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- W2895521104 abstract "Many computational and experimental methods have been proposed for predicting functions performed by proteins. In silico methods are time and resource-consuming, due to the large amount of data used for annotation. Moreover, computational predictions for protein functions are usually incomplete and biased. Although some tools combine different annotation strategies to predict functions, biologists (human experts) have to use their knowledge to analyze and improve these predictions. This complex scenario presents suitable features for a multiagent approach, e.g., expert knowledge, distributed resources, and an environment that includes different computational methods. Also, argumentation theory can increase the expressiveness of biological knowledge of proteins, considering inconsistencies and incompleteness of information. The main goal of this work is to present an argumentation theory-based multiagent model to annotate proteins, called ArgMAS-AP. Additionally, we discuss a theoretical example with real data to evaluate the suitability of our model." @default.
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- W2895521104 date "2018-01-01" @default.
- W2895521104 modified "2023-09-27" @default.
- W2895521104 title "An Argumentation Theory-Based Multiagent Model to Annotate Proteins" @default.
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- W2895521104 doi "https://doi.org/10.1007/978-3-030-01722-4_7" @default.
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