Markov mean properties for cell death-related protein classification

Gestal

Munteanu

et al. 2016

PeerJ

Self Cite

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.

Section: Discussionmentioning

confidence: 99%

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

Gestal

Munteanu

et al. 2016

PeerJ

Self Cite

“…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%

“…The collection of Weka machine-learning algorithms (Hall et al, 2009) was also used for the learning and classification processes in the detection of microstructural white matter degeneration in AD with DTI (M. Dyrba et al, 2013a) and for the detection of brain atrophy patterns based on MRI for the prediction and early detection of AD (Farhan, et al, 2014;Plant, et al, 2010). Weka is a full toolkit for classification and regression used for several other purposes such as the detection of autism (Kosmicki, et al, 2015), annotation of diverse human tissues (Ernst and Kellis, 2015) or for the classification of cell death-related proteins (Fernandez-Lozano et al, 2014a).…”

Section: Introductionmentioning

confidence: 99%

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

Munteanu

Expert Systems with Applications

Mato‐Abad

et al. 2015

Self Cite

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.

“…The QSAR models (Puzyn et al, 2010) were extensively used for antifungal (Gonzalez-Diaz et al, 2006), antiviral (Prado-Prado et al, 2011b, and antimalarial (Katritzky et al, 2006) drugs and they were extended to macromolecules such as proteins (González-Díaz and Uriarte, 2005;González-Díaz et al, 2009;Ivanciuc, 2009;Randic et al, 2007;Randić et al, 2006) or nucleic acids (Gonzalez-Diaz et al, 2005;Li and Wang, 2004;Randić et al, 2000). Previous studies on other protein functions were focused on anti-oxidant , transporter (Fernandez-Lozano et al, 2013), enzyme regulator (Concu et al, 2009;Fernandez-Lozano et al, 2014b), cell death-related (Fernandez-Lozano et al, 2014a) or cancer-related Munteanu et al, 2009) proteins. There are also other works which involves QSAR models for the prediction of protein inhibitors, where the structures or sequence of the proteins have been considered (Prado-Prado et al, 2012;Prado-Prado et al, 2011a;Speck-Planche et al, 2012;Speck-Planche et al, 2013;Viña et al, 2009) The scientific community pays special attention to signaling protein classification problems and further efforts are needed in order to complete the scientific knowledge in this field, as the classification of proteins is a key step in computational genomics.…”

Section: Introductionmentioning

confidence: 99%

Classification of signaling proteins based on molecular star graph descriptors using Machine Learning models

Journal of Theoretical Biology

Cuiñas

Seoane

et al. 2015

Self Cite

Signaling proteins are an important topic in drug development due to the increased importance of finding fast, accurate and cheap methods to evaluate new molecular targets involved in specific diseases. The complexity of the protein structure hinders the direct association of the signaling activity with the molecular structure. Therefore, the proposed solution involves the use of protein star graphs for the peptide sequence information encoding into specific topological indices calculated with S2SNet tool. The Quantitative Structure-Activity Relationship classification model obtained with Machine Learning techniques is able to predict new signaling peptides. The best classification model is the first signaling prediction model, which is based on eleven descriptors and it was obtained using the Support Vector Machines-Recursive Feature Elimination (SVM-RFE) technique with the Laplacian kernel (RFE-LAP) and an AUROC of 0.961. Testing a set of 3114 proteins of unknown function from the PDB database assessed the prediction performance of the model. Important signaling pathways are presented for three UniprotIDs (34 PDBs) with a signaling prediction greater than 98.0%.