Download SimpleSOM.tar.gz |

Table 2 Format of WTSPCA and WTSSOM files

Table 3 Convergence.txt

X

where

Here, N means the number of input vectors.

The initial weight vectors are determined based on the first and second principal components of the M-dimensional space by PCA. Weights in the first dimension (I) are arranged into lattices corresponding to a width that is five times the standard deviation (5δ_{1}) of the first principal component. The second dimension (J) is defined by the nearest integer greater than (δ_{2}/δ_{1}) x I. The total number of weights in the first dimension I is set by a user. The weight vector on the ijth lattice (w_{ij}) is represented as follows:

The initial weight vectors are determined based on the first and second principal components of the M-dimensional space by PCA. Weights in the first dimension (I) are arranged into lattices corresponding to a width that is five times the standard deviation (5δ

Here x_{av} is the average vector for oligonucleotide frequencies of all input vectors, and b_{1} and b_{2} are eigenvectors for the first and second principal components.

##### Step 2: Adaptation of weight vectors to the input vectors.

The minimum Euclidean distance of the input vector x_{k} with respect to all weight vectors w_{ij} (i = 1,2,...,I; j = 1,2,...,J) is denoted by w_{i'j'}. The input vector x_{k} is classified into set *S*_{ij}for the lattice points (i, j) satisfying *i'*-*β*≤*i*≤*i'*+*β*(*r*) and *j'*-*β*≤*j*≤*j'*+*β*(*r*) . After classification of all input vectors to the lattice pointes (i, j), weight vectors are updated by

The two parameters α(r) and β(r) are learning coefficients for the *r*th cycle, and N_{ij} is the number of components of S_{ij}. α(r) and β(r) are calculated as follows:

α(r) = max {0.01, α(1)(1 - r/T)}

β(r) = max {0, β(1) - r}

β(r) = max {0, β(1) - r}

Here, α(1) and β(1) are the initial values for the T-cycle of the learning process. The learning process is monitored by the total distance between x_{k} and the nearest weight vector w_{i'j'}, represented as

##### Step 3: Classification of input vectors to weight vectors

Each of the input vectors is classified into lattice point whose distance is the minimum from the input vector.