GA by Prashant & Vivek

8/3/2019 GA by Prashant & Vivek

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New Approach for Classification Of

Protein By Genetic Algorithm

By

Prashant Makwana And

Vivek Soni

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Introductiony WhyWe need Classification?

Huge amount of Protein Data and still increasing

To know Structure And Function

Database Management(DBMS) purpose

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What is GA?yA class of probabilistic optimization algorithms

y Inspired by the biological evolution process

y Uses concepts ofNatural Selection and Genetic

Inheritance

(Darwin 1859)

y Originally developed by John Holland (1975)

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Conty Definition:

class of Evolutionary Algorithm(EA), which generate

useful solutions to optimization problems usingtechniques inspired by natural evolution, such asinheritance, mutation, selection, and crossover.

y Genetic algorithms find application in bioinformatics,phylogenetics, computational science, economics,manufacturing, mathematics, physics and other fields.

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Classes of Search TechniquesSearch Techniques

Calculus BaseTechniques

Guided random searchtechniques

EnumerativeTechniques

BFSDFS DynamicProgramming

Tabu Search Hill

Climbing

Simulated

Annealing

EvolutionaryAlgorithms

GeneticProgramming

GeneticAlgorithms

Fibonacci Sort

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GA Overview

y GA terms

Population of string(Chromosomes/genotypes ofgenome)

Fitness function

y Initialization

y Selection

y Reproduction

y Termination

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Cont..y Initialization

Random process of selecting population/chromosome

y Selection

a proportion of the existing population is selected to breeda new generation

Based on Fitness Function/Fitness

y Reproduction

The next step is to generate a second generation populationof solutions from those selected through genetic operators:Crossover (also called recombination), and/or Mutation.

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Conty Termination

This generational process is repeated until a termination

condition has been reached. Common terminatingconditions are:1. A solution is found that satisfies minimum criteria2. Fixed number of generations reached3. Allocated budget (computation time/money) reached

4. The highest ranking solution's fitness is reaching or hasreached a plateau such that successive iterations nolonger produce better results

5. Manual inspection6. Combinations of the above

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GA procedure1. Choose the initial population of individuals

2. Evaluate the fitness of each individual in thatpopulation

3. Repeat on this generation until termination (timelimit, sufficient fitness achieved, etc.):

y Select the best-fit individuals for reproduction

y Breed new individuals through crossover and mutationoperations to give birth to offspring

y Evaluate the individual fitness of new individuals

y Replace least-fit population with new individuals9


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Our Worky Protein

Olfactory receptor

y OrganismHuman

y Database

Uniprot

y Datasets

5 sets each of 50 fasta sequences

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Cont1. Find Conserved Domains for each protein sequence

using prosite

2. Multiple sequence alignment(MSA) for each set3. Develop PSSM matrix for each set

4. Use GA operators: Mutation & Crossover

5. Generate new sets by GA operators

6. Calculate Determinant for each set7. Calculate fitness for each set

8. Repeat steps 4,5,6,7 till termination

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new set sequencefitness

Conty Final constant values(Fitness) which then compared

with a new set of sequence fitness to check for family

criteriaSet 1

Set 2

Set 3

Set n

Final Constant Values range for

given protein family

New sequencebelongs to givenprotein family

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Reason for each stepsy Use of Prosite

to determine conserved domain for given sequence

Conserved domain selected as a parameter forclassification

y Use of PSSM

To obtain numerical values for given sets, whichfurther used for GA

y Use of mutation & crossover

to check for maximum possible chances of variationsfor given sets

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Why GA/Advantage of GA?y It solves problems with multiple solutions.

y Genetic algorithm is a method which is very easy tounderstand and it practically does not demand theknowledge of mathematics.

y Genetic algorithms are easily transferred to existingsimulations and models.

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Disadvantagey Certain optimization problems cannot be solved by

means of genetic algorithms. This occurs due to poorly

known fitness functions .

yAnother drawback that GAs require large number ofresponse (fitness) function evaluations depending on

the number of individuals and the number ofgenerations.

y GA is usually slower than traditional techniques.15


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Future of GA in Bioinformaticsy Future approach for our work will be to consider more

parameter for protein classification .

Protein structure prediction by combining GA withother algorithm

y Immune system models: GAs have been used to modelvarious aspects of the natural immune system,including somatic mutation during an individualslifetime and the discovery of multi-gene familiesduring evolutionary time.

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Conty Ecological models: GAs have been used to model

ecological phenomena such as host-parasite co-evolutions, symbiosis.

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Conclusiony

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Referencey jin-xiong : Essential bioinformatics

y http://en.wikipedia.org/wiki/Genetic_algorithm

y http://www.informatics.indiana.edu/fil/CAS/PPT/Davis/sld001.htm

y http://fasta.bioch.virginia.edu/fasta_www2/chaps.cgi

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Uniprot(Oflactory Receptor)

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CHAPS

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PSSM

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