Biologically inspired ChaosNet architecture for Hypothetical Protein Classification

TL;DR

This paper introduces ChaosNet, a biologically inspired neural network using chaotic maps, demonstrating its effectiveness in classifying Hypothetical proteins with less training data than traditional methods.

Contribution

The paper applies ChaosNet to bioinformatics, specifically Hypothetical protein classification, showing its competitive performance with reduced data requirements.

Findings

ChaosNet achieves comparable or better accuracy than traditional ANNs.

It requires significantly less training data.

Effective in classifying complex biological data.

Abstract

ChaosNet is a type of artificial neural network framework developed for classification problems and is influenced by the chaotic property of the human brain. Each neuron of the ChaosNet architecture is the one-dimensional chaotic map called the Generalized Luroth Series (GLS). The addition of GLS as neurons in ChaosNet makes the computations straightforward while utilizing the advantageous elements of chaos. With substantially less data, ChaosNet has been demonstrated to do difficult classification problems on par with or better than traditional ANNs. In this paper, we use Chaosnet to perform a functional classification of Hypothetical proteins [HP], which is indeed a topic of great interest in bioinformatics. The results obtained with significantly lesser training data are compared with the standard machine learning techniques used in the literature.