Sample classification using the GA/KNN algorithm with varying ...

Similarly if the p parameter equals 5, then the formula will be as follows:
Methods :
D = [ (g1k – g1j) 5 +…….+(gnk – gnj) 5 ] 1/5
Data: Golub data was obtained from BINF 733 website .The data was filtered and
transformed to log scale as shown below (taken from Li et al. 1 ).
The filtering was done by excluding genes whose expression levels were below 50 in
more than 57 out of the 72 samples. 5455 genes were left after the filtering which were
transformed to the log scale. The data set was tailored to act as an input for the GA/KNN
algorithm.
Usage of GA/KNN algorithm:
The GA/KNN source code was kindly provided by Dr. Leping Li .First, it was run on the
dataset without any modification. In that case the p parameter was equal to 2. Then the
code was modified by changing the distance calculation used while calculating the Knearest
neighbors. Then the modified source code was run for p parameter values 3, 4 ,5,
6 and 7. Higher p values increased the run time of the program and the program did not
successfully execute because of limited computing resources.
The program was run several times on unix server(mason.gmu.edu) to find out the
optimal parameters, taking into account the run time. The following parameters were
found best suited for the golub data set:
Chromosome length : 10 (higher values did not change the output)
Number of near optimal solutions : 5000(higher values did not change the output)
Termination fitness cutoff : 36
K( in KNN) 3
Number of training samples 38
Number of test samples 34
For a Detailed description of each of these parameters please refer to Li et al 1 .
The results from the algorithm were analyzed and the number of wrong classifications
were counted for each run(for each value of p parameter). Each run of the program
yielded a Gene ranking. 50 Top ranked genes were taken in each case and the dataset was
cut short from 5455 genes across 72 samples to 50 genes across 72 samples. This data
was now plotted in its first two principal components using MATLAB.