EnsembleGASVR: A novel ensemble method for classifying missense single nucleotide polymorphisms
Theofilatos, Konstantinos A.
Kleftogiannis, Dimitrios A.
Likothanasis, Spiridon D.
Tsakalidis, Athanasios K.
Mavroudi, Seferina P.
KAUST DepartmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Computer Science Program
Online Publication Date2014-04-26
Print Publication Date2014-08-15
Permanent link to this recordhttp://hdl.handle.net/10754/563511
MetadataShow full item record
AbstractMotivation: 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 ofmissing 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 twostep 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. © The Author 2014.
SponsorsFunding: Trisevgeni Rapakoulia and Dimitrios Kleftogiannis were supported by the King Abdullah University of Science and Technology (KAUST).
PublisherOxford University Press (OUP)
- Prediction of the phenotypic effects of non-synonymous single nucleotide polymorphisms using structural and evolutionary information.
- Authors: Bao L, Cui Y
- Issue date: 2005 May 15
- A novel missense-mutation-related feature extraction scheme for 'driver' mutation identification.
- Authors: Tan H, Bao J, Zhou X
- Issue date: 2012 Nov 15
- Knowledge-based computational mutagenesis for predicting the disease potential of human non-synonymous single nucleotide polymorphisms.
- Authors: Masso M, Vaisman II
- Issue date: 2010 Oct 21
- Predicting the insurgence of human genetic diseases associated to single point protein mutations with support vector machines and evolutionary information.
- Authors: Capriotti E, Calabrese R, Casadio R
- Issue date: 2006 Nov 15
- A Feature and Algorithm Selection Method for Improving the Prediction of Protein Structural Class.
- Authors: Ni Q, Chen L
- Issue date: 2017