EnsembleGASVR: a novel ensemble method for classifying missense single nucleotide polymorphisms.
BACKGROUND
Single nucleotide polymorphisms (SNPs) are considered the most frequently occurring DNA sequence variations. Several computational methods have been proposed for the classification of missense SNPs to neutral and disease associated. However, existing computational approaches fail to select relevant features by choosing them arbitrarily without sufficient documentation. Moreover, they are limited to the problem of missing values, imbalance between the learning datasets and most of them do not support their predictions with confidence scores.


RESULTS
To overcome these limitations, a novel ensemble computational methodology is proposed. EnsembleGASVR facilitates a two-step algorithm, which in its first step applies a novel evolutionary embedded algorithm to locate close to optimal Support Vector Regression models. In its second step, these models are combined to extract a universal predictor, which is less prone to overfitting issues, systematizes the rebalancing of the learning sets and uses an internal approach for solving the missing values problem without loss of information. Confidence scores support all the predictions and the model becomes tunable by modifying the classification thresholds. An extensive study was performed for collecting the most relevant features for the problem of classifying SNPs, and a superset of 88 features was constructed. Experimental results show that the proposed framework outperforms well-known algorithms in terms of classification performance in the examined datasets. Finally, the proposed algorithmic framework was able to uncover the significant role of certain features such as the solvent accessibility feature, and the top-scored predictions were further validated by linking them with disease phenotypes.


METHODS
Datasets and codes are freely available on the Web at http://prlab.ceid.upatras.gr/EnsembleGASVR/dataset-codes.zip. All the required information about the article is available through http://prlab.ceid.upatras.gr/EnsembleGASVR/site.html.
King Abdullah University of Science and Technology (KAUST), Computer, Electrical and Mathematical Sciences and Engineering Division (CEMSE), Thuwal 23955-6900, Saudi Arabia, Computer Engineering and Informatics Department, University of Patras, Building B, Patras, 26504, Greece and Department of Social Work, School of Health Sciences, Technological Institute of Western Greece, Patras, Greece.
Trisevgeni
T
Rapakoulia
King Abdullah University of Science and Technology (KAUST), Computer, Electrical and Mathematical Sciences and Engineering Division (CEMSE), Thuwal 23955-6900, Saudi Arabia, Computer Engineering and Informatics Department, University of Patras, Building B, Patras, 26504, Greece and Department of Social Work, School of Health Sciences, Technological Institute of Western Greece, Patras, Greece.
Konstantinos
K
Theofilatos
King Abdullah University of Science and Technology (KAUST), Computer, Electrical and Mathematical Sciences and Engineering Division (CEMSE), Thuwal 23955-6900, Saudi Arabia, Computer Engineering and Informatics Department, University of Patras, Building B, Patras, 26504, Greece and Department of Social Work, School of Health Sciences, Technological Institute of Western Greece, Patras, Greece.
Dimitrios
D
Kleftogiannis
King Abdullah University of Science and Technology (KAUST), Computer, Electrical and Mathematical Sciences and Engineering Division (CEMSE), Thuwal 23955-6900, Saudi Arabia, Computer Engineering and Informatics Department, University of Patras, Building B, Patras, 26504, Greece and Department of Social Work, School of Health Sciences, Technological Institute of Western Greece, Patras, Greece.
Spiros
S
Likothanasis
King Abdullah University of Science and Technology (KAUST), Computer, Electrical and Mathematical Sciences and Engineering Division (CEMSE), Thuwal 23955-6900, Saudi Arabia, Computer Engineering and Informatics Department, University of Patras, Building B, Patras, 26504, Greece and Department of Social Work, School of Health Sciences, Technological Institute of Western Greece, Patras, Greece.
Athanasios
A
Tsakalidis
King Abdullah University of Science and Technology (KAUST), Computer, Electrical and Mathematical Sciences and Engineering Division (CEMSE), Thuwal 23955-6900, Saudi Arabia, Computer Engineering and Informatics Department, University of Patras, Building B, Patras, 26504, Greece and Department of Social Work, School of Health Sciences, Technological Institute of Western Greece, Patras, GreeceKing Abdullah University of Science and Technology (KAUST), Computer, Electrical and Mathematical Sciences and Engineering Division (CEMSE), Thuwal 23955-6900, Saudi Arabia, Computer Engineering and Informatics Department, University of Patras, Building B, Patras, 26504, Greece and Department of Social Work, School of Health Sciences, Technological Institute of Western Greece, Patras, Greece.
Seferina
S
Mavroudi
D000465:Algorithms; D019943:Amino Acid Substitution; D006801:Humans; D020125:Mutation, Missense; D020641:Polymorphism, Single Nucleotide