Overlapping Clusters and Support Vector Machines based Interval Type-2 Fuzzy System for the Prediction of Peptide Binding Affinity

In the post-genome era, it is becoming more complex
to process high-dimensional, low-instance available and nonlinear
biological datasets. This study aims at addressing these characteristics as they have adverse effects on the performance of
predictive models in bioinformatics. In this paper, an interval
type-2 Takagi Sugeno fuzzy predictive model is proposed in
order to manage high-dimensionality and nonlinearity of such
datasets which is the common feature in bioinformatics. A new
clustering framework is proposed for this purpose to simplify
antecedent operations for an interval type-2 fuzzy system. This
new clustering framework is based on overlapping regions
between the clusters. The cluster analysis of partitions and
statistical information derived from them have identified the
upper and lower membership functions forming the premise
part. This is further enhanced by adapting the regression
version of support vector machines in the consequent part. The
proposed method is used in experiments to quantitatively predict
affinities of peptide bindings to biomolecules. This case study
imposes a challenge in post-genome studies and remains an
open problem due to the complexity of the biological system,
diversity of peptides and curse of dimensionality of amino acid
index representation characterising the peptides. Utilizing four
different peptide binding affinity datasets, the proposed method
resulted in better generalisation ability for all of them yielding
an improved prediction accuracy of up to 58.2% on unseen
peptides in comparison with the predictive methods presented
in the literature.