Qualitative Hidden Markov Models for Classifying Gene Expression Data

Abstract

Hidden Markov Models (HMMs) have been successfully used in tasks involving prediction and recognition of patterns in sequence data, with applications in areas such as speech recognition and bioinformatics. While variations of traditional HMMs proved to be practical in applications where it is feasible to obtain the numerical probabilities required for the specification of the parameters of the model and the probabilities available are descriptive of the underlying uncertainty, the capabilities of HMMs remain unexplored in applications where this convenience is not available. Motivated by such applications, we present a HMM that uses qualitative probabilities instead of quantitative ones. More specifically, the HMM presented here captures the order of magnitude of the probabilities involved instead of numerical probability values. We analyze the resulting model by using it to perform classification tasks on gene expression data.