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Using Genetic Algorithms to Improve Support Vector Regression in the Analysis of Atomic Spectra of Lubricant Oils
(Emerald, 2016-06)
[Abstract] Purpose
– The purpose of this paper is to assess the quality of commercial lubricant oils. A spectroscopic method was used in combination with multivariate regression techniques (ordinary multivariate ...
Integrative Multi-Omics Data-Driven Approach for Metastasis Prediction in Cancer
(ACM, 2018)
[Abstract]
Nowadays biomedical research is generating huge amounts of omic data, covering all levels of genetic information from nucleotide sequencing to protein metabolism. In the beginning, data were analyzed independently ...
A methodology for the design of experiments in computational intelligence with multiple regression models
(Peer J, 2016-12-01)
[Abstract] The design of experiments and the validation of the results achieved with them are vital in any research study. This paper focuses on the use of different Machine Learning approaches for regression tasks in the ...
Biomedical data integration in computational drug design and bioinformatics
(Bentham Science, 2013)
[Abstract In recent years, in the post genomic era, more and more data is being generated by biological high throughput technologies, such as proteomics and transcriptomics. This omics data can be very useful, but the real ...
Approach of Genetic Algorithms With Grouping Into Species Optimized With Predator-Prey Method for Solving Multimodal Problems
(Springer, 2012)
[Abstract] Over recent years, Genetic Algorithms have proven to be an appropriate tool for solving certain problems. However, it does not matter if the search space has several valid solutions, as their classic approach ...
Graph-based processing of macromolecular information
(Bentham Science, 2015)
[Abstract] The complex information encoded into the element connectivity of a system gives rise to the possibility of graphical processing of divisible systems by using the Graph theory. An application in this sense is the ...
