  We propose an in silico molecular associative memory model for pattern
learning, storage and denoising using Pairwise Markov Random Field (PMRF)
model. Our PMRF-based molecular associative memory model extracts locally
distributed features from the exposed examples, learns and stores the patterns
in the molecular associative memory and denoises the given noisy patterns via
DNA computation based operations. Thus, our computational molecular model
demonstrates the functionalities of content-addressability of human memory. Our
molecular simulation results show that the averaged mean squared error between
the learned and denoised patterns are low (< 0.014) up to 30% of noise.
