arXiv subjects

G. Sahoo

Publications and source records attributed to G. Sahoo.

21 records · Page 2Linked to original sources

arXiv2011

Artificial Immune Privileged Sites as an Enhancement to Immuno-Computing Paradigm

The immune system is a highly parallel and distributed intelligent system which has learning, memory, and associative capabilities. Artificial Immune System is an evolutionary paradigm inspired by the biological aspects of the immune system of mammals. The immune system can inspire to form new algorithms learning from its course of action. The human immune system has motivated scientists and engineers for finding powerful information processing algorithms that has solved complex engineering problems. This work is the result of an attempt to explore a different perspective of the immune system namely the Immune Privileged Site (IPS) which has the ability to make an exception to different parts of the body by not triggering immune response to some of the foreign agent in these parts of the body. While the complete system is secured by an Immune System at certain times it may be required that the system allows certain activities which may be harmful to other system which is useful to it and learns over a period of time through the immune privilege model as done in case of Immune Privilege Sites in Natural Immune System.

cs.CR↗

arXiv2010

Algorithm for Predicting Protein Secondary Structure

Predicting protein structure from amino acid sequence is one of the most important unsolved problems of molecular biology and biophysics.Not only would a successful prediction algorithm be a tremendous advance in the understanding of the biochemical mechanisms of proteins, but, since such an algorithm could conceivably be used to design proteins to carry out specific functions.Prediction of the secondary structure of a protein (alpha-helix, beta-sheet, coil) is an important step towards elucidating its three dimensional structure as well as its function. In this research, we use different Hidden Markov models for protein secondary structure prediction. In this paper we have proposed an algorithm for predicting protein secondary structure. We have used Hidden Markov model with sliding window for secondary structure prediction.The secondary structure has three regular forms, for each secondary structural element we are using one Hidden Markov Model.

cs.CE↗

arXiv2010

A Probabilistic Model For Sequence Analysis

This paper presents a probabilistic approach for DNA sequence analysis. A DNA sequence consists of an arrangement of the four nucleotides A, C, T and G and different representation schemes are presented according to a probability measure associated with them. There are different ways that probability can be associated with the DNA sequence: one way is when the probability of an occurrence of a letter does not depend on the previous one (termed as unsuccessive probability) and in another scheme the probability of occurrence of a letter depends on its previous letter (termed as successive probability). Further, based on these probability measures graphical representations of the schemes are also presented. Using the diagram probability measure one can easily calculate an associated probability measure which can serve as a parameter to check how close is a new sequence to already existing ones.

q-bio.QM↗