This document provides an overview of using soft computing techniques for DNA sequence classification. It discusses DNA and DNA sequencing. It then introduces common soft computing techniques used for classification, including neural networks, fuzzy logic, and genetic algorithms. The document proposes using these soft computing methods for DNA sequence classification and describes related studies. It outlines a methodology using neural networks and genetic algorithms and analyzes the advantages of soft computing for this application. In conclusion, it states that soft computing techniques are well-suited for DNA sequence classification problems.

1. Classification of DNA
Sequence Using Soft
Computing
Techniques: A Survey

2. Today’s agenda
 Introduction
 DNA
 DNA sequencing
 Soft computing
 Related study
 Problem solution
 Proposed solution
 Methodology
 Results, analysis, conclusion
 References

3. Introduction
DNA
DNA(deoxyribonucleic acid)
DNA is a hereditary materials,blue
print, recipe, genetic code.
DNA is a molecule , bunch of atoms
and these atoms stick together from a
spiral ladder[1].

4. DNA
 DNA process is complicated, sophisticated as magical.
 DNA structure is made of 4 different kinds of
nucleotides.
 Adenine
 guanine
 Thymine
 Cytosine
 A single strand of DNA is extremely large and millions
of ladder living inside the nucleus of the cell [1].

6. DNA CLASSFICATION
 DNA is classified on the basis of structure.
 Dna sequencing is the proper arrangement of
nucleotides in a DNA segments.
 many strategies are used to spot species using DNA
sequences, as well as population heritable information
and species particular sequence pattern.
 One of the classification method is shotgun.
 We use different kinds of approaches for classification
of DNA using soft computing techniques[2].

7. Soft computing
 The Soft Computing is taking part in a very important
role in science and engineering applications.
 Soft computing is that the combination of
methodologies that give the knowledge to handle the
important time things.
 Soft Computing is an umbrella term for a group of
computing techniques.
 Soft Computing is a promising approach to computing
the human mind to reason and learn in very
surroundings of ambiguity and impreciseness[2].

8. Soft computing techniques
Neural networks
Fuzzy logic sets
Genetic algorithms etc.

9. Neural networks
 An ANN is based on a collection of connected units called artificial
neurons (analogous to axons in a biological brain). Each connection axon
between neurons can transmit a signal to another neuron. The receiving
neuron can process the signal(s) and then signal downstream neurons
connected to it. Neurons may have state, generally represented by real
numbers, typically between 0 and 1. Typically, neurons are organized in
layers. Different layers may perform different kinds of transformations on
their inputs. Signals travel from the first (input), to the last (output) layer,
possibly after traversing the layers multiple times. In artificial networks with
multiple hidden layers, the initial layers might detect primitives (e.g. the
pupil in an eye, the iris, eyelashes, etc..) and their output is fed forward to
deeper layers who perform more abstract generalizations (e.g. eye,
mouth).... and so on until the final layers perform the complex object
recognition (e.g. face)[5].

13. Fuzzy Logic Approach
 Fuzzy logic provides an answer based mostly upon
uncertain, inaccurate, noisy, or lost input information.
Uncertainty is currently thought-about essential to
science and fuzzy logic may be a way to model and handle
it using linguistic communication. The Fuzzy ARTMAP
model, a machine learning methodology to organize the
protein string. The protein sequence is classified using
Fuzzy model.

14. Genetic algorithms
 The evolution usually starts from a population of randomly generated
individuals, and is an iterative process, with the population in each iteration
called a generation. In each generation, the fitness of every individual in the
population is evaluated; the fitness is usually the value of the objective
function in the optimization problem being solved. The more fit individuals
are stochastically selected from the current population, and each individual's
genome is modified (recombined and possibly randomly mutated) to form a
new generation. The new generation of candidate solutions is then used in
the next iteration of the algorithm. Commonly, the algorithm terminates
when either a maximum number of generations has been produced, or a
satisfactory fitness level has been reached for the population.

19. Related study
 Soft computing methodologies in bioinformatics RK Jena, MM Aqel, P
Srivastava… - European Journal of …, 2009 - zemris.fer.hr[4].
 Soft Computing Applications in Bioinformatics: A Succinct Study[3].
 Improving promoter prediction Improving promoter prediction for the
NNPP2. 2 algorithm: a case study using Escherichia
coli DNA sequences
 S Burden, YX Lin, R Zhang - Bioinformatics, 2004 -
academic.oup.com[4].

20. Proposed solution
 The unique advantage of soft computing is that helps to learn from
experimental procedure that helps for DNA classification.
 The major components of Soft Computing are Fuzzy Sets (FS), Artificial
Neural Networks (ANN).
 Genetic algorithms (GAs), Evolutionary Strategies (ES), Support Vector
Machines (SVM), Rough Sets (RS), Simulated Annealing (SA).
 Biological inspired Swarm Optimization (SO), Ant Colony Optimization
(ACO) and Tabu Search (TS).
 Soft Computing techniques are recognized as gorgeous options to the
standard, conventional hard computing methods[2].

21. Methodology
 This survey detects which methodology of soft computing are used
frequently together to solve the problems of Deoxyribonucleic acid
(DNA) sequencing.
 Neural Network based classifier enhanced for Non-linear and Noisy
information.
 Fuzzy ARTMAP based Classifier Concerned only about the physical
Structure.
 For unsupervised classification Genetic algorithms are appropriate
for DNA sequences.
 Genetic Algorithms appropriate for global optimizations.
 Now a day’s soft computing techniques like ACO, Artificial Bee
Colony (ABC), SO are more efficient and used in Bioinformatics[2].

22. Analysis
 DNA sequence classification is a significant problem in
computational biology.
 The DNA sequence is used to identify differences and
similarities between organisms within a species.
 The selection of attributes is primary criteria in DNA
classification.
 DNA sequence classification techniques involve for origin of
particular characteristics from the progressions.
 Different species have distinct genetic structure[2].

23. conclusion
 The different models that are accustomed classify the DNA sequence are
chosen with various to the problems.
 This paper specified the review of current analysis work involving soft
computing techniques to categorize the DNA sequences.
 It has been examined that analysis of knowledge type connected elements of
DNA sequence classification.
 Neural Network based classifier enhanced for Non-linear and Noisy
information.
 Fuzzy ARTMAP based Classifier Concerned only about the physical Structure.
 For unsupervised classification Genetic algorithms are appropriate for DNA
sequences.
 Genetic Algorithms appropriate for global optimizations[2].

24. References
 [1]. https://en.wikipedia.org/wiki/DNA
 [2].K. Bhargavi* and S. Jyothi .Indian Journal of Science and
Technology, Vol 9(47), DOI: 10.17485/ijst/2016/v9i47/89343,
December 2016.
 [3].International Conference on Technological Innovations in
Engineering (ICTIE-2016), At MIT, Pune, Volume: Vol 3, Issue
12, December 2016.
 [4].https://scholar.google.com.pk/scholar?q=related+study+
about+dna+in+soft+computing&hl=en&as_sdt=0&as_vis=1&
oi=scholart&sa=X&ved=0ahUKEwid3_fA-
JDXAhUaSo8KHRqnDVsQgQMIIzAA
 [5]. https://en.wikipedia.org/wiki/Artificial_neural_network