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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.
Cited literature [17 references]
https://hal.inria.fr/hal-01819542
Contributor : Sabeur Aridhi <>
Submitted on : Thursday, July 5, 2018 - 1:26:07 AM
Last modification on : Wednesday, February 24, 2021 - 4:24:02 PM
Long-term archiving on: : Monday, October 1, 2018 - 4:28:00 PM
Contributor : Sabeur Aridhi <>
Submitted on : Thursday, July 5, 2018 - 1:26:07 AM
Last modification on : Wednesday, February 24, 2021 - 4:24:02 PM
Long-term archiving on: : Monday, October 1, 2018 - 4:28:00 PM
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