DNA primer selection for PCR using a combination of artificial and neural intelligence techniques

The polymerase chain reaction (PCR) is a process for DNA amplification where short DNA sequences (primers) are used to provide the starting position for the amplification enzyme (DNA polymerase). However, biologists still rely on a pseudo-random choice of primers, making the procedure expensive yet uncertain. This thesis asserts that a combination of artificial intelligence (AI) and neural intelligence (NI) techniques can be useful to improve the primer selection process. Results show that the combined system (AI and NI) derives reduced sets of good primers from the original larger sets of several hundreds of pairs of possible primers that are deduced by AI rule-based system (RBS). The adaptive resonance theory (ART) neural network discovers leading primers using a follow-the-leader clustering algorithm.