Genetic algorithm crossover mutation and multiple sequence

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Reference no: EM131173350 , Length: 2500 Words

Write a report on a topic: "Prediction of Baldness of Upcoming Generation using Genetic Algorithm Crossover Mutation and Multiple Sequence Alignment".

Human genetics is involved in identifying genes that lead to an increased risk of Genetic disease in certain individuals. The identification of disease susceptibility in the genes can help us to improve human health  through  the  development  of  new  prevention, diagnosis, and treatment strategies. In our proposed paper we are researching on baldness i.e. We are predicting that weather upcoming generation of given sequence would suffer from baldness or not. 

1. Introduction

2. Proposed Model

Steps involved in processing sequence of DNA:

1. Start

2. Initialization

3. Chromosome representation

4. Genetic operations

  • Selection
  • Crossover 
  • Selection for next generation
  • Mutation operation 
  • Calculate overall alignment fitness 
  • Discard the chromosomes
  • Result
  • End

3. Computation Model

  • Genetic basis of male pattern baldness
  • Arabidopsis Phenotypes

4.Multiple sequence alignment Using GA

  • Outline of Genetic Algorithm

5. Hereditary Testing

6. Conclusion

Academic requirements:

Your work must be submitted as  2500 words.

Your work should be submitted in the formats outlined for each point in the assignment

The entire assignment brief and completed front sheet must accompany your work.

Verified Expert

A multiple sequence alignment arranges protein sequences into a rectangular array with the goal that residues {in a|during a|in associate degree exceedingly|in a very} given column square measure homologous (derived from one position in an ancestral sequence), superposable (in a rigid native structural alignment) or play a common useful role. Although these 3 criteria square measure primarily equivalent for closely connected proteins, sequence, structure and function diverge over biological process time and totally different|completely different} criteria might end in different alignments. Manually refined alignments continue to be superior to purely automatic methods; there's thus an eternal effort to boost the biological accuracy of MSA tools. Additionally, the high computational value of most naive algorithms motivates enhancements in speed and memory usage

Reference no: EM131173350

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