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Computational phenotype prediction of ionizing-radiation-resistant bacteria with a multiple-instance learning model

Abstract : Ionizing-radiation-resistant bacteria (IRRB) are important in biotechnology. The use of these bacteria for the treatment of radioactive wastes is determined by their surprising capacity of adaptation to radionuclides and a variety of toxic molecules. In silico methods are unavailable for the purpose of phenotypic prediction and genotype-phenotype relationship discovery. We analyze basal DNA repair proteins of most known proteomes sequences of IRRB and ionizing-radiation-sensitive bacteria (IRSB) in order to learn a classifier that correctly predicts unseen bacteria. In this work, we formulate the problem of predicting IRRB as a multiple-instance learning (MIL) problem and we propose a novel approach for predicting IRRB. We use a local alignment technique to measure the similarity between protein sequences to predict ionizing-radiation-resistant bacteria. The first results are satisfactory and provide a MIL-based prediction system that predicts whether a bacterium belongs to IRRB or to IRSB. The proposed system is available online.
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Sabeur Aridhi, Haithem Sghaier, Mondher Maddouri, Engelbert Mephu Nguifo. Computational phenotype prediction of ionizing-radiation-resistant bacteria with a multiple-instance learning model. BioKDD '13 - Proceedings of the 12th International Workshop on Data Mining in Bioinformatics, Aug 2013, Chicago, United States. ⟨10.1145/2500863.2500866⟩. ⟨hal-01819542⟩

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