Improving eQTL Analysis Using a Machine Learning Approach for Data Integration

Beretta, S., Castelli, M., Gonçalves, I., Kel, I., Giansanti, V., & Merelli, I. (2018). Improving eQTL Analysis Using a Machine Learning Approach for Data Integration: A Logistic Model Tree Solution. Journal of Computational Biology, 25(10), 1091-1105. DOI: 10.1089/cmb.2017.0167

Bibliographic Details
Main Author: Beretta, Stefano (author)
Other Authors: Castelli, Mauro (author), Gonçalves, Ivo (author), Kel, Ivan (author), Giansanti, Valentina (author), Merelli, Ivan (author)
Format: article
Language:eng
Published: 2019
Subjects:
data integration
eQTL analysis
evolutionary algorithm
genetic programming
machine learning
Modelling and Simulation
Molecular Biology
Genetics
Computational Mathematics
Computational Theory and Mathematics
Online Access:http://hdl.handle.net/10362/85459
Country:Portugal
Oai:oai:run.unl.pt:10362/85459
Description
Summary:Beretta, S., Castelli, M., Gonçalves, I., Kel, I., Giansanti, V., & Merelli, I. (2018). Improving eQTL Analysis Using a Machine Learning Approach for Data Integration: A Logistic Model Tree Solution. Journal of Computational Biology, 25(10), 1091-1105. DOI: 10.1089/cmb.2017.0167

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