Unravelling the Architecture of Membrane Proteins with Conditional Random Fields

• URL: http://arxiv.org/abs/2008.02467v1
• Date: Thu, 6 Aug 2020 05:57:20 GMT
• Title: Unravelling the Architecture of Membrane Proteins with Conditional Random Fields
• Authors: Lior Lukov, Sanjay Chawla, Wei Liu, Brett Church, and Gaurav Pandey
• Abstract summary: We will show that the 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. A comparison on benchmark data sets against twenty-eight other methods shows that the CRF model leads to extremely accurate predictions.
• Score: 11.321552104966326
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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