Over the past decade, the field of bioinformatics and computer modeling applied to biological challenges has been growing exponentially. Many approaches have been developed that tackle applications ranging from protein classification, structure prediction to disease prediction. This work focuses on the design of a novel kernel for the Radial Basis Function Neural Networks (RBFNN). The proposed kernel is based on weighted cosine distance between the input vector and the center vectors associated with RBFNN. The weighting is introduced in: i) the inner product of input and neuron’s center, ii) norm of the input vector, and iii) norm of the center vector. We demonstrate how the weighting matrix can be chosen for different applications to optimize the performance of the WC-RBF. As case studies, we present the PDZ domain classification and the channel estimation problem with correlated inputs. We also design an adaptive technique to update the weighting matrix for an arbitrary data using the approach of steepest descent optimization. For the validation of our adaptive design, we present the problem of Leukemia disease prediction. Simulations are presented to validate the performance of the proposed RBFNN kernel in contrast to the conventional RBFNN kernels.
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