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Proteins are distributed to the organelles of the
cell by a highly complex sorting machinery. Since
experimental localization techniques are time
consuming and expensive, various computational
techniques to predict the subcellular localization of
proteins have been developed. This book describes the
biological signals and mechanisms that guide protein
localization, and the computational methods employed
for localization prediction. The focus is thereby on
transmembrane proteins, an important class of
proteins that are inserted into the membranes of the
cell. Different topological models of transmembrane
proteins, utilizing Support Vector Machines, Hidden
Markov Models and Conditional Random Fields, are
studied and their prediction performances are
evaluated. The methods described in this book should
be of interest to all researchers working in the
field of protein localization prediction.
cell by a highly complex sorting machinery. Since
experimental localization techniques are time
consuming and expensive, various computational
techniques to predict the subcellular localization of
proteins have been developed. This book describes the
biological signals and mechanisms that guide protein
localization, and the computational methods employed
for localization prediction. The focus is thereby on
transmembrane proteins, an important class of
proteins that are inserted into the membranes of the
cell. Different topological models of transmembrane
proteins, utilizing Support Vector Machines, Hidden
Markov Models and Conditional Random Fields, are
studied and their prediction performances are
evaluated. The methods described in this book should
be of interest to all researchers working in the
field of protein localization prediction.