Events MOL2NET'17, Conference on Molecular, Biomed., Comput. & Network Science and Engineering, 3rd ed.

Event submissions

Published

This submission belongs to the session 03. USEDAT-03: USA-EU Data Analysis Training Prog. Work., Cambridge, UK-Bilbao, Spain-Duluth, USA, 2017 of the event MOL2NET'17, Conference on Molecular, Biomed., Comput. & Network Science and Engineering, 3rd ed.

Published date

23 Jan, 2018

Citation

Viviana Quevedo, Alejandro Pazos-Sierra, Bernabé Ortega-Tenezaca, Humbert González-Díaz, Julio César Vargas-Burgos, Prediction of RIFIN proteins with gene orientation network indices, in Proceedings of MOL2NET'17, Conference on Molecular, Biomed., Comput. & Network Science and Engineering, 3rd ed., 15 January–15 December 2017, MDPI: Basel, Switzerland, doi: 10.3390/mol2net-03-05124

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Prediction of RIFIN proteins with gene orientation network indices

Viviana Quevedo ^1,2

Bernabé Ortega-Tenezaca ²

Julio César Vargas-Burgos ²

Alejandro Pazos-Sierra ¹

Humbert González-Díaz ³

1. RNASA-IMEDIR, Computer Science Faculty, University of A Coruña, 15071, A Coruña, Spain

2. Universidad Estatal Amazónica

3. Dept. of Organic Chemistry II, University of the Basque Country UPV/EHU, 48940, Leioa, Biscay, Spain

Abstract

Gene orientation may have a direct influence on the expression of genes. In this work, we developed a Linear Discriminant Analysis (LDA) model able to predict RIFIN-like proteins of out of 5365 of Plasmodium falciparum proteins with Sensitivity and Specificity 70-80% in training and external validation series.

Keywords

Malaria

Plasmodium sp. proteome

Chromosome microstructure

Gene orientation

Complex Networks

Machine Learning

Poster

MOL2NET-2017-template 2.pdf

Employment of hyphenated approach for metabolomic fingerprinting of phenolics from Torilis leptophylla roots

Study of microbiological laboratory techniques for the etiological diagnosis and guidance in the treatment of invasive candidiasis