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Development of computational-statistical methods to construct, model and analyze biological networks associated with human diseases


The understanding of the biological mechanisms underlying human diseases is one of the main challenges in biological sciences. Although several efforts, the large number of heterogeneous factors that influence the genesis of a disease makes it a very hard task. One of the challenges consists in understanding diseases by developing methods to statistically analyze and computationally manipulate huge scale data. This difficulty is generated by ultra large data size, heterogeneity, multidimensionality, and presence of intrinsic noise. In this context, the main aim of this project is the development of computational statistical techniques to infer the phenomena that emerges from the interactions of different biological components involved in diseases. In other words, we will develop formal statistical methods in graphs (hypothesis test, model selection, parameter estimator, etc) in order to compare neural networks obtained by the modeling of functional and structural resonance imaging data; and to integrate genomic, transcriptomic and phenotype in cancer. This will allow the modeling and integration of biological data obtained in diverse collaborations between our group and biomedical labs (Lab. of RNA and micro RNAs regulation in diseases (Prof. Massirer) and Lab. of Molecular Biology (Prof. Bengtson), both at UNICAMP; neuroscience data from Prof. Sato at UFABC and Dr. Takahashi of Princeton University) and consequently aid biomedical researchers to elucidate the mechanisms involved in several diseases, in particular, cancer, cardiovascular diseases, diabetes, and neural disorders. (AU)

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Scientific publications (7)
(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)
SATO, JOAO RICARDO; VIDAL, MACIEL CALEBE; SANTOS, SUZANA DE SIQUEIRA; MASSIRER, KATLIN BRAUER; FUJITA, ANDRE. Complex Network Measures in Autism Spectrum Disorders. IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS, v. 15, n. 2, p. 581-587, . (12/00195-3, 13/00506-1, 13/03447-6, 14/09576-5, 13/10498-6)
FUJITA, ANDRE; TAKAHASHI, DANIEL Y.; PATRIOTA, ALEXANDRE G.; SATO, JOAO R.. A non-parametric statistical test to compare clusters with applications in functional magnetic resonance imaging data. STATISTICS IN MEDICINE, v. 33, n. 28, p. 4949-4962, . (12/21788-2, 11/50761-2, 14/09576-5, 13/10498-6)
SATO, JOAO RICARDO; BALARDIN, JOANA; VIDAL, MACIEL CALEBE; FUJITA, ANDRE. 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 & NEUROSCIENCE, v. 41, n. 2, p. 124-132, . (14/09576-5, 11/50761-2)
FONSECA, MONIQUE T.; RODRIGUES, ABNER C.; CEZAR, LUANA C.; FUJITA, ANDRE; SORIANO, FRANCISCO G.; STEINER, ALEXANDRE A.. Spontaneous hypothermia in human sepsis is a transient, self-limiting, and nonterminal response. Journal of Applied Physiology, v. 120, n. 12, p. 1394-1401, . (09/15530-0, 13/00506-1, 12/03831-8, 11/50761-2, 14/09576-5)
KINKER, GABRIELA SARTI; THOMAS, ANDREW MALTEZ; CARVALHO, VINICIUS JARDIM; LIMA, FELIPE PRATA; FUJITA, ANDRE. Deletion and low expression of NFKBIA are associated with poor prognosis in lower-grade glioma patients. SCIENTIFIC REPORTS, v. 6, . (14/27287-0, 15/01507-7, 15/01587-0, 13/03447-6, 14/09576-5)
SANTOS, SUZANA DE SIQUEIRA; DE ALMEIDA GALATRO, THAIS FERNANDA; WATANABE, RODRIGO AKIRA; OBA-SHINJO, SUELI MIEKO; NAGAHASHI MARIE, SUELY KAZUE; FUJITA, ANDRE. CoGA: An R Package to Identify Differentially Co-Expressed Gene Sets by Analyzing the Graph Spectra. PLoS One, v. 10, n. 8, . (14/09576-5, 12/25417-9, 11/50761-2, 13/03447-6)
VIDAL, MACIEL C.; SATO, JOAO R.; BALARDIN, JOANA B.; TAKAHASHI, DANIEL Y.; FUJITA, ANDRE. ANOCVA in R: A Software to Compare Clusters between Groups and Its Application to the Study of Autism Spectrum Disorder. FRONTIERS IN NEUROSCIENCE, v. 11, . (16/13422-9, 13/03447-6, 15/01587-0, 13/10498-6, 14/09576-5)

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