النشر العلمي

  • Kernel Logistic Regression Algorithm for Large- Scale Data Classification

Abstract: Kernel Logistic Regression (KLR) is a powerful classification technique that has been applied successfully in many
classification problems. However, it is often not found in large-scale data classification problems and this is mainly because it
is computationally expensive. In this paper, we present a new KLR algorithm based on Truncated Regularized Iteratively Reweighted
Least Squares(TR-IRLS) algorithm to obtain sparse large-scale data classification in short evolution time. This new
algorithm is called Nystrom Truncated Kernel Logistic Regression (NTR-KLR). The performance achieved using NTR-KLR
algorithm is comparable to that of Support Vector Machines (SVMs) methods. The advantage is NTR-KLR can yield
probabilistic outputs and its extension to the multi class case is well defined. In addition, its computational complexity is lower
than that of SVMs methods and it is easy to implement.

published in The International Arab Journal of Information Technology

  • Predicting beta-turns in proteins using support vector machines with fractional polynomials

Abstract
Background: β-turns are secondary structure type that have essential role in molecular recognition, protein
folding, and stability. They are found to be the most common type of non-repetitive structures since 25% of amino
acids in protein structures are situated on them. Their prediction is considered to be one of the crucial problems in
bioinformatics and molecular biology, which can provide valuable insights and inputs for the fold recognition and
drug design.
Results: We propose an approach that combines support vector machines (SVMs) and logistic regression (LR) in a
hybrid prediction method, which we call (H-SVM-LR) to predict β-turns in proteins. Fractional polynomials are used
for LR modeling. We utilize position specific scoring matrices (PSSMs) and predicted secondary structure (PSS) as
features. Our simulation studies show that H-SVM-LR achieves Qtotal of 82.87%, 82.84%, and 82.32% on the BT426,
BT547, and BT823 datasets respectively. These values are the highest among other β-turns prediction methods that
are based on PSSMs and secondary structure information. H-SVM-LR also achieves favorable performance in
predicting β-turns as measured by the Matthew’s correlation coefficient (MCC) on these datasets. Furthermore,
H-SVM-LR shows good performance when considering shape strings as additional features.
Conclusions: In this paper, we present a comprehensive approach for β-turns prediction. Experiments show that
our proposed approach achieves better performance compared to other competing prediction methods.

published in Proteome Science

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