We used Markov Chains in order to differentiate amongst two categories, a positive one and a negative one. With Markov chains
we calculate the probability of a certain residue being in a certain position, given the preceding residue. Thus we create matrices
with which we predict whether a sequence is positive or negative depending on the training datasets we had initially provided.
For PredSL, we used several combinations of positive and negative sets, and therefore got a corresponding number of prediction scores, for each sequence.
The combinations we used are:
Sequence Type
Positive
Negative
Plant
chloroplast
cytopasmic
mitochondrial
cytoplasmic
chloroplast
mitochondrial
chloroplast & mitochondrial
cytoplasmic
chloroplast & mitochondrial
secreted
chloroplast
mitochondrial & cytoplasmic
Non-plant
mitochondrial
cytoplasmic
mitochondrial
secreted
mitochondrial
secreted & cytoplasmic
| Sequence Type | Positive | Negative |
| Plant | chloroplast | cytopasmic |
| mitochondrial | cytoplasmic | |
| chloroplast | mitochondrial | |
| chloroplast & mitochondrial | cytoplasmic | |
| chloroplast & mitochondrial | secreted | |
| chloroplast | mitochondrial & cytoplasmic | |
| Non-plant | mitochondrial | cytoplasmic |
| mitochondrial | secreted | |
| mitochondrial | secreted & cytoplasmic |