Network Analysis of Antibiotic Resistance Identifies Compelling Mutations across Pathways The mechanism for the formation of antibiotic resistance is not clearly understood and has remained challenging due to the evolving nature of the bacterial genome. Staphylococcus aureus is a commensal of the human microbiota found in the upper and occasionally lower respiratory airways and adherent to adnexal regions. The S. aureus is responsible for several acute and chronic illnesses such as osteomyelitis, endocarditis, and infection from implanted devices. It can cause mild to lifethreatening infections. In addition to its potential for hostility, S. aureus demonstrates the exceptional diversity of resistance mechanisms against antimicrobial efforts. S. aureus resistance can be attributed to permanent mutations at the genetic level. SANVA (Staphylococcus aureus NetworkBased Variant Analysis) is an analytic method based on a network that can analyze the staphylococcal isolates to find the compelling mutations in ProteinProtein Interaction network. These mutated genes might be able to introduce new targets for antimicrobial drugs.

Identifying BetaLactam Resistance with Neural Networks Antibiotic resistant bacteria are an everpresent threat in todayu0027s society and matter of great concern for the medical and scientific communities. With the high frequency of mutations, and the ability to transfer genes across species, bacteria can acquire resistance to currently used antibiotics. Identifying resistance to antibiotics requires growing the bacteria in various antibiotic concentrations to determine the Minimum Inhibitory Concentration (MIC) value for that bacteriaantibiotic combination which is a longer than two day process. Additionally, the testing to evaluate genetic resistance determinants is cumbersome and demands considerable expertise. This paper looks at Neural Networks (NN) and decision trees as viable options to predict the resistance category for an antibiotic, bacteria combination based in the betalactamases present in that organism. We show that decision trees have just as high accuracy as NN, and provide more detail and incite in how a decision was made.