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Items: 31

1.

Multi-omics integration for neuroblastoma clinical endpoint prediction.

Francescatto M, Chierici M, Rezvan Dezfooli S, Zandonà A, Jurman G, Furlanello C.

Biol Direct. 2018 Apr 3;13(1):5. doi: 10.1186/s13062-018-0207-8.

2.

Phylogenetic convolutional neural networks in metagenomics.

Fioravanti D, Giarratano Y, Maggio V, Agostinelli C, Chierici M, Jurman G, Furlanello C.

BMC Bioinformatics. 2018 Mar 8;19(Suppl 2):49. doi: 10.1186/s12859-018-2033-5.

3.

PD-L1 Is a Therapeutic Target of the Bromodomain Inhibitor JQ1 and, Combined with HLA Class I, a Promising Prognostic Biomarker in Neuroblastoma.

Melaiu O, Mina M, Chierici M, Boldrini R, Jurman G, Romania P, D'Alicandro V, Benedetti MC, Castellano A, Liu T, Furlanello C, Locatelli F, Fruci D.

Clin Cancer Res. 2017 Aug 1;23(15):4462-4472. doi: 10.1158/1078-0432.CCR-16-2601. Epub 2017 Mar 7.

PMID:
28270499
4.

Efficient randomization of biological networks while preserving functional characterization of individual nodes.

Iorio F, Bernardo-Faura M, Gobbi A, Cokelaer T, Jurman G, Saez-Rodriguez J.

BMC Bioinformatics. 2016 Dec 20;17(1):542. doi: 10.1186/s12859-016-1402-1.

5.

Metric projection for dynamic multiplex networks.

Jurman G.

Heliyon. 2016 Aug 4;2(8):e00136. doi: 10.1016/j.heliyon.2016.e00136. eCollection 2016 Aug.

6.

DTW-MIC Coexpression Networks from Time-Course Data.

Riccadonna S, Jurman G, Visintainer R, Filosi M, Furlanello C.

PLoS One. 2016 Mar 31;11(3):e0152648. doi: 10.1371/journal.pone.0152648. eCollection 2016.

7.

Promoter-level expression clustering identifies time development of transcriptional regulatory cascades initiated by ErbB receptors in breast cancer cells.

Mina M, Magi S, Jurman G, Itoh M, Kawaji H, Lassmann T, Arner E, Forrest AR, Carninci P, Hayashizaki Y, Daub CO; FANTOM Consortium, Okada-Hatakeyama M, Furlanello C.

Sci Rep. 2015 Jul 16;5:11999. doi: 10.1038/srep11999.

8.

A null model for Pearson coexpression networks.

Gobbi A, Jurman G.

PLoS One. 2015 Jun 1;10(6):e0128115. doi: 10.1371/journal.pone.0128115. eCollection 2015.

9.

Fast randomization of large genomic datasets while preserving alteration counts.

Gobbi A, Iorio F, Dawson KJ, Wedge DC, Tamborero D, Alexandrov LB, Lopez-Bigas N, Garnett MJ, Jurman G, Saez-Rodriguez J.

Bioinformatics. 2014 Sep 1;30(17):i617-23. doi: 10.1093/bioinformatics/btu474.

10.

The concordance between RNA-seq and microarray data depends on chemical treatment and transcript abundance.

Wang C, Gong B, Bushel PR, Thierry-Mieg J, Thierry-Mieg D, Xu J, Fang H, Hong H, Shen J, Su Z, Meehan J, Li X, Yang L, Li H, Łabaj PP, Kreil DP, Megherbi D, Gaj S, Caiment F, van Delft J, Kleinjans J, Scherer A, Devanarayan V, Wang J, Yang Y, Qian HR, Lancashire LJ, Bessarabova M, Nikolsky Y, Furlanello C, Chierici M, Albanese D, Jurman G, Riccadonna S, Filosi M, Visintainer R, Zhang KK, Li J, Hsieh JH, Svoboda DL, Fuscoe JC, Deng Y, Shi L, Paules RS, Auerbach SS, Tong W.

Nat Biotechnol. 2014 Sep;32(9):926-32. doi: 10.1038/nbt.3001. Epub 2014 Aug 24.

11.

A promoter-level mammalian expression atlas.

FANTOM Consortium and the RIKEN PMI and CLST (DGT), Forrest AR, Kawaji H, Rehli M, Baillie JK, de Hoon MJ, Haberle V, Lassmann T, Kulakovskiy IV, Lizio M, Itoh M, Andersson R, Mungall CJ, Meehan TF, Schmeier S, Bertin N, Jørgensen M, Dimont E, Arner E, Schmidl C, Schaefer U, Medvedeva YA, Plessy C, Vitezic M, Severin J, Semple C, Ishizu Y, Young RS, Francescatto M, Alam I, Albanese D, Altschuler GM, Arakawa T, Archer JA, Arner P, Babina M, Rennie S, Balwierz PJ, Beckhouse AG, Pradhan-Bhatt S, Blake JA, Blumenthal A, Bodega B, Bonetti A, Briggs J, Brombacher F, Burroughs AM, Califano A, Cannistraci CV, Carbajo D, Chen Y, Chierici M, Ciani Y, Clevers HC, Dalla E, Davis CA, Detmar M, Diehl AD, Dohi T, Drabløs F, Edge AS, Edinger M, Ekwall K, Endoh M, Enomoto H, Fagiolini M, Fairbairn L, Fang H, Farach-Carson MC, Faulkner GJ, Favorov AV, Fisher ME, Frith MC, Fujita R, Fukuda S, Furlanello C, Furino M, Furusawa J, Geijtenbeek TB, Gibson AP, Gingeras T, Goldowitz D, Gough J, Guhl S, Guler R, Gustincich S, Ha TJ, Hamaguchi M, Hara M, Harbers M, Harshbarger J, Hasegawa A, Hasegawa Y, Hashimoto T, Herlyn M, Hitchens KJ, Ho Sui SJ, Hofmann OM, Hoof I, Hori F, Huminiecki L, Iida K, Ikawa T, Jankovic BR, Jia H, Joshi A, Jurman G, Kaczkowski B, Kai C, Kaida K, Kaiho A, Kajiyama K, Kanamori-Katayama M, Kasianov AS, Kasukawa T, Katayama S, Kato S, Kawaguchi S, Kawamoto H, Kawamura YI, Kawashima T, Kempfle JS, Kenna TJ, Kere J, Khachigian LM, Kitamura T, Klinken SP, Knox AJ, Kojima M, Kojima S, Kondo N, Koseki H, Koyasu S, Krampitz S, Kubosaki A, Kwon AT, Laros JF, Lee W, Lennartsson A, Li K, Lilje B, Lipovich L, Mackay-Sim A, Manabe R, Mar JC, Marchand B, Mathelier A, Mejhert N, Meynert A, Mizuno Y, de Lima Morais DA, Morikawa H, Morimoto M, Moro K, Motakis E, Motohashi H, Mummery CL, Murata M, Nagao-Sato S, Nakachi Y, Nakahara F, Nakamura T, Nakamura Y, Nakazato K, van Nimwegen E, Ninomiya N, Nishiyori H, Noma S, Noma S, Noazaki T, Ogishima S, Ohkura N, Ohimiya H, Ohno H, Ohshima M, Okada-Hatakeyama M, Okazaki Y, Orlando V, Ovchinnikov DA, Pain A, Passier R, Patrikakis M, Persson H, Piazza S, Prendergast JG, Rackham OJ, Ramilowski JA, Rashid M, Ravasi T, Rizzu P, Roncador M, Roy S, Rye MB, Saijyo E, Sajantila A, Saka A, Sakaguchi S, Sakai M, Sato H, Savvi S, Saxena A, Schneider C, Schultes EA, Schulze-Tanzil GG, Schwegmann A, Sengstag T, Sheng G, Shimoji H, Shimoni Y, Shin JW, Simon C, Sugiyama D, Sugiyama T, Suzuki M, Suzuki N, Swoboda RK, 't Hoen PA, Tagami M, Takahashi N, Takai J, Tanaka H, Tatsukawa H, Tatum Z, Thompson M, Toyodo H, Toyoda T, Valen E, van de Wetering M, van den Berg LM, Verado R, Vijayan D, Vorontsov IE, Wasserman WW, Watanabe S, Wells CA, Winteringham LN, Wolvetang E, Wood EJ, Yamaguchi Y, Yamamoto M, Yoneda M, Yonekura Y, Yoshida S, Zabierowski SE, Zhang PG, Zhao X, Zucchelli S, Summers KM, Suzuki H, Daub CO, Kawai J, Heutink P, Hide W, Freeman TC, Lenhard B, Bajic VB, Taylor MS, Makeev VJ, Sandelin A, Hume DA, Carninci P, Hayashizaki Y.

Nature. 2014 Mar 27;507(7493):462-70. doi: 10.1038/nature13182.

12.

Stability indicators in network reconstruction.

Filosi M, Visintainer R, Riccadonna S, Jurman G, Furlanello C.

PLoS One. 2014 Feb 27;9(2):e89815. doi: 10.1371/journal.pone.0089815. eCollection 2014.

13.

A combinatorial model of malware diffusion via bluetooth connections.

Merler S, Jurman G.

PLoS One. 2013;8(3):e59468. doi: 10.1371/journal.pone.0059468. Epub 2013 Mar 21.

14.

Minerva and minepy: a C engine for the MINE suite and its R, Python and MATLAB wrappers.

Albanese D, Filosi M, Visintainer R, Riccadonna S, Jurman G, Furlanello C.

Bioinformatics. 2013 Feb 1;29(3):407-8. doi: 10.1093/bioinformatics/bts707. Epub 2012 Dec 14.

PMID:
23242262
15.

Clinical value of prognosis gene expression signatures in colorectal cancer: a systematic review.

Sanz-Pamplona R, Berenguer A, Cordero D, Riccadonna S, Solé X, Crous-Bou M, Guinó E, Sanjuan X, Biondo S, Soriano A, Jurman G, Capella G, Furlanello C, Moreno V.

PLoS One. 2012;7(11):e48877. doi: 10.1371/journal.pone.0048877. Epub 2012 Nov 7. Review.

16.

A comparison of MCC and CEN error measures in multi-class prediction.

Jurman G, Riccadonna S, Furlanello C.

PLoS One. 2012;7(8):e41882. doi: 10.1371/journal.pone.0041882. Epub 2012 Aug 8.

17.

Algebraic comparison of partial lists in bioinformatics.

Jurman G, Riccadonna S, Visintainer R, Furlanello C.

PLoS One. 2012;7(5):e36540. doi: 10.1371/journal.pone.0036540. Epub 2012 May 17.

18.

Effect of size and heterogeneity of samples on biomarker discovery: synthetic and real data assessment.

Di Camillo B, Sanavia T, Martini M, Jurman G, Sambo F, Barla A, Squillario M, Furlanello C, Toffolo G, Cobelli C.

PLoS One. 2012;7(3):e32200. doi: 10.1371/journal.pone.0032200. Epub 2012 Mar 5.

19.

RegnANN: Reverse Engineering Gene Networks using Artificial Neural Networks.

Grimaldi M, Visintainer R, Jurman G.

PLoS One. 2011;6(12):e28646. doi: 10.1371/journal.pone.0028646. Epub 2011 Dec 28.

20.

A machine learning pipeline for quantitative phenotype prediction from genotype data.

Guzzetta G, Jurman G, Furlanello C.

BMC Bioinformatics. 2010 Oct 26;11 Suppl 8:S3. doi: 10.1186/1471-2105-11-S8-S3.

21.

The MicroArray Quality Control (MAQC)-II study of common practices for the development and validation of microarray-based predictive models.

Shi L, Campbell G, Jones WD, Campagne F, Wen Z, Walker SJ, Su Z, Chu TM, Goodsaid FM, Pusztai L, Shaughnessy JD Jr, Oberthuer A, Thomas RS, Paules RS, Fielden M, Barlogie B, Chen W, Du P, Fischer M, Furlanello C, Gallas BD, Ge X, Megherbi DB, Symmans WF, Wang MD, Zhang J, Bitter H, Brors B, Bushel PR, Bylesjo M, Chen M, Cheng J, Cheng J, Chou J, Davison TS, Delorenzi M, Deng Y, Devanarayan V, Dix DJ, Dopazo J, Dorff KC, Elloumi F, Fan J, Fan S, Fan X, Fang H, Gonzaludo N, Hess KR, Hong H, Huan J, Irizarry RA, Judson R, Juraeva D, Lababidi S, Lambert CG, Li L, Li Y, Li Z, Lin SM, Liu G, Lobenhofer EK, Luo J, Luo W, McCall MN, Nikolsky Y, Pennello GA, Perkins RG, Philip R, Popovici V, Price ND, Qian F, Scherer A, Shi T, Shi W, Sung J, Thierry-Mieg D, Thierry-Mieg J, Thodima V, Trygg J, Vishnuvajjala L, Wang SJ, Wu J, Wu Y, Xie Q, Yousef WA, Zhang L, Zhang X, Zhong S, Zhou Y, Zhu S, Arasappan D, Bao W, Lucas AB, Berthold F, Brennan RJ, Buness A, Catalano JG, Chang C, Chen R, Cheng Y, Cui J, Czika W, Demichelis F, Deng X, Dosymbekov D, Eils R, Feng Y, Fostel J, Fulmer-Smentek S, Fuscoe JC, Gatto L, Ge W, Goldstein DR, Guo L, Halbert DN, Han J, Harris SC, Hatzis C, Herman D, Huang J, Jensen RV, Jiang R, Johnson CD, Jurman G, Kahlert Y, Khuder SA, Kohl M, Li J, Li L, Li M, Li QZ, Li S, Li Z, Liu J, Liu Y, Liu Z, Meng L, Madera M, Martinez-Murillo F, Medina I, Meehan J, Miclaus K, Moffitt RA, Montaner D, Mukherjee P, Mulligan GJ, Neville P, Nikolskaya T, Ning B, Page GP, Parker J, Parry RM, Peng X, Peterson RL, Phan JH, Quanz B, Ren Y, Riccadonna S, Roter AH, Samuelson FW, Schumacher MM, Shambaugh JD, Shi Q, Shippy R, Si S, Smalter A, Sotiriou C, Soukup M, Staedtler F, Steiner G, Stokes TH, Sun Q, Tan PY, Tang R, Tezak Z, Thorn B, Tsyganova M, Turpaz Y, Vega SC, Visintainer R, von Frese J, Wang C, Wang E, Wang J, Wang W, Westermann F, Willey JC, Woods M, Wu S, Xiao N, Xu J, Xu L, Yang L, Zeng X, Zhang J, Zhang L, Zhang M, Zhao C, Puri RK, Scherf U, Tong W, Wolfinger RD; MAQC Consortium.

Nat Biotechnol. 2010 Aug;28(8):827-38. doi: 10.1038/nbt.1665. Epub 2010 Jul 30.

22.

Functional analysis of multiple genomic signatures demonstrates that classification algorithms choose phenotype-related genes.

Shi W, Bessarabova M, Dosymbekov D, Dezso Z, Nikolskaya T, Dudoladova M, Serebryiskaya T, Bugrim A, Guryanov A, Brennan RJ, Shah R, Dopazo J, Chen M, Deng Y, Shi T, Jurman G, Furlanello C, Thomas RS, Corton JC, Tong W, Shi L, Nikolsky Y.

Pharmacogenomics J. 2010 Aug;10(4):310-23. doi: 10.1038/tpj.2010.35.

23.

Repeatability of published microarray gene expression analyses.

Ioannidis JP, Allison DB, Ball CA, Coulibaly I, Cui X, Culhane AC, Falchi M, Furlanello C, Game L, Jurman G, Mangion J, Mehta T, Nitzberg M, Page GP, Petretto E, van Noort V.

Nat Genet. 2009 Feb;41(2):149-55. doi: 10.1038/ng.295. Epub 2008 Jan 28.

PMID:
19174838
24.

Machine learning methods for predictive proteomics.

Barla A, Jurman G, Riccadonna S, Merler S, Chierici M, Furlanello C.

Brief Bioinform. 2008 Mar;9(2):119-28. doi: 10.1093/bib/bbn008. Epub 2008 Feb 29. Review.

PMID:
18310105
25.

Algebraic stability indicators for ranked lists in molecular profiling.

Jurman G, Merler S, Barla A, Paoli S, Galea A, Furlanello C.

Bioinformatics. 2008 Jan 15;24(2):258-64. Epub 2007 Nov 16.

PMID:
18024475
26.

A grid environment for high-throughput proteomics.

Cannataro M, Barla A, Flor R, Jurman G, Merler S, Paoli S, Tradigo G, Veltri P, Furlanello C.

IEEE Trans Nanobioscience. 2007 Jun;6(2):117-23.

PMID:
17695745
27.

Semisupervised learning for molecular profiling.

Furlanello C, Serafini M, Merler S, Jurman G.

IEEE/ACM Trans Comput Biol Bioinform. 2005 Apr-Jun;2(2):110-8.

PMID:
17044176
28.

Terminated Ramp-Support vector machines: a nonparametric data dependent kernel.

Merler S, Jurman G.

Neural Netw. 2006 Dec;19(10):1597-611. Epub 2006 Apr 17.

PMID:
16603338
29.

Gene expression profiling identifies potential relevant genes in alveolar rhabdomyosarcoma pathogenesis and discriminates PAX3-FKHR positive and negative tumors.

De Pittà C, Tombolan L, Albiero G, Sartori F, Romualdi C, Jurman G, Carli M, Furlanello C, Lanfranchi G, Rosolen A.

Int J Cancer. 2006 Jun 1;118(11):2772-81.

30.

Entropy-based gene ranking without selection bias for the predictive classification of microarray data.

Furlanello C, Serafini M, Merler S, Jurman G.

BMC Bioinformatics. 2003 Nov 6;4:54.

31.

An accelerated procedure for recursive feature ranking on microarray data.

Furlanello C, Serafini M, Merler S, Jurman G.

Neural Netw. 2003 Jun-Jul;16(5-6):641-8.

PMID:
12850018

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