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Storage, modeling and analysis of dynamical systems for e-Science applications

Grant number: 15/01587-0
Support type:Research Grants - eScience Program - Thematic Grants
Duration: February 01, 2016 - January 31, 2020
Field of knowledge:Physical Sciences and Mathematics - Computer Science
Principal Investigator:João Eduardo Ferreira
Home Institution: Instituto de Matemática e Estatística (IME). Universidade de São Paulo (USP). São Paulo, SP, Brazil
Co-Principal Investigators:Marcel Parolin Jackowski ; Paulo Sergio Graziano Magalhães ; Roberto Hirata Junior ; Ronaldo Fumio Hashimoto
Assoc. researchers:André Fujita ; André Santanchè ; Antonio Maria Francisco Luiz Jose Bonomi ; Ariane Machado Lima ; Carlos Eduardo Driemeier ; Carlos Eduardo Ferreira ; Cléver Ricardo Guareis de Farias ; David Corrêa Martins Junior ; Ester Cerdeira Sabino ; Fábio Vale Scarpare ; Felipe Werndl Trevizan ; Gisela Tunes da Silva ; Henrique Coutinho Junqueira Franco ; Junior Barrera ; Karina Valdivia Delgado ; Leliane Nunes de Barros ; Leonardo Lamas Leandro Ribeiro ; Luciano Vieira de Araújo ; Marcelo da Silva Reis ; Marcio Ferreira da Silva ; Marcio Katsumi Oikawa ; Marco Dimas Gubitoso ; Maria Teresa Borges Pimenta Barbosa ; Michelle Cristina Araujo Picoli ; Otavio Cavalett ; Routo Terada ; Vera Lúcia Reis de Gouveia
Associated grant(s):16/11515-0 - 25th International Joint Conference on Artificial Intelligence: advances in bioinformatics and artificial intelligence: bridging the gap, AR.EXT
Associated scholarship(s):17/01330-5 - 3D image analysis for realistic modeling and simulation of biomass conversion processes, BP.PD
16/22900-1 - Markov decision processes specified by probabilistic logic programming: representation and solution, BP.DD


The astonishing fast rate of technology evolution has given rise to a new era of scientific knowledge discovery. This new age of science, known as e-Science, is described as the new great computational and multidisciplinary team science requiring novel methodologies for data storage, modeling and analysis. In particular, the development of transactional and analytical software systems for e-Science applications when viewed from a dynamic systems perspective, presents a number of new computational challenges. Among them are the scientific knowledge discovery processes, which involve frequent changes of requirements for data storage, modeling and analysis. The need to address scientific dynamic systems in order to cope with complex applications of e-Science has woken up the scientific community for the development of robust and evolutionary software systems to meet these new challenges. Dynamical system is the classical mathematical formalism to represent phenomena that evolute with time, which are of great interest in science. In this project, our main goal is to develop computational models and methodologies to support e-Science applications viewed as dynamic systems. Our fundamental research covers three key research areas, namely storage, modeling, and analysis of dynamic systems. These research areas are significant and relevant to the FAPESP e-Science Program since many of the present e-Science challenges are related to the proper treatment of dynamic systems. Hence, we plan to develop and apply computational methodologies that will ease the development of e-Science applications, thereby contributing to the improvement of worldwide scientific knowledge, while respecting legal and ethical restrictions in data management. (AU)

Articles published in Agência FAPESP about the research grant
The use of bioinformatics in the study of complex diseases 

Scientific publications (10)
(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)
ATASHPAZ-GARGARI, ESMAEIL; REIS, MARCELO S.; BRAGA-NETO, ULISSES M.; BARRERA, JUNIOR; DOUGHERTY, EDWARD R. A fast Branch-and-Bound algorithm for U-curve feature selection. PATTERN RECOGNITION, v. 73, p. 172-188, JAN 2018. Web of Science Citations: 0.
JACOMINI, RICARDO DE SOUZA; MARTINS, JR., DAVID CORREA; DA SILVA, FELIPE LENO; REALI COSTA, ANNA HELENA. GeNICE: A Novel Framework for Gene Network Inference by Clustering, Exhaustive Search, and Multivariate Analysis. JOURNAL OF COMPUTATIONAL BIOLOGY, v. 24, n. 8, p. 809-830, AUG 2017. Web of Science Citations: 0.
FUJITA, ANDRE; TAKAHASHI, DANIEL YASUMASA; BALARDIN, JOANA BISOL; VIDAL, MACIEL CALEBE; SATO, JOAO RICARDO. Correlation between graphs with an application to brain network analysis. COMPUTATIONAL STATISTICS & DATA ANALYSIS, v. 109, p. 76-92, MAY 2017. Web of Science Citations: 0.
FUJITA, ANDRE; VIDAL, MACIEL C.; TAKAHASHI, DANIEL Y. A Statistical Method to Distinguish Functional Brain Networks. FRONTIERS IN NEUROSCIENCE, v. 11, FEB 14 2017. Web of Science Citations: 2.
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, JAN 24 2017. Web of Science Citations: 0.
DRIEMEIER, CARLOS; LING, LIU YI; SANCHES, GUILHERME M.; PONTES, ANGELICA O.; GRAZIANO MAGALHAES, PAULO S.; FERREIRA, JOAO E. A computational environment to support research in sugarcane agriculture. COMPUTERS AND ELECTRONICS IN AGRICULTURE, v. 130, p. 13-19, NOV 15 2016. Web of Science Citations: 0.
GOYA, DENISE H.; NAKAMURA, DIONATHAN; TERADA, ROUTO. Certificateless Key Agreement Protocols under Strong Models. IEICE TRANSACTIONS ON FUNDAMENTALS OF ELECTRONICS COMMUNICATIONS AND COMPUTER SCIENCES, v. E99A, n. 10, p. 1822-1832, OCT 2016. Web of Science Citations: 0.
MOREIRA, DANIEL A. M.; DELGADO, KARINA VALDIVIA; DE BARROS, LELIANE NUNES. Robust probabilistic planning with ilao. APPLIED INTELLIGENCE, v. 45, n. 3, p. 662-672, OCT 2016. Web of Science Citations: 0.
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, APR 7 2016. Web of Science Citations: 0.
MEKKAOUI, CHOUKRI; METELLUS, PHILIPPE; KOSTIS, WILLIAM J.; MARTUZZI, ROBERTO; PEREIRA, FABRICIO R.; BEREGI, JEAN-PAUL; REESE, TIMOTHY G.; CONSTABLE, TODD R.; JACKOWSKI, MARCEL P. Diffusion Tensor Imaging in Patients with Glioblastoma Multiforme Using the Supertoroidal Model. PLoS One, v. 11, n. 1 JAN 13 2016. Web of Science Citations: 3.

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