No abstract

No abstract

Identification of disordered regions in protein sequence(s) is a very important problem and a long standing puzzle to biologists. In many proteins, these structural disorders (an outcome of improper protein folding) inside an otherwise ordered structure exhibit many important biological functions. Classical approaches to identify these regions in a sequence rely mainly on the pattern recognition algorithms where amino acid residues are classified as ordered or disordered. In this work, we employ recurrence quantification analysis based approach to understand the complex dynamics underlying these regions. We hypothesize and demonstrate that proteins with disordered regions show a strong evidence of the order-chaos-order transition pattern by using windowed recurrence quantification analysis on a database of 476 proteins available from .

No abstract

In this paper we propose a method to enhance noise reduction for data generated from a known differential equation. We develop a theoretical basis for the procedure and then illustrate it in the case of some data generated using Duffing's equation. This method consists of embedding the data in higher dimensions and then transforming the data into a lower dimension using a singular matrix. We show that the singular matrix squeezes out some of the noise and leaves the true signal intact. Finally, using a nonlinear function, we reverse the effect of the singular matrix to get closer to the original data.