Group of Systems Biology and Systems Neuroscience
Institute of Mathematics and Statistics
University of São Paulo

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Book chapters

  1. Patriota AG, Vidal MC, de Jesus DAC, Fujita A. ANOCVA: a non-parametric statistical test to compare clustering structures. In: da Silva FAB, Carels N, Silva Junior FP (Eds.). Theoretical and Applied Aspects of Systems Biology. Springer. (in press).
  2. Ribeiro AH, Soler JMP, Chaibub Neto E, Fujita A. Causal inference and structure learning of genotype-phenotype networks using genetic variation. In: Ka-Chun Wong (Ed.). Big Data Analytics in Genomics. Springer. 89-143, 2016.
  3. de Siqueira Santos S, Takahashi DY, Sato JR, Ferreira CE, Fujita A. Statistical methods in graphs: parameter estimation, model selection, and test. In: Yongtang Shi, Sueliang Li, Matthias Dehmer (Eds.). Mathematical foundations and applications of graph entropy. Wiley-Blackwell. 6: 183-202, 2016.
  4. Fujita A, Severino P, Alexandrino PMR, Oliveira FCA, Miyano S. Granger causality for time series gene expression data. In: Ka-Chun Wong (Ed.). Computational Biology and Bioinformatics: Gene Regulation. CRC Press - Taylor & Francis Group. 1: 48-65, 2016.
  5. Fujita A, Miyano S. A tutorial to identify nonlinear associations in gene expression time series data. In: Etsuko Miyamono-Sato; Hiroyuki Ohashi; Hirotaka Sasaki; Jun-Ichi Nishikawa; Hiroshi Yanagawa (Eds.). Transcription Factor Regulatory Networks - Methods and Protocols.Springer, 2014.
  6. Demasi MAA, Carreira ACO, Gomes LR, Lima MT, Lobba ARM, Lojudice FH, Degaki TL, Montor WR, Fujita A, Sogayar MC. Genomica funcional em oncologia. In: Paulo Hoff; Artur Katz; Roger Chammas; Vincente Odoni; Yana Novis (Eds.). Tratado de Oncologia. 1st edition. Rio de Janeiro: Atheneu, p. 505-521, 2013.
  7. Fujita A, Sato JR, Demasi MAA, Miyano S, Sogayar MC, Ferreira CE. An introduction to time-varying connectivity estimation for gene regulatory networks. In: Frank Emmert-Streib; Matthias Dehmer. (Eds.). Medical Biostatistics for complex diseases. Weinheim, Germany: Wiley VCH Verlag, p.205-230, 2010.

Article in magazine

  1. Fujita A. Todos pela inovação. Computação Brasil, p. 17-17. (article in Portuguese).


Invited talks (selected)

  1. The Conference of the International Federation of Classification Societies - Japan, August 08-10, 2017 (http://ifcs.boku.ac.at/_conference/index.php/ifcs2017/ifcs2017/schedConf/overview).
  2. The 52th in silico Megabank research seminar, Tohoku Medical Megabank Organization, Tohoku University - Japan, January 07, 2015 (http://nagasakilab.csml.org/en/archives/1172).
  3. FAPESP-Royal Academy of Engineering Brazil-UK Frontiers of Engineering, Jarinú, Brazil, November 6 – 8, 2014 (http://www.fapesp.br/8927).
  4. Trans-Atlantic Partnership Workshop on Synthetic Biology – São Paulo, Brazil, April 9 – 10, 2014 (https://sites.google.com/site/wswarwickusp/home).
  5. International Symposium on Tumor Biology in Kanazawa & Symposium on Drug Discovery in Academics - Kanazawa, Japan, January 23 - 24, 2014 (http://www.kanazawa-u.ac.jp/~ganken/kanazawakokusai2014/indexE.html).
  6. X-Meeting/BSB (International Conference of the AB3C & Brazilian Symposium on Bioinformatics) - Recife, Brazil, November 03 - 06, 2013 (http://x-meeting.com).
  7. UEA/NRP – Brazil Workshop on Plant Sciences – Norwich, England, January 23 – 26, 2013 (http://www.fapesp.br/7226).
  8. SABI (Sociedad Argentina de Bioingeniería) - XVIII Congreso Argentino de Bioingeniería - Mar del Plata, Argentina, September 28 - 30, 2011 (http://www.sabi2011.fi.mdp.edu.ar/).
  9. ISI (International Statistical Institute) - satellite meeting, Dynamic Statistical Models - Copenhagen, Denmark, August 17 - 19, 2011 (http://statistics.ku.dk/isi-satellite/).
  10. Over 20 seminars at national universities (Federal University of Rio de Janeiro, Federal University of Minas Gerais, Federal University of Paraná, etc) and workshops (First Brazilian Workshop on Bioinformatics/Chemometrics for Metabolomics, Workshop in Bioinformatics and Algorithms, Argentina-Brazil School of Bioinformatics in the study of vaccines, etc).


Papers in journals

  1. Bando SY, Iamashita P, Guth BE, Santos LF, Fujita A, Abe CM, Ferreira LR, Moreira-Filho CA. A hemolytic-uremic syndrome-associated strain O113:H21 Shiga toxin-producing Escherichia coli specifically expresses a transcriptional module containing dicA and is related to gene network dysregulation in Caco-2 cells. PLoS ONE. 12: e0189613, 2017
  2. Esteves E, Maruyama SR, Sakuma R, Fujita A, Martins LA, Righi AA, Costa FB, Palmisano G, Labruna MB, Sa-Nunes A, Ribeiro JM, Fogaca AC. Analysis of the salivary gland transcriptome of unfed and partially fed amblyomma sculptum ticks and descriptive proteome of the saliva. Frontiers in Cellular and Infection Microbiology. 7: 476, 2017
  3. Ribeiro AH, Lotufo PA, Fujita A, Goulart AC, Chor D, Mill JG, Bensenor IM, Santos IS. Association between short-term systolic blood pressure variability and carotid intima-media thickness in ELSA-Brasil baseline. American Journal of Hypertension. 30: 954-960, 2017.
  4. Martins LA, Galletti MFBM, Ribeiro JM, Fujita A, Costa FB, Bruna MB, Daffre S, Fogaca AC. The distinct transcriptional response of the midgut of amblyomma sculptum and Amblyomma aureolatum ticks to Rickettsia rickettsii correlates to their differences in susceptibility to infection. Frontiers in Cellular and Infection Microbiology. 7: 129, 2017.
  5. Vidal MC, Sato JR, Balardin JB, Takahashi DY, Fujita A. ANOCVA in R: a software to compare clusters between groups and its application to the study of autism spectrum disorder. Frontiers in Neuroscience. 11: 1, 2017.
  6. Fujita A, Vidal MC, Takahashi DY. A statistical method to distinguish functional brain networks. Frontiers in Neuroscience. 11: 1, 2017.
  7. Fujita A, Takahashi DY, Balardin JB, Vidal MC, Sato JR. Correlation between graphs with an application to brain network analysis. Computational Statistics & Data Analysis. 109: 76-92, 2016.
  8. Galletti MFBM,  Rosa RD,  Fujita A,  Martins LA,  Soares HS,  Labruna MB,  Daffre S,  Fogaca AC. Virulence genes of Rickettsia rickettsii are differentially modulated by either temperature upshift or blood feeding in tick midgut and salivary glands. Parasites & Vectors. 9: 331, 2016. 
  9. Kinker GS,  Thomas AM,  Carvalho VJ,  Lima FP,  Fujita A. Deletion and low expression of NFKBIA are associated with poor prognosis in lower-grade glioma patients. Scientific Reports, 6: 24160, 2016.
  10. Sato JR,  Balardin J,  Vidal, MC,  Fujita A. Identification of segregated regions in the functional brain connectome of autistic patients by a combination of fuzzy spectral clustering and entropy analysis. Journal of Psychiatry and Neuroscience.41: 124--132, 2016.
  11. Fonseca M,  Rodrigues AC,  Cezar L,  Fujita A,  Soriano F,  Steiner A. Spontaneous hypothermia in human sepsis is a transient, self-limiting and non-terminal response. Journal of Applied Physiology. 1, 2016.
  12. Sato JR,  Vidal MC,  Santos SS,  Massirer KB,  Fujita A. Complex network measures in Autism Spectrum Disorders. IEEE/ACM Transactions on Computational Biology and Bioinformatics. 2015. 
  13. Gomes LR,  Fujita A,  Mott JD,  Soares FA,  Labriola L,  Sogayar, MC. RECK is not an independent prognostic marker for breast cancer. BMC Cancer. 15: 660, 2015.
  14. Nakata A,  Yoshida R,  Yamaguchi R,  Yamauchi M,  Tamada Y,  Fujita A,  Shimamura T,  Imoto S,  Higuchi T,  Nomura M,  Kimura T,  Nokihara H,  Higashiyama M,  Kondoh K,  Nishihara H,  Tojo A,  Yano S,  Miyano S,  Gotoh N. Elevated Beta-catenin pathway as a novel target for patients with resistance to EGF receptor targeting drugs. Scientific Reports. 5: 13076, 2015.
  15. Santos SS,  Galastro TFA,  Watanabe RA,  Oba-Shinjo SM,  Nagahashi-Marie SK,  Fujita A. CoGA: an R package to identify differentially co-expressed gene sets by analyzing the graph spectra. PLoS ONE. 10: 0135831, 2015.
  16. Alexandrino PMR,  Mendonça TT,  Bautista LPG,  Cherix J,  Lozano-Sakalauska GC, Fujita A,  Ramos-Filho E,  Long P,  Padilla G,  Taciro MK,  Gomez JGC,  Silva LF. Draft genome sequence of the polyhydroxyalkanoate-producing bacterium Burkholderia sacchari LMG 19450 isolated from Brazilian sugarcane plantation soil. Genome Announcements. 3: e00313-15, 2015.
  17. Rodrigues AC,  Machado BS, Florence G, Hamad AP, Sakamoto AC, Fujita A, Baccala LA, Amaro Jr E, Sameshima K. Brain network dynamics characterization in epiletic seizures. The European Physical Journal Special Topics. 223:2933 – 2941, 2014.
  18. Azevedo H, Fujita A, Bando SY, Iamashita P,  Moreira-Filho CA. Transcriptional network analysis reveals that AT1 and AT2 angiotensin II receptors are both involved in the regulation of genes essential for glioma progression. PLoS ONE. 9: e110934, 2014.
  19. Maciel C, Fujita A, Gueroni DI, Ramos AD, Capurro ML,  Sa-Nunes A. Evans blue as a simple and low-cost method to discriminate mosquitoes feeding choice on small laboratory animals. PLoS ONE. 9: e110551, 2014.
  20. Fujita A, Takahashi DY, Patriota AG, Sato JR. A non-parametric statistical test to compare clusters with applications in functional magnetic resonance imaging data. Statistics in Medicine. 33: 4949-4962, 2014.
  21. Molina E,  Fujita A,  Sogayar MC,  Demasi MAA. A quantitative and humane tail bleeding assay for efficacy evaluation of antihaemophilic factors in haemophilia A mice. Haemophilia. 20: e392-e398, 2014.
  22. Fujita A,  Takahashi DY,  Patriota AG. A non-parametric method to estimate the number of clusters. Computational Statistics & Data Analysis. 73: 27-29, 2014.
  23. Santos SS,  Takahashi DY,  Nakata A,  Fujita A. A comparative study of statistical methods used to identify dependencies between gene expression signals. Briefings in Bioinformatics. 15: 906-918, 2013.
  24. Halcsik E,  Forni MF,  Fujita A,  Verano-Braga T,  Jensen ON,  Sogayar MC. New insights in osteogenic differentiation revealed by mass spectrometric assessment of phosphorylated substrates in murine skin mesenchymal cells. BMC Cell Biology. 14: 47, 2013.
  25. Galletti MFBM,  Fujita A,  Nishiyama-Jr MY,  Malossi CD,  Pinter A,  Soares JF,  Daffre S,  Labruna MB,  Fogaca AC. Natural blood feeding and temperature shift modulate the global transcriptional profile of Rickettsia rickettsii infecting its tick vector. PLoS ONE. 8: e77388, 2013.
  26. Sato JR,  Takahashi DY,  Hoexter MQ,  Massirer KB, Fujita A. Measuring network's entropy in ADHD: A new approach to investigate neuropsychiatric disorders. NeuroImage. 77: 44-51, 2013.
  27. Takahashi DY,  Sato JR,  Ferreira CE, Fujita A. Discriminating different classes of biological networks by analyzing the graphs spectra distribution. PLoS ONE. 7: e49949, 2012.
  28. Fujita A, Severino P, Kojima K, Sato JR, Patriota AG, Miyano S. Functional clustering of time series gene expression data by Granger causality. BMC Systems Biology.6: 137, 2012.
  29. Nagasaki M,  Fujita A,  Sekiya Y,  Saito A, Ikeda E, Li C, Miyano S. XiP: a computational environment to create, extend, and share workflows. Bioinformatics. 29: 137-139, 2012.
  30. Sato JR, Hoexter MQ, Fujita A, Rohde LA. Evaluation of pattern recognition and feature extraction methods in ADHD prediction. Frontiers in Systems Neuroscience. 6: 68, 2012.
  31. Kojima K, Imoto S, Yamaguchi R, Fujita A, Yamauchi M, Gotoh N, Miyano S. Identifying Regulational Alterations in Gene Regulatory Networks by State Space Representation of Vector Autoregressive Models and Variational Annealing. BMC Genomics. 13: S6, 2012.
  32. Bustos-Valenzuela JC, Fujita A, Halcsik E, Granjeiro JM, Sogayar MC. Unveiling novel genes upregulated by both rhBMP2 and rhBMP7 during early osteoblastic transdifferentiation of C2C12 cells. BMC Research Notes. 4: 370, 2011.
  33. Nagasaki M, Saito A, Fujita A, Tremmel G, Ueno K, Ikeda E, Jeong E, Miyano S. Systems biology model repository for macrophage pathway simulation. Bioinformatics. 27:1591-1593, 2011.
  34. Kasparek T, Thomaz CE, Sato JR, Schwarz D, Janousova E,Marecek R, Prikryl R, Vanicek J, Fujita A, Ceskova E. Maximum-uncertainty linear discrimination analysis of first-episode schizophrenia subjects. Psychiatry Research: Neuroimaging. 191:174-181, 2011.
  35. Fujita A, Sato JR, Demasi MAA, Yamaguchi R, Shimamura T, Ferreira CE, Sogayar MC, Miyano S. Inferring contagion in regulatory networks. IEEE/ACM Transactions on Computational Biology and Bioinformatics. 8:570-576, 2011.
  36. Fujita A, Kojima, K, Patriota AG, Sato JR, Severino P, Miyano S. A fast and robust statistical test based on Likelihood ratio with Bartlett correction to identify Granger causality between gene sets. Bioinformatics. 26:2349-2351, 2010.
  37. Niida A, Imoto S, Yamaguchi R, Nagasaki M, Fujita A, Shimamura T, Miyano S. Model-free unsupervised gene set screening based on information enrichment in expression profiles. Bioinformatics.　26:3090-3097, 2010.
  38. Fujita A, Severino P, Sato JR, Miyano S. Granger causality in systems biology: modeling gene networks in time series microarray data using vector autoregressive models. Lecture Notes in Bioinformatics. 6268:13-24, 2010.
  39. Sato JR, Fujita A, Cardoso EF, Thomaz CE, Brammer MJ, Amaro Jr E. Analyzing the connectivity between regions of interest: an approach based on cluster Granger causality for fMRI data analysis. NeuroImage. 52:1444-1455, 2010.
  40. Shimamura T, Imoto S, Nagasaki M, Yamauchi M, Yamaguchi R, Fujita A, Tamada Y, Gotoh N, Miyano S. Collocation-based sparse estimation for constructing dynamic gene networks. Genome Informatics. 24:164-178, 2010.
  41. Fujita A, Nagasaki, M, Imoto S, Saito A, Ikeda E, Shimamura T, Yamaguchi R, Hayashizaki Y, Miyano S. Comparison of gene expression profiles produced by CAGE, illumina microarray and Real Time RT-PCR. Genome Informatics. 24:56-68, 2010.
  42. Fujita A, Sato JR, Kojima K, Gomes LR, Nagasaki M, Sogayar MC, Miyano S. Identification of Granger causality between gene sets. Journal of Bioinformatics and Computational Biology. 8:679-701, 2010.
  43. Fujita A, Patriota AG, Sato JR, Miyano S. The impact of measurement error in the identification of regulatory networks. BMC Bioinformatics. 10:412, 2009.
  44. Fujita A, Sato JR, da Silva FHL, Galvao MC, Sogayar MC, Miyano S. Quality control and reproducibility in DNA microarray experiments. Genome Informatics. 23:21-31, 2009.
  45. Shimamura T, Imoto S, Yamaguchi R, Fujita A, Nagasaki M, Miyano S. Recursive regularization for inferring gene networks from time-course gene expression profiles. BMC Systems Biology. 3:41, 2009.
  46. Fujita A, Sato JR, Demasi MAA, Sogayar MC, Ferreira CE, Miyano S. Comparing Pearson, Spearman and Hoeffding's D measure for gene expression association analysis. Journal of Bioinformatics and Computational Biology. 7(4):663-684, 2009.
  47. Sato JR, Martin MGM, Fujita A, Mourão-Miranda J, Brammer MJ, Amaro Jr E. An fMRI normative database for connectivity networks using one-class support vector machines. Human Brain Mapping. 30:1068-1076, 2009.
  48. Sato JR, Fujita A, Thomaz CE, Martin MG, Mourão-Miranda J, Brammer MJ, Amaro Jr E. Evaluating SVM and MLDA in the extraction of discriminant regions for mental state prediction. NeuroImage. 46:105-114, 2009.
  49. Fujita A, Gomes LR, Sato JR, Yamaguchi R, Thomaz CE, Sogayar MC, Miyano S. Multivariate gene expression analysis reveals functional connectivity changes between normal/tumoral prostates. BMC Systems Biology. 2:106, 2008.
  50. Hatanaka Y, Nagasaki M, Yamaguchi R, Obayashi T, Numata K, Fujita A, Shimamura T, Tamada Y, Imoto S, Kinoshita K, Nakai K, Miyano S. A novel strategy to search conserved transcriptional factor binding sites among coexpressing genes in human. Genome Informatics. 20:212-221, 2008.
  51. Kojima K, Fujita A, Shimamura T, Imoto S, Miyano S. Estimation of nonlinear gene regulatory networks via L1 regularized NVAR from time series gene expression data. Genome Informatics. 20:37-51, 2008.
  52. Sato JR, Thomaz CE, Cardoso EF, Fujita A, Morais-Martin MG, Amaro Jr E. Hyperplane Navigation: a method to set individual scores in fMRI group datasets. NeuroImage. 42:1473-1480, 2008.
  53. Fujita A, Sato JR, Garay-Malpartida HM, Sogayar MC, Ferreira CE, Miyano S. Modeling nonlinear gene regulatory networks from time-series gene expression data. Journal of Bioinformatics and Computational Biology. 6:961-79, 2008.
  54. Fujita A, Sato JR, Festa F, Gomes LR, Oba-Shinjo SM, Marie SKN, Ferreira CE, Sogayar MC. Identification of COL6A1 as a differentially expressed gene in human astrocytomas. Genetics and Molecular Research. 7:371-378, 2008.
  55. Fujita A, Sato JR, Ferreira CE, Sogayar MC. GEDI: a user-friendly toolbox for analysis of large-scale gene expression data. BMC Bioinformatics. 8:457, 2007.
  56. Fujita A, Sato JR, Garay-Malpartida HM, Yamaguchi R, Miyano S, Sogayar MC, Ferreira CE. Modeling gene expression regulatory networks with the sparse vector autoregressive model. BMC Systems Biology. 1:39, 2007.
  57. Fujita A, Sato JR, Garay-Malpartida HM, Morettin PA, Sogayar MC, Ferreira CE. Time-varying modeling of gene expression regulatory networks using wavelet dynamic vector autoregressive method. Bioinformatics. 23:1623-1630, 2007.
  58. Sato JR, Fujita A, Amaro Jr E, Miranda JM, Morettin PA, Brammer MJ. DWT-CEM: An algorithm for scale-temporal clustering in fMRI. Biological Cybernetics. 97:33-45, 2007.
  59. Fujita A, Sato JR, Rodrigues LO, Ferreira MC, Sogayar MC. Evaluating different methods of microarray data normalization. BMC Bioinformatics. 7:469, 2006.
  60. Fujita A, Massirer KB, Durham AM, Ferreira CE, Sogayar MC. The GATO gene annotation tool for research laboratories. Brazilian Journal of Medical and Biological Research. 38:1571-1574, 2005.