Machine learning for molecular systems biology (MLMSB) application on synthetic lethality, conditionally essential genes and cooperative transcription

• Grant number: 13/02018-4
• Support type: Regular Research Grants
• Duration: April 01, 2013 - March 31, 2015
• Field of knowledge: Biological Sciences - Biophysics - Biophysics of Processes and Systems
• Principal Investigator: Ney Lemke
• Grantee: Ney Lemke
• Home Institution: Instituto de Biociências (IBB). Universidade Estadual Paulista (UNESP). Campus de Botucatu. Botucatu , SP, Brazil
• Assoc. researchers: Jose Luiz Rybarczyk Filho

Abstract

The development of high-throughput techniques in biology is transforming biology in a data-rich discipline. We will consider in this project integrated biological networks: these networks deal with all the gene interactions mediated by metabolism, regulation and protein-protein interactions. We will deploy machine learning tools that will use topological data from these graphs, expression data, genomic organization and cellular localization to extract relevant biological information such as detection of drug target genes, morbid genes for humans or essential genes for bacteria. In this project we will use these information to investigate the influence of topological properties on synthetic lethality and conditionally essential genes. (AU)

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)

FERNANDES DE SOUZA, RAFAEL TOLEDO; LEWCZUK GERHARDT, GUENTHER JOHANNES; SCHOENWALD, SUZANA VEIGA; RYBARCZYK-FILHO, JOSE LUIZ; LEMKE, NEY. Synchronization and Propagation of Global Sleep Spindles. PLoS One, v. 11, n. 3 MAR 10 2016. Web of Science Citations: 7.

ZHANG, XUE; ACENCIO, MARCIO LUIS; LEMKE, NEY. Predicting Essential Genes and Proteins Based on Machine Learning and Network Topological Features: A Comprehensive Review. FRONTIERS IN PHYSIOLOGY, v. 7, MAR 8 2016. Web of Science Citations: 21.

KANDOI, GAURAV; ACENCIO, MARCIO L.; LEMKE, NEY. Prediction of Druggable Proteins Using Machine Learning and Systems Biology: A Mini-Review. FRONTIERS IN PHYSIOLOGY, v. 6, DEC 8 2015. Web of Science Citations: 7.

POLLO-OLIVEIRA, LETICIA; POST, HARM; ACENCIO, MARCIO LUIS; LEMKE, NEY; VAN DEN TOORN, HENK; TRAGANTE, VINICIUS; HECK, ALBERT J. R.; ALTELAAR, A. F. MAARTEN; YATSUDA, ANA PATRICIA. Unravelling the Neospora caninum secretome through the secreted fraction (ESA) and quantification of the discharged tachyzoite using high-resolution mass spectrometry-based proteomics. PARASITES & VECTORS, v. 6, NOV 23 2013. Web of Science Citations: 8.

ACENCIO, MARCIO LUIS; BOVOLENTA, LUIZ AUGUSTO; CAMILO, ESTHER; LEMKE, NEY. Prediction of Oncogenic Interactions and Cancer-Related Signaling Networks Based on Network Topology. PLoS One, v. 8, n. 10 OCT 25 2013. Web of Science Citations: 5.

CAMILO, ESTHER; BOVOLENTA, LUIZ A.; ACENCIO, MARCIO L.; RYBARCZYK-FILHO, JOSE L.; CASTRO, MAURO A. A.; MOREIRA, JOSE C. F.; LEMKE, NEY. GALANT: a Cytoscape plugin for visualizing data as functional landscapes projected onto biological networks. Bioinformatics, v. 29, n. 19, p. 2505-2506, OCT 1 2013. Web of Science Citations: 3.

VALENTE, GUILHERME T.; ACENCIO, MARCIO L.; MARTINS, CESAR; LEMKE, NEY. The Development of a Universal In Silico Predictor of Protein-Protein Interactions. PLoS One, v. 8, n. 5 MAY 31 2013. Web of Science Citations: 16.