In this paper, hidden Markov models (HMMs) are discussed in the context of molecular biological sequence analysis. The statistics relevant in the HMM approach are described in detail. An HMM based method is used to analyze two proteins that contain short protein repeats (SPRs). As a benchmark, a...

Evaluation of hazards associated with exposure to chemicals, understanding of relationships between dose and adverse effect, extrapolation of effects from high experimental doses to low doses associated with actual exposures, and extrapolation from effects observed in animals to effects expected...

This paper presents the application of special unsupervised neural networks (self-organizing maps) to different domains, as sleep apnea discovery, protein sequences analysis and tumor classification. An enhancement of the original algorithm, as well as the introduction of several hierachical...

The expanding availability of protein data enforces the application of empirical methods necessary to recognize protein structures. In this paper a sequence-structure alignment method is described and applied to various Ubiquitin-like folded Ras-binding domains. On the basis of two probability...

These report presents two methods for the identification of signal peptides and their cleavage sites. The first method is based on based neural networks and the second on hidden Markov models. The transmembrane protein topology can also be identified by a method based on hidden Markov models,...