Identifying living organisms by using artificial neural networks approach / Raifiza Abdul Rahim

Identifying DNA sequences is very useful in forensic area. Currently, there
are a lot of computational biology approaches (bioinformatics) in solving the
molecular biology. The variation, complexity, and incompletely-understood
nature of sequences make it impractical to hand-code algorithm by applying
the human ability and laboratory equipments in identifying the sequences.
Artificial neural network (ANN), which is one of the commonly used machine
learning technique, might be preferable to form its own descriptions of genetic
concepts. Thus, it is applied in developing a prototype to identify the living
organism whether it is human (Malay or India) or non-human. A multi-layer
backpropagation algorithm of one hidden layer with 5 neurons was used. It is
the constant representation whereby it produces one output. The training set
was composed of 7 types of organisms from randomly selected DNA
nucleotide sequences. The result of this prototype shows that it successfully
can train the sequence of non-human. The reason it cannot train the human
sequence probably because the way of massaging the data. By using the
different sequences from the same types of organisms, the network
successfully can identify. The training epoch and time can be accelerated if
the network is included with the momentum.