Abstract
In this chapter, we present a case study of performing visual analytics to the protein disorder prediction problem. Protein disorder is one of the most important characteristics in understanding many biological functions and interactions. Due to the high cost to perform lab experiments, machine learning algorithms such as neural networks and support vector machines have been used for its identification. Rather than applying these generic methods, we show in this chapter that more insights can be found using visual analytics. Visualizations using linear discriminant analysis reveal that the disorder within each protein is usually well separated linearly. However, if various proteins are integrated together, there does not exist a clear linear separation rule in general. Based on this observation, we perform another visualization on the linear discriminant vector for each protein and confirm that the proteins are clearly clustered into several groups. Inspired by such findings, we apply k-means clustering on the proteins and construct a different classifier on each group, which leads us to a significant improvement of disorder prediction performance. Moreover, within the identified protein subgroups, the separation accuracy topped 99 %, a clear indicator for further biological investigations on these subgroups.
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This research is partially supported by NSF grant CCF-0808863. Any opinions, findings and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation.
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Choo, J., Li, F., Joo, K., Park, H. (2012). A Visual Analytics Approach for Protein Disorder Prediction. In: Dill, J., Earnshaw, R., Kasik, D., Vince, J., Wong, P. (eds) Expanding the Frontiers of Visual Analytics and Visualization. Springer, London. https://doi.org/10.1007/978-1-4471-2804-5_10
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DOI: https://doi.org/10.1007/978-1-4471-2804-5_10
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