Kernel-Based Feature Selection Techniques for Transport Proteins Based on Star Graph Topological Indices

Fernández-Lozano, Carlos; Gestal, Marcos; Souto, Nieves Pedreira; Postelnicu, Lucian; Dorado, Julián; Munteanu, Cristian R.

doi:10.2174/15680266113139990119

Cited by 18 publications

(16 citation statements)

References 67 publications

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“…Furthermore, a recent work carried out by the authors of the current study has employed this approach to assess whether the texture information could be used in proteomics to improve the quality of the image analysis of proteins separated on a gel (Fernandez-Lozano et al, 2016). These works were based on the experience of the group obtained in the processing of several cheminformatics datasets related to classification tasks (Fernandez-Lozano et al, 2013; Fernandez-Lozano et al, 2014; Aguiar-Pulido et al, 2013). …”

Section: Discussionmentioning

confidence: 99%

A methodology for the design of experiments in computational intelligence with multiple regression models

Fernández-Lozano

Gestal

Munteanu

et al. 2016

PeerJ

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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 field of Computational Intelligence and especially on a correct comparison between the different results provided for different methods, as those techniques are complex systems that require further study to be fully understood. A methodology commonly accepted in Computational intelligence is implemented in an R package called RRegrs. This package includes ten simple and complex regression models to carry out predictive modeling using Machine Learning and well-known regression algorithms. The framework for experimental design presented herein is evaluated and validated against RRegrs. Our results are different for three out of five state-of-the-art simple datasets and it can be stated that the selection of the best model according to our proposal is statistically significant and relevant. It is of relevance to use a statistical approach to indicate whether the differences are statistically significant using this kind of algorithms. Furthermore, our results with three real complex datasets report different best models than with the previously published methodology. Our final goal is to provide a complete methodology for the use of different steps in order to compare the results obtained in Computational Intelligence problems, as well as from other fields, such as for bioinformatics, cheminformatics, etc., given that our proposal is open and modifiable.

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Section: Discussionmentioning

confidence: 99%

A methodology for the design of experiments in computational intelligence with multiple regression models

Fernández-Lozano

Gestal

Munteanu

et al. 2016

PeerJ

Self Cite

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“…ANNs have been widely used for different problems, such as data analysis, non-invasive diagnosis in the medical field (Ding et al, 2014;S. Resino et al, 2011), linking chemical knowledge (GomezCarracedo et al, 2007;Gómez-Carracedo et al, 2007) to protein function prediction (C. Fernandez-Lozano et al, 2013a, 2014a, 2014b or, as mentioned before, for AD classification with morphometric measures (Aguilar et al, 2013;Escudero et al, 2011). ANNs consist of many simple, interconnected computational units.…”

Section: Artificial Neural Networkmentioning

confidence: 99%

Classification of mild cognitive impairment and Alzheimer’s Disease with machine-learning techniques using 1H Magnetic Resonance Spectroscopy data

Munteanu

Fernández-Lozano

Mato‐Abad

et al. 2015

Expert Systems with Applications

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Please cite this article as: Munteanu, C.R., Fernandez-Lozano, C., Abad, V.M., Fernández, S.P., Álvarez-Linera, J., Hernández-Tamames, J.A., Pazos, A., Classification of mild cognitive impairment and Alzheimer's disease with machine-learning techniques using 1 H Magnetic Resonance Spectroscopy data, Expert Systems with Applications (2015), doi: http://dx.Abstract Several magnetic resonance techniques have been proposed as non-invasive imaging biomarkers for the evaluation of disease progression and early diagnosis of Alzheimer's Disease (AD). This work is the first application of the Proton Magnetic Resonance Spectroscopy 1 H-MRS data and machine-learning techniques to the classification of AD. A gender-matched cohort of 260 subjects aged between 57 and 99 years from the Alzheimer's Disease Research Unit, of the Fundación CIEN-Fundación Reina Sofía has been used. A single-layer perceptron was found for AD prediction with only two spectroscopic voxel volumes (Tvol and CSFvol) in the left hippocampus, with an AUROC value of 0.866 (with TPR 0.812 and FPR 0.204) in a filter feature selection approach. These results suggest that knowing the composition of white and grey matter and cerebrospinal fluid of the spectroscopic voxel is essential in a 1 H-MRS study to improve the accuracy of the quantifications and classifications, particularly in those studies involving elder patients and neurodegenerative diseases.

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“…Obviously, the test partitions have not been rebalanced and are the same as the ones used for PAES-3obj. We recall that, in a number of contributions, the SMOTE algorithm has been recognized as one of the most suitable algorithms for rebalancing the training set and allowing traditional classification algorithms to achieve good results with imbalanced datasets [9,[61][62][63][64][65].…”

Section: Comparison Between Paes-3obj and Two State-of-the-art Fuzzy mentioning

confidence: 99%

An experimental study on evolutionary fuzzy classifiers designed for managing imbalanced datasets

Antonelli¹,

Ducange²,

Marcelloni³

2014

Neurocomputing

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Kernel-Based Feature Selection Techniques for Transport Proteins Based on Star Graph Topological Indices

Cited by 18 publications

References 67 publications

A methodology for the design of experiments in computational intelligence with multiple regression models

A methodology for the design of experiments in computational intelligence with multiple regression models

Classification of mild cognitive impairment and Alzheimer’s Disease with machine-learning techniques using 1H Magnetic Resonance Spectroscopy data

An experimental study on evolutionary fuzzy classifiers designed for managing imbalanced datasets

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