Clustermatch: discovering hidden relations in highly diverse kinds of qualitative and quantitative data without standardization-Reference-Cited by-同舟云学术

Clustermatch: discovering hidden relations in highly diverse kinds of qualitative and quantitative data without standardization

Published:2018-10-24 Issue:11 Volume:35 Page:1931-1939
ISSN:1367-4803
Container-title:Bioinformatics
language:en
Short-container-title:

Author:

Pividori Milton¹²^ORCID,Cernadas Andres³,de Haro Luis A³,Carrari Fernando³⁴⁵⁶,Stegmayer Georgina⁷^ORCID,Milone Diego H⁷^ORCID

Affiliation:

1. Center for Translational Data Science, The University of Chicago, Chicago IL, USA

2. Department of Medicine, Section of Genetic Medicine, The University of Chicago, Chicago IL, USA

3. Instituto de Fisiología, Biología Molecular y Neurociencias (IFIBYNE-UBA-CONICET) Ciudad Universitaria, C1428EHA Buenos Aires, Argentina

4. Instituto de Biotecnología, Instituto Nacional de Tecnología Agropecuaria (IB-INTA), and Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), B1686WAA Castelar, Argentina

5. Departamento de Botânica, Instituto de Biociências, Universidade de São Paulo, Rua do Matão, 277, São Paulo, Brazil

6. Facultad de Agronomía, Universidad de Buenos Aires, Argentina

7. Research Institute for Signals, Systems and Computational Intelligence, sinc(i), FICH/UNL-CONICET, Santa Fe, Argentina

Funder

Consejo Nacional de Investigaciones Cientificas y Tecnicas

Universidad Nacional del Litoral

Agencia Nacional de Promoción Científica y Tecnológica

European Union Horizon 2020 Research and Innovation Programme

Agencia Santafesina de Ciencia, Tecnología e Innovación

Publisher

Oxford University Press (OUP)

Subject

Computational Mathematics,Computational Theory and Mathematics,Computer Science Applications,Molecular Biology,Biochemistry,Statistics and Probability

Link

http://academic.oup.com/bioinformatics/article-pdf/35/11/1931/28759338/bty899.pdf

Reference33 articles.

1. Minerva and minepy: a c engine for the mine suite and its r, python and matlab wrappers;Albanese;Bioinformatics,2013

2. A new algorithm to optimize maximal information coefficient;Chen;PLoS One,2016

3. Neuronal network analyses reveal novel associations between volatile organic compounds and sensory properties of tomato fruits;Cortina;Metabolomics,2018

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