Artificial Intelligence: Structures and Strategies for Complex Problem Solving (5th Edition)
Artificial Intelligence: Structures and Strategies for Complex Problem Solving (5th Edition)
Predicting protein localization in budding Yeast
Bioinformatics
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In a previous work, we built a classifier that used a decision tree to predict fungal protein localization based on physiochemical properties of proteins. 178 features selected from proteins compositional properties, functional motifs and signal sequences were studied for their effect on subcellular localization. That work resulted in a localizer that would successfully predict some of the reported localizations in 64% of the cases and all the reported localizations in 49% of the cases. Here, we improve on the results of the mentioned work by streamlining the classes of protein features used. Considering various modes of intra-cellular protein movement and the requirements for such transport, we establish a list of features that would have direct impact on the recognition of the proteins by the transport machinery of the cell. We shall detect the occurrence of such features in fungal proteins and use them as potential determinants of subcellular localization. The system rebuilt based on 980 of such features is validated using a 5-fold cross validation and results in a success rate of 87% for predicting some and 77% for predicting all the reported localization sites of 3 fungal species for which annotations on subcellular localization were available.