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

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

Outline
• Introduction
• Biological Background
• Genetic Algorithm
• Genetics Algorithm Paper discussion
• Conclusion

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

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.

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.

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

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

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/

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.

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.

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

Biology Background
• Random assortment of chromosomes: The partition of the members of a pair
of chromosomes is completely at random with many possible combinations.

Biology Background
Natural Selection Process
Source: BBC Biology Genetics: http://www.bbc.co.uk/bitesize/higher/biology/genetics_adaptation/natural_selection/revision/2/

Biology Background
Natural Selection Process
Source: Wikipedia, Evolution: http://en.wikipedia.org/wiki/Evolution

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

Genetic Algorithm
Nobal Niraula, Genetic Algorithms by Example http://www.slideshare.net/kancho/genetic-algorithm-by-example

Genetic algorithm process
Phases in the Genetic algorithm process.
Source: http://www.cs.ucdavis.edu/~vemuri

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

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.

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.

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.

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.

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.

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).

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.

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.

“…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)

“…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)

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.

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.

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

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