hidden markov model matlab code for bioinformatics data

( 2007) further developed it for multichannel observations.

( 1999) extended the HMM to include individual covariates and Bartolucci et al. In the social science framework, Vermunt et al. In social sciences, such models are commonly referred to as latent Markov (chain) models (Wiggins 1955, 1973 Van de Pol and De Leeuw 1986) typically they have been used for analysing panel data with a few measurement points. 1998 Rabiner 1989), often to study single long sequences such as time series. Hidden Markov models have been widely used in economics, bioinformatics, and engineering (see, e.g., MacDonald and Zucchini 1997 Durbin et al. We illustrate this approach with an empirical application to complex longitudinal life course data but such an approach, and HMMs in general, are useful in various longitudinal problems across disciplines. With the help of HMMs the data can then be illustrated with a graph showing typical phases within trajectories and the transitions between them and/or shown as simplified (single-channel) trajectories consisting of these typical phases.

For the first task we use the standard multichannel sequence analysis approach and for the second task we propose using hidden Markov models (HMMs). We propose an approach for compressing the information within multichannel sequences and for facilitating the interpretation of such data by finding (1) groups of similar trajectories and (2) similar phases within trajectories belonging to the same group. Describing and visualizing results is, however, often challenging. This approach is simple and fast in computing dissimilarities between sequences, and cluster analysis is often used for grouping similar sequences. 2010) has been the standard tool for the analysis of multichannel sequence data (for empirical applications see, e.g., Eerola and Helske 2016 Müller et al. Multichannel sequence analysis (Gauthier et al. Joint analysis of complex multidimensional data poses several challenges. If some data is only partially observed, the multichannel approach also allows for handling data as it is instead of having to make difficult decisions on how to combine observed and unobserved states (Helske and Helske 2018). A multichannel approach often gives a simpler representation of the data as opposed to combining states across life domains (the extended alphabet approach) the latter approach rapidly grows the state space as the number of channels and/or states grows. Such data is often referred to as multichannel or multidimensional sequence data. For example, life course data may contain sequences of employment, family formation, and residence. Longitudinal data often consists of multiple parallel sequences that ought to be analyzed jointly.

Hidden markov model matlab code for bioinformatics data