Читать книгу Informatics and Machine Learning. From Martingales to Metaheuristics онлайн

27 страница из 101

ssss1 Chunking on a dynamic table. Works for a HMM using a simple join recovery.

The main application areas for HMMs covered in this book are power signal analysis generally, and bioinformatics and cheminformatics specifically (the main reviews and applications discussed are from [128–134]). For bioinformatics, we have information encoding molecules that are polymers, giving rise to sequential data format, thus HMMs are well suited for analysis. To begin to understand bioinformatics, however, we need to know not only the biological encoding rules, largely rediscovered on the basis of their statistical anomalies in ssss1–ssss1, but also the idiosyncratic structures seen (genomes and transcriptomes) that are full of evolutionary artifacts and similarities to evolutionary cousins. To know the nature of the statistical imprinting on the polymeric encodings also requires an understanding of the biochemical constraints that give rise to the statistical biases seen. Once taken altogether, bioinformatics offers a lot of clarity on why Nature has settled on the particular genomic “mess,” albeit with optimizations, that it has selectively arrived at. See [1, 3] for further discussion of bioinformatics.