A machine learning approach for genome-wide prediction of morbid and druggable human genes based on systems-level data

Costa‚ P.R.; Acencio‚ M.L.; Lemke‚ N.

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(Reference retrieved automatically from Google Scholar through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

A machine learning approach for genome-wide prediction of morbid and druggable human genes based on systems-level data

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Author(s):	Costa‚ P.R. ; Acencio‚ M.L. ; Lemke‚ N. Total Authors: 3
Document type:	Journal article
Source:	BMC Genomics; v. 11, n. Suppl 5, p. S9, 2010.

FAPESP's process:	07/02827-9 - Characterization and simulation of biological networks
Grantee:	Ney Lemke
Support Opportunities:	Regular Research Grants


FAPESP's process:	07/01213-7 - In silico construction of integrated molecular networks encompassing cell cycle regulation by extracellular matrix adhesion in Homo sapiens
Grantee:	Marcio Luis Acencio
Support Opportunities:	Scholarships in Brazil - Doctorate


FAPESP's process:	07/08466-8 - Construction and analysis of the molecular integrated network of human genes
Grantee:	Pedro Rafael Costa
Support Opportunities:	Scholarships in Brazil - Scientific Initiation

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