Prediction of Ionizing Radiation Resistance in Bacteria Using a Multiple Instance Learning Model.
Ionizing-radiation-resistant bacteria (IRRB) are important in biotechnology. In this context, in silico methods of phenotypic prediction and genotype-phenotype relationship discovery are limited. In this work, we analyzed basal DNA repair proteins of most known proteome sequences of IRRB and ionizing-radiation-sensitive bacteria (IRSB) in order to learn a classifier that correctly predicts this bacterial phenotype. We formulated the problem of predicting bacterial ionizing radiation resistance (IRR) as a multiple-instance learning (MIL) problem, and we proposed a novel approach for this purpose. We provide a MIL-based prediction system that classifies a bacterium to either IRRB or IRSB. The experimental results of the proposed system are satisfactory with 91.5% of successful predictions.
1 Laboratoire d'Informatique, de Modélisation et d'Optimisation des Systèmes (LIMOS)-Université Blaise Pascal (UBP) , Clermont Ferrand, France .|2 Centre National de Recherche Scientifique (CNRS) , UMR 6158, LIMOS, Aubière, France .|3 University of Tunis El Manar , Faculty of Sciences of Tunis, LIPAH, Tunis, Tunisia .
Sabeur
S
Aridhi
4 National Center for Nuclear Sciences and Technology (CNSTN) , Sidi Thabet Technopark, Ariana, Tunisia .|5 Laboratory BVBGR, ISBST, University of Manouba , La Manouba, Tunisia .
Haïtham
H
Sghaier
1 Laboratoire d'Informatique, de Modélisation et d'Optimisation des Systèmes (LIMOS)-Université Blaise Pascal (UBP) , Clermont Ferrand, France .|2 Centre National de Recherche Scientifique (CNRS) , UMR 6158, LIMOS, Aubière, France .|3 University of Tunis El Manar , Faculty of Sciences of Tunis, LIPAH, Tunis, Tunisia .
Manel
M
Zoghlami
3 University of Tunis El Manar , Faculty of Sciences of Tunis, LIPAH, Tunis, Tunisia .|6 Taibah University , Medinah, Saudi Arabia .
Mondher
M
Maddouri
1 Laboratoire d'Informatique, de Modélisation et d'Optimisation des Systèmes (LIMOS)-Université Blaise Pascal (UBP) , Clermont Ferrand, France .|2 Centre National de Recherche Scientifique (CNRS) , UMR 6158, LIMOS, Aubière, France .
Engelbert Mephu
EM
Nguifo
D000465:Algorithms; D001185:Artificial Intelligence; D001419:Bacteria / Q000235:genetics* / Q000528:radiation effects*; D001426:Bacterial Proteins / Q000235:genetics; D004260:DNA Repair; D004269:DNA, Bacterial / Q000235:genetics; D016678:Genome; D008954:Models, Biological; D010641:Phenotype; D011839:Radiation, Ionizing
D001426:Bacterial Proteins; D004269:DNA, Bacterial
bacterial ionizing radiation resistance; multiple instance learning; prediction