. Cicatiello, M. Ravo, O.M.V. Grober, A. Facchiano, C. Angelini, A. Weisz. Time-course whole-genome microarray analysis of estrogen effects on hormone-responsive breast cancer cells. BMC Bioinformatics, vol 9, suppl 2, art 12,(2008).
  31. 31. C. Angelini, D. De Canditiis, M. Mutarelli, M. Pensky. A Bayesian Approach to Estimation and Testing in Time-course Microarray Experiments, Statistical Applications in Genetics and Molecular Biology: vol 6 : Iss. 1, Article 24, (2007).
  32. 32. I. Cascino, F. Paladini, F. Belfiore, A., Cauli, C. Angelini, M.T. Fiorillo, A. Matieu, R. Sorrentino. Identification of previously unrecognized predisposing factors for ankylosing spondylitis from analysis of HLA-B27 extended haplotypes in Sardinia. Arthritis & Rheumatism, 56, pp. 2640-2651, (2007).
  33. 33. F. Abramovich, C. Angelini, D. De Canditiis. Pointwise optimality of Bayesian wavelet estimators. Ann. Inst. Statist. Math, 59, pp. 425-434,(2007).
  34. 34. F. Abramovich, C. Angelini. Testing in Mixed-effects FANOVA Model. Journal of Statistical Planning and Inference, 36, pp. 4326-4348, (2006).
  35. 35.C. Angelini, D. Cava, G. Katul, B. Vidakovic, Resampling hierarchical processes in the wavelet domain: A case study using atmospheric turbulence, Physica D, 27, pp. 24-40, (2005).
  36. 36. F. Abramovich, U. Amato, C. Angelini, On optimality of Bayesian wavelet estimators. Scandinavian Journal of Statistics, 31, pp. 217--234, (2004).
  37. 37. C. Angelini, T. Sapatinas, Empirical Bayes Approach to wavelet Regression using ε-contaminated priors. Journal of Statistical Computation and Simulation, 74, 741-764, (2004).
  38. 38. C. Angelini, and B. Vidakovic. Γ-Minimax Wavelet Shrinkage: A Robust Incorporation of Information about Energy of a Signal in Denoising Applications, Statistica Sinica, 14 pp.103-125, (2004).
  39. 39. C. Angelini, D. De Canditiis and F. Leblanc. Wavelet regression estimation in nonparametric mixed effect models. Journal of Multivariate Analysis, 85, pp. 267-291, (2003).
  40. 40. G. Katul, C. Angelini, D. De Canditiis, B. Vidakovic, T.D. Albertson and U. Amato. Are the effect of large scale flow conditions really lost through the turbulent cascade?, Geophysical Research Letter, 30, pp. 1164-1168, (2003).
  41. 41. C. Angelini, D. De Canditiis. Pointwise convergence of the wavelet regularization estimator. Comm. Statist.: Theory and Method, 31, pp.1561-1578, (2002).
  42. 42. C. Angelini, D. De Canditiis. Fourier frequency adaptive regularization for smoothing data, J. Comp. App. Math. 115, pp.35-50, (2000).
  43. 43. U. Amato, C. Angelini, C. Serio. Compression of AVHRR Images by Wavelet Packets. Environmental Model. Software, 15, pp. 127-138, (2000).
  44. 44. U. Amato, C. Angelini, C. Serio. Wavelet compression of AVHRR imagery, FRACTALS, 5, pp. 11-22, (1997).

Book-chapters, Proceedings, and other international Journals (with referee)

  1. 1. F. Russo, D. Righelli, C. Angelini, Advantages and Limits in the Adoption of Reproducible Research and R-Tools for the Analysis of Omic Data. Lecture notes in Bioinformatics, 9874, pp 245-258, (2016).
  2. 2. A. Iuliano, A. Occhipinti, C. Angelini, I. De Feis, P.Liò. Applications of network-based survival analysis methods for pathway detection in cancer. in Lecture Notes in Bioinformatics, 8623, 76-88, (2015)
  3. 3. C. Angelini and V. Costa. Understanding gene regulatory mechanisms by integrating ChIP-seq and RNA-seq data: statistical solutions to biological problems , Front. Cell Dev. Biol.,(2014)
  4. 4. F. Gagliardi, C. Angelini. Discovering Typical Transcription-Factors Patterns in Gene Expression Levels of Mouse Embryonic Stem Cells by Instance-Based Classifiers, ICIAP 2013 Workshops, Lecture Notes in Computer Science 8158, pp. 381–388, (2013).
  5. 5. C. Angelini, D. De Canditiis, M. Pensky, N. Brownstein. Bayesian models for the analysis of multi sample time-course microarray experiments. Lecture Notes in Bioinformatics 7548, pp. 21-35, (2012).
  6. 6. C. Angelini, D. De Canditiis, M. Pensky, Bayesian methods for Time-course microarray analysis: from genes detection to clustering. In Studies in Theoretical and Applied Statistics, Springer, pp. 47-56, (2012).
  7. 7. V. Costa, C. Angelini, L. D'Apice, M. Mutarelli, A. Casamassimi, M. Rienzo, L. Sommese, M.A. Gallo, M. Aprile, R. Esposito, L. Leone, A. Donizetti, S. Crispi, B. Sarubbi, R. Calabrò, M. Picardi, P. Salvatore, P. De Berardinis, C. Napoli, A. Ciccodicola. RNA-seq: from computational challenges to biological insights, in Network tools and applications in Bioinformatics, proceeding of Nettab 2010, ARACNE, Network tools and Applications in Biology, pp. 77-83, (2010).
  8. 8. C. Angelini, I. De Feis, R. van der Wath, V.-A. Nguyen, P. Liò. Combining Replicates and Nearby Species Data: A Bayesian approach., in Lecture Notes in Bioinformatics, 6160, pp. 191-205, (2010)
  9. 9. L. Murino, C. Angelini, I. Bifulco, I. De Feis, G. Raiconi, R. Tagliaferri, Multiple Clustering Solutions Analysis Through Lest-Square Consensus Algorithms, in Lecture Notes in Bioinformatics, 6160, 215-227, (2010)
  10. 10. C. Angelini, D. De Canditiis, M. Pensky. Estimation and Testing in Time-course Microarray Experiments. In Bayesian Modeling in Bioinformatics, Chapman & Hall/CRC Biostatistics Series. (2010).
  11. 11. C. Angelini, D. De Canditiis, M. Pensky. Bayesian models for time‐course microarray analysis: from genes' detection to clustering. In Statistical methods for the analysis of large data-sets. Book of short paper of the Italian Statistical Society, Invited paper by the Royal Statistical Society, pp. 19-23, (2009).
  12. 12. C. Angelini, L. Cutillo, I. De Feis, R. van der Wath, P. Liò. Combining experimental evidences from replicates and nearby species data for annotating novel genomes. In AIP Proceedings, Vol. 1028, (2008).
  13. 13. C. Angelini, L. Cutillo, I. De Feis, R. van der Wath, P. Liò. Identify regulatory sites using neighborhood species, in Lecture Notes in Computer Science, 4447, pp 1-10. (2007).
  14. 14. F. Abramovich, C. Angelini. Bayesian MAP multiple testing procedures. Sankhya 68, pp. 436--460, (2006).
  15. 15. C. Angelini, B. Vidakovic. Some Novel Methods in Wavelet Data Analysis: Wavelet Anova, F-test Shrinkage, and Γ-Minimax Wavelet Shrinkage. In `Wavelets and Their Applications' Krisna, Radha and Thangavelu Eds, Allied Publishers, pp. 31-45, (2003).
  16. 16. U. Amato, C. Angelini, G. Masiello, C. Serio. Wavelet Based Fast Forward Model for the 14 CO_2 Absorption Band, Proceedings ASSFTS9 2000, 70-73, (2000).
  17. 17. U. Amato, C. Angelini, G. Masiello, C. Serio. Fast Wavelet Radiative Transfer Model for Inversion of IASI Radiance. Technical Proceedings IEEE 2000 International Geoscience and Remote Sensing Symposium, 2797-2799, (2000).
  18. 18. U. Amato, C. Angelini. TOWER - Telescopic Optimal Wavelet Estimator of the Risk. Int. J. Appl. Sci. and Comp., 7, pp. 125-141, (2000).


  1. C. Angelini. Wavelets for Nonparametric Regression and Image Compression. Tesi di Dottorato, Università degli Studi di Napoli "Federico II". (November 2001).
  2. C. Angelini. Applicazioni delle Wavelets. Tesi di Laurea; Università degli Studi di Napoli "Federico II". (December 1994).

Other Publications

  1. C. Angelini, D. Righelli, F Russo Reproducible Research in the era of Next Generation Sequencing: current approaches, examples and future perspectives, EMBnet.journal 21 (A) (2015).
  2. C. Angelini, F Russo Analyzing RNA-Seq data with RNASeqGUI, EMBnet.journal 20 (A), e783. (2014)
  3. C. Angelini, Metodi di analisi statistica di dati di Next generation sequencing. pp 55-59 in XI Corso di formazione avanzata in Medicina genomica e terapia personalizzata in ematologia/oncologia a cura di Carlo Bernasconi, Collegio Ghislieri, centro per la Comunicazione e la ricerca. Edizioni EDIMES volume 11, (2012).
  4. C. Angelini, Wavelet methods for the analysis of Mass Spectrometry data, Newsletter RNTBIO, December 2011, Special Issue B4P2011 Bioinformatics for Proteomics.
  5. C. Angelini and I. De Feis. DNA-puzzles: un gioco per matematici. Maddmaths: Matematica applicata e Didattica. Focus, Luglio 2010.
  6. C. Angelini, A. Ciccodicola, V. Costa and I. De Feis. Analyzing the Whole Transcriptome by RNA-Seq data: the Tip of the Iceberg, ERCIM NEWS July 2010, Special Theme Computational Biology, pp.16-17. 2010.
  7. C. Angelini, M. Vannucci. Bayesian methods for wavelet-based modeling. Annotated references for ISBA Bulletin, 12, June 2005.