A mixed integer programming-based global optimization framework for analyzing gene expression data

DOI10.1007/s10898-017-0530-0zbMath1381.90061OpenAlexW2613975428MaRDI QIDQ1683332

Giovanni Felici, Mario Rosario Guarracino, Kumar Parijat Tripathi, Daniela Evangelista

Publication date: 7 December 2017

Published in: Journal of Global Optimization (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10898-017-0530-0

zbMATH Keywords

global optimizationstatistical significancefeature selectiongene expressionMIPpathways

Mathematics Subject Classification ID

Mixed integer programming (90C11) Nonconvex programming, global optimization (90C26) Genetics and epigenetics (92D10)

Uses Software

• CPLEX
• affy
• limma
• DAVID
• FastAnnotator
• Transcriptator
• MISSEL

Cites Work

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• Unnamed Item
• Integer programming models for feature selection: new extensions and a randomized solution algorithm
• Feature selection for support vector machines via mixed integer linear programming
• Classification and characterization of gene expression data with generalized eigenvalues
• Logical analysis of binary data with missing bits
• Statistics and Causal Inference
• COMMENTS ON NEUBERG'S REVIEW OF CAUSALITY
• 10.1162/153244303322753616
• THE PROBABLE ERROR OF A MEAN

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