Unravelling the architecture of membrane proteins with conditional random fields

In this thesis we use Conditional Random Fields (CRFs) as a sequential classifier to predict the location of transmembrane helical regions in membrane proteins. CRFs allow for a seamless and principled integration of biological domain knowledge into the model and are known to have several advantages over other approaches. We have used this flexibility in order to incorporate several biologically inspired features into the model. We compared our approach with twenty eight other methods and received the highest score in the percentage of residues predicted correctly. We have also carried out experiments comparing CRFs against Maximum Entropy Models (MEMMs). Our results confirm that CRFs overcome the label bias problem, which are known to afflict MEMMs. Furthermore, we have used CRFs to analyze the architecture of the protein complex, Cytochrome c oxidase, and have recreated the results obtained from physical experiments

Unravelling the architecture of membrane proteins with conditional random fields

In this thesis we use Conditional Random Fields (CRFs) as a sequential classifier to predict the location of transmembrane helical regions in membrane proteins. CRFs allow for a seamless and principled integration of biological domain knowledge into the model and are known to have several advantages over other approaches. We have used this flexibility in order to incorporate several biologically inspired features into the model. We compared our approach with twenty eight other methods and received the highest score in the percentage of residues predicted correctly. We have also carried out experiments comparing CRFs against Maximum Entropy Models (MEMMs). Our results confirm that CRFs overcome the label bias problem, which are known to afflict MEMMs. Furthermore, we have used CRFs to analyze the architecture of the protein complex, Cytochrome c oxidase, and have recreated the results obtained from physical experiments

Unravelling the Architecture of Membrane Proteins with Conditional Random

In this thesis we use Conditional Random Fields (CRFs) as a sequential classi-fier to predict the location of transmembrane helical regions in membrane pro-teins. CRFs allow for a seamless and principled integration of biological domain knowledge into the model and are known to have several advantages over other ap-proaches. We have used this flexibility in order to incorporate several biologically inspired features into the model. We compared our approach with twenty eight other methods and received the highest score in the percentage of residues pre-dicted correctly. We have also carried out experiments comparing CRFs against Maximum Entropy Models (MEMMs). Our results confirm that CRFs overcome the label bias problem, which are known to afflict MEMMs. Furthermore, we have used CRFs to analyze the architecture of the protein complex, Cytochrome c oxidase, and have recreated the results obtained from physical experiments. i Acknowledgement