Unravelling the Architecture of Membrane Proteins with Conditional Random Fields
This addresses a key classification problem in protein science for bioinformatics researchers, offering a versatile tool for potential broader applications.
The paper tackles protein secondary structure prediction by applying Conditional Random Fields (CRF) to integrate micro-level biological information, achieving extremely accurate predictions compared to 28 other methods on benchmark datasets.
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.