  In this paper, we will show that the recently introduced graphical model:
Conditional Random Fields (CRF) provides a template to integrate micro-level
information about biological entities into a mathematical model to understand
their macro-level behavior. More specifically, we will apply the CRF model to
an important classification problem in protein science, namely the secondary
structure prediction of proteins based on the observed primary structure. A
comparison on benchmark data sets against twenty-eight other methods shows that
not only does the CRF model lead to extremely accurate predictions but the
modular nature of the model and the freedom to integrate disparate, overlapping
and non-independent sources of information, makes the model an extremely
versatile tool to potentially solve many other problems in bioinformatics.

Machine Learning;Machine Learning