Bioinformatics Literature Review
A Review of Genetic Algorithms
Lit Review Talk
by
Kato Mivule
COSC891 – Bioinformatics, S...
Outline
• Introduction
• Biological Background
• Genetic Algorithm
• Genetics Algorithm Paper discussion
• Conclusion
Bowi...
Sources
Information presented in these slides is adapted from the following sources:
1. Michael Skinner, Genetic Algorithm...
Genetic Algorithms (GA) - Introduction
• Genetic Algorithms (GA) were first developed by John Holland (1975).
• GA is a se...
Genetic Algorithms (GA) – Introduction
• GA works well with a very large set of candidate solutions.
• GA are outperformed...
Genetic Algorithms (GA) – Introduction
• GA use the process of natural selection and evolution.
• “…Some birds developed l...
Biology background
• The Body is made up of cells. The cell has a center called a nucleus. The
nucleus contains the chromo...
Biology background
• The Body is made up of cells. The cell has a center called a nucleus. The
nucleus contains the chromo...
Biology background
• The chromosome is composed of firmly coiled strings of deoxyribonucleic acid
(DNA). Genes are section...
Biology background
• DNA: A molecule of DNA is made up of two strands called the double helix.
The DNA Strand contains fou...
Biology background
• Genes and Proteins: The genetic information coded into DNA in the genes
gives the cells instructions ...
Biology Background
• Random assortment of chromosomes: The partition of the members of a pair
of chromosomes is completely...
Biology Background
Natural Selection Process
Source: BBC Biology Genetics: http://www.bbc.co.uk/bitesize/higher/biology/ge...
Biology Background
Natural Selection Process
Source: Wikipedia, Evolution: http://en.wikipedia.org/wiki/Evolution
Bowie St...
Genetic Algorithm Pseudo-code
Generate an initial population of individuals
Evaluate the fitness of all individuals
while ...
Genetic Algorithm
Bowie State University Department of Computer Science
Bioinformatics Literature Review
Nobal Niraula, Ge...
Genetic algorithm process
Bowie State University Department of Computer Science
Bioinformatics Literature Review
Phases in...
Genetic Algorithm (GA)
•Initial Population: GA starts by generating a random initial population
•Creating the Next Generat...
Paper Review: C. H. Ooi and P. Tan, “Genetic algorithms applied to multi-class prediction
for the analysis of gene express...
Paper Review: C. H. Ooi and P. Tan, “Genetic algorithms applied to multi-class prediction
for the analysis of gene express...
Paper Review: C. H. Ooi and P. Tan, “Genetic algorithms applied to multi-class prediction
for the analysis of gene express...
Paper Review: C. H. Ooi and P. Tan, “Genetic algorithms applied to multi-class prediction
for the analysis of gene express...
Paper Review: C. H. Ooi and P. Tan, “Genetic algorithms applied to multi-class prediction
for the analysis of gene express...
Paper Review: C. H. Ooi and P. Tan, “Genetic algorithms applied to multi-class prediction
for the analysis of gene express...
Paper Review: C. H. Ooi and P. Tan, “Genetic algorithms applied to multi-class prediction
for the analysis of gene express...
Paper: C. H. Ooi and P. Tan, “Genetic algorithms applied to multi-class prediction for the
analysis of gene expression dat...
Paper: C. H. Ooi and P. Tan, “Genetic algorithms applied to multi-class prediction for the
analysis of gene expression dat...
Paper: C. H. Ooi and P. Tan, “Genetic algorithms applied to multi-class prediction for the
analysis of gene expression dat...
Paper: C. H. Ooi and P. Tan, “Genetic algorithms applied to multi-class prediction for the
analysis of gene expression dat...
Conclusion
• Genetic algorithms tend to get outdone by more situation specific algorithms
in the simpler search spaces.
• ...
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Lit Review Talk by Kato Mivule: A Review of Genetic Algorithms

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Lit Review Talk by Kato Mivule: A Review of Genetic Algorithms and Paper Review: C. H. Ooi and P. Tan, “Genetic algorithms applied to multi-class prediction for the analysis of gene expression data,” Bioinformatics, vol. 19, no. 1, pp. 37–44, 2003.

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Lit Review Talk by Kato Mivule: A Review of Genetic Algorithms

  1. 1. Bioinformatics Literature Review A Review of Genetic Algorithms Lit Review Talk by Kato Mivule COSC891 – Bioinformatics, Spring 2014 Bowie State University Bowie State University Department of Computer Science
  2. 2. Outline • Introduction • Biological Background • Genetic Algorithm • Genetics Algorithm Paper discussion • Conclusion Bowie State University Department of Computer Science Bioinformatics Literature Review
  3. 3. Sources Information presented in these slides is adapted from the following sources: 1. Michael Skinner, Genetic Algorithms Overview, http://geneticalgorithms.ai-depot.com/Tutorial/Overview.html , accessed online, March 2nd 2014. 2. Genetic Algorithms, Lecture Notes UC Davis Computer Science Dept, http://www.cs.ucdavis.edu/~vemuri/classes/ecs271/Genetic%20Algorithms%20Short%20Tutorial.htm 3. Wikipedia, Genetic algorithm, http://en.wikipedia.org/wiki/Genetic_algorithm 4. Nobal Niraula, Genetic Algorithms by Example http://www.slideshare.net/kancho/genetic-algorithm-by-example 5. BBC Genetics: http://www.bbc.co.uk/bitesize/intermediate2/biology/environmental_and_genetics/factors_affecting_variation_spec ies/revision/6/ 6. Deoxyribonucleic Acid (DNA), https://www.genome.gov/25520880#al-3 7. MATLAB, How the Genetic Algorithm Works, http://www.mathworks.com/help/gads/how-the-genetic-algorithm- works.html Bowie State University Department of Computer Science Bioinformatics Literature Review
  4. 4. Genetic Algorithms (GA) - Introduction • Genetic Algorithms (GA) were first developed by John Holland (1975). • GA is a search heuristic that mimics the process of natural evolution. • GA uses Darwin's concepts of “Natural Selection” and “Genetic Inheritance”. • GA are used to solve problems with little information about those problems. • GA are Generalized to work in any search space. • GA use selection and evolution to generate numerous solutions to a problem. Bowie State University Department of Computer Science Bioinformatics Literature Review
  5. 5. Genetic Algorithms (GA) – Introduction • GA works well with a very large set of candidate solutions. • GA are outperformed by more situation specific algorithms in the simpler search spaces. • GA are not always the best choice, their time run is long. • GA are good at creating high quality solutions to a problem. Bowie State University Department of Computer Science Bioinformatics Literature Review
  6. 6. Genetic Algorithms (GA) – Introduction • GA use the process of natural selection and evolution. • “…Some birds developed large, strong beaks suited to cracking nuts, others long, narrow beaks more suitable for digging bugs out of wood. The birds that had these characteristics when blown to the island survived longer than other birds. This allowed them to reproduce more and therefore have more offspring that also had this unique characteristic. Those without the characteristic gradually died out from starvation. Eventually all of the birds had a type of beak that helped it survive on its island. The individuals themselves do not change, but those that survive better, or have a higher fitness, will survive longer and produce more offspring. This continues to happen, with the individuals becoming more suited to their environment every generation. It was this continuous improvement that inspired computer scientists, one of the most prominent being John Holland, to create genetic algorithms…” Genetic Algorithms Overview, Michael Skinner Bowie State University Department of Computer Science Bioinformatics Literature Review
  7. 7. Biology background • The Body is made up of cells. The cell has a center called a nucleus. The nucleus contains the chromosomes. The chromosome is composed of firmly coiled strings of deoxyribonucleic acid (DNA). • Genes are sections of DNA that determine particular traits, like eye and skin color. You have more than 20,000 genes. A gene mutation is an modification in DNA. Some changes in your genes result in genetic disorders. Source: http://www.riversideonline.com/health_reference/Tools/DS00549.cfm Bowie State University Department of Computer Science Bioinformatics Literature Review
  8. 8. Biology background • The Body is made up of cells. The cell has a center called a nucleus. The nucleus contains the chromosomes. The chromosomes contain the DNA strand. Source: BBC Genetics: http://www.bbc.co.uk/bitesize/intermediate2/biology/environmental_and_genetics/factors_affecting_variation_species/revision/6/ Bowie State University Department of Computer Science Bioinformatics Literature Review
  9. 9. Biology background • The chromosome is composed of firmly coiled strings of deoxyribonucleic acid (DNA). Genes are sections of DNA that determine particular traits, like eye and skin color. Source: BBC Genetics: http://www.bbc.co.uk/bitesize/intermediate2/biology/environmental_and_genetics/factors_affecting_variation_species/revision/6/ Bowie State University Department of Computer Science Bioinformatics Literature Review
  10. 10. Biology background • DNA: A molecule of DNA is made up of two strands called the double helix. The DNA Strand contains four types of molecules, Adenine (A), Thymine (T), Guanine (G) and Cytosine (C). The molecules are held together by weak hydrogen bonds. Adenine pairs with Thymine. Guanine pairs with Cytosine. • A section of this DNA is called a gene. It is normally hundreds or thousands of DNA bases long. Source: BBC Genetics: http://www.bbc.co.uk/bitesize/intermediate2/biology/environmental_and_genetics/factors_affecting_variation_species/revision/6/ Bowie State University Department of Computer Science Bioinformatics Literature Review
  11. 11. Biology background • Genes and Proteins: The genetic information coded into DNA in the genes gives the cells instructions to make many specific protein molecules • Proteins are built using amino acid molecules. The order of the DNA bases is code for the order of amino acids in the protein Source: BBC Genetics: http://www.bbc.co.uk/bitesize/intermediate2/biology/environmental_and_genetics/factors_affecting_variation_species/revision/6/ Bowie State University Department of Computer Science Bioinformatics Literature Review
  12. 12. Biology Background • Random assortment of chromosomes: The partition of the members of a pair of chromosomes is completely at random with many possible combinations. Source: BBC Genetics: http://www.bbc.co.uk/bitesize/intermediate2/biology/environmental_and_genetics/factors_affecting_variation_species/revision/6/ Bowie State University Department of Computer Science Bioinformatics Literature Review
  13. 13. Biology Background Natural Selection Process Source: BBC Biology Genetics: http://www.bbc.co.uk/bitesize/higher/biology/genetics_adaptation/natural_selection/revision/2/ Bowie State University Department of Computer Science Bioinformatics Literature Review
  14. 14. Biology Background Natural Selection Process Source: Wikipedia, Evolution: http://en.wikipedia.org/wiki/Evolution Bowie State University Department of Computer Science Bioinformatics Literature Review
  15. 15. Genetic Algorithm Pseudo-code Generate an initial population of individuals Evaluate the fitness of all individuals while termination condition not met do Select fitter individuals for reproduction Recombine between individuals Mutate individuals Evaluate the fitness of the modified individuals Generate a new population End while Source: Nobal Niraula, Genetic Algorithms by Example http://www.slideshare.net/kancho/genetic-algorithm-by-example Bowie State University Department of Computer Science Bioinformatics Literature Review
  16. 16. Genetic Algorithm Bowie State University Department of Computer Science Bioinformatics Literature Review Nobal Niraula, Genetic Algorithms by Example http://www.slideshare.net/kancho/genetic-algorithm-by-example
  17. 17. Genetic algorithm process Bowie State University Department of Computer Science Bioinformatics Literature Review Phases in the Genetic algorithm process. Source: http://www.cs.ucdavis.edu/~vemuri
  18. 18. Genetic Algorithm (GA) •Initial Population: GA starts by generating a random initial population •Creating the Next Generation: children are created from the current initial population •GA generates three types of children for the next generation: •Elite children: individuals with the best fitness values who survive. •Crossover children: combining the vectors of a pair of parents. •Mutation children: introducing random changes to a single parent. •Stopping Conditions for the Algorithm •The algorithm stops when the value of the fitness criteria is met. Source: MATLAB How the Genetic Algorithm Works, http://www.mathworks.com/help/gads/how-the-genetic-algorithm-works.html Bowie State University Department of Computer Science Bioinformatics Literature Review
  19. 19. Paper Review: C. H. Ooi and P. Tan, “Genetic algorithms applied to multi-class prediction for the analysis of gene expression data,” Bioinformatics, vol. 19, no. 1, pp. 37–44, 2003. The Problem: •The expression dataset being analyzed involves multiple classes. •The efficient selection of good predictive gene groups from datasets that are inherently ‘noisy’. •The development of new methodologies that can enhance the successful classification of these complex datasets. Bowie State University Department of Computer Science Bioinformatics Literature Review
  20. 20. Paper Review: C. H. Ooi and P. Tan, “Genetic algorithms applied to multi-class prediction for the analysis of gene expression data,” Bioinformatics, vol. 19, no. 1, pp. 37–44, 2003. Methods: • GA is applied to the problem of multi-class prediction. •A GA-based gene selection scheme is employed to automatically •Determine the members of a predictive gene group •Determine the optimal group size •Determine the classification success using a maximum likelihood (MLHD) classification method. Bowie State University Department of Computer Science Bioinformatics Literature Review
  21. 21. Paper Review: C. H. Ooi and P. Tan, “Genetic algorithms applied to multi-class prediction for the analysis of gene expression data,” Bioinformatics, vol. 19, no. 1, pp. 37–44, 2003. Results: •The Authors state that GA/MLHD-based approach achieves higher classification accuracies than other published predictive methods on the same multi-class test dataset. •The Authors claim that GA/MLHD also permits substantial feature reduction in classifier gene sets without compromising predictive accuracy. Bowie State University Department of Computer Science Bioinformatics Literature Review
  22. 22. Paper Review: C. H. Ooi and P. Tan, “Genetic algorithms applied to multi-class prediction for the analysis of gene expression data,” Bioinformatics, vol. 19, no. 1, pp. 37–44, 2003. Dataset and Data Preprocessing •Authors used the NCI60 gene expression dataset contains the gene expression profiles of 64 cancer cell lines as measured by cDNA microarrays containing 9703 spotted cDNA sequences. •Authors downloaded data from http://genome- www.stanford.edu/sutech/download/nci60/dross arrays nci60.tgz. •Authors during data preprocessing, excluded spots with missing data, control, and empty leaving 6167 genes. Bowie State University Department of Computer Science Bioinformatics Literature Review
  23. 23. Paper Review: C. H. Ooi and P. Tan, “Genetic algorithms applied to multi-class prediction for the analysis of gene expression data,” Bioinformatics, vol. 19, no. 1, pp. 37–44, 2003. Overall Methodology The GA/MLHD classification strategy consists of two main components: (1) a GA-based gene selector (2) a maximum likelihood (MLHD) classifier. •The actual classification process is performed using the maximum likelihood (MLHD) classifier. •Each individual in the population thus represents a specific gene predictor subset •A fitness function is used to determine the classification accuracy of a predictor set. Bowie State University Department of Computer Science Bioinformatics Literature Review
  24. 24. Paper Review: C. H. Ooi and P. Tan, “Genetic algorithms applied to multi-class prediction for the analysis of gene expression data,” Bioinformatics, vol. 19, no. 1, pp. 37–44, 2003. System and Methods •Initialization and Evaluation: An initial population is formed by creating N random strings, where the population size N is pre-specified •Selection, Crossover and Mutation: Two selection methods were used to select the strings for the mating pool: (i) stochastic universal sampling (SUS) and (ii) roulette wheel selection (RWS). Bowie State University Department of Computer Science Bioinformatics Literature Review
  25. 25. Paper Review: C. H. Ooi and P. Tan, “Genetic algorithms applied to multi-class prediction for the analysis of gene expression data,” Bioinformatics, vol. 19, no. 1, pp. 37–44, 2003. System and Methods •Crossovers: performed by randomly choosing a pair of strings from the mating pool and then applying a crossover operation on the selected string pair. •Uniform mutation: operations applied at probability p(m) on each of the offspring strings produced from crossover. •Termination :evaluation, selection, crossover and mating are repeated for G generations until the string with the best fitness of all generations is outputted as the solution. Bowie State University Department of Computer Science Bioinformatics Literature Review
  26. 26. Paper: C. H. Ooi and P. Tan, “Genetic algorithms applied to multi-class prediction for the analysis of gene expression data,” Bioinformatics, vol. 19, no. 1, pp. 37–44, 2003. A maximum likelihood (MLHD) classifier •To build an MLHD classifier (James, 1985), a total of M(t) tumor samples are used as training samples. The remaining M(θ) tumor samples are used as test samples. •For the NCI60 dataset, the ratio between M(t) and M(θ) is 2:1. •Discriminant Function: The basis of the discriminant function is Bayes’ rule of maximum likelihood: Assign the sample to the class with the highest conditional probability. Bowie State University Department of Computer Science Bioinformatics Literature Review
  27. 27. Paper: C. H. Ooi and P. Tan, “Genetic algorithms applied to multi-class prediction for the analysis of gene expression data,” Bioinformatics, vol. 19, no. 1, pp. 37–44, 2003. “…Comparing GA-based Predictor Sets to Predictor Sets Obtained from Other Methodologies The best predictor set obtained using the GA-based selection scheme exhibited a cross validation error rate of 14.63% and an independent test error rate of 5% (Table 1, row 1, and see Supplementary Information for specific misclassifications). This is an improvement in accuracy as compared to other methodologies assessed by Dudoit et al. (2000), where the lowest independent test error rate was reported as 19%...” Ooi and Tan (2003) Bowie State University Department of Computer Science Bioinformatics Literature Review
  28. 28. Paper: C. H. Ooi and P. Tan, “Genetic algorithms applied to multi-class prediction for the analysis of gene expression data,” Bioinformatics, vol. 19, no. 1, pp. 37–44, 2003. “…Comparison of expression profiles of predictor sets obtained through different methodologies. Columns represent different class distinctions, and only training set samples are depicted. (a) Expression profile of genes selected through the GA/MLHD method (only genes for the best predictor set are shown). (b) Expression profile of 20 genes selected through the BSS/WSS ratio ranking method. (c) Expression profile of 18 genes selected through the OVA/S2N ratio ranking method. Arrows depict genes which have highly correlated expression patterns across the sample classes. Classes are labeled as follows: BR (breast), CN (central nervous system), CL (colon), LE (leukemia), ME (melanoma), NS (non-small-cell lung carcinoma), OV (ovarian), RE (renal) and PR (reproductive system)…” Ooi and Tan (2003) Bowie State University Department of Computer Science Bioinformatics Literature Review
  29. 29. Paper: C. H. Ooi and P. Tan, “Genetic algorithms applied to multi-class prediction for the analysis of gene expression data,” Bioinformatics, vol. 19, no. 1, pp. 37–44, 2003. Conclusion •The authors state that their report shows that highly accurate classification results can be obtained using a combination of GA-based gene selection and discriminant-based classification methods. •The authors note that accuracy achieved (95% for NCI60) is better than other published methods employing the same dataset. •The authors note that other advantages of the GA-based approach are that it automatically determines the optimal predictor set size and the delivery of predictive accuracies that are comparable to other methods. Bowie State University Department of Computer Science Bioinformatics Literature Review
  30. 30. Conclusion • Genetic algorithms tend to get outdone by more situation specific algorithms in the simpler search spaces. • Genetic algorithms are not always the best choice, their time run is long. • Genetic algorithms are good at creating high quality solutions to a problem. Bowie State University Department of Computer Science Bioinformatics Literature Review

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