A Deep Neural Network for Antimicrobial Peptide Recognition With the widespread use of antibiotics, many bacteria have developed resistance. Antimicrobial peptides have broad applications in medicine because of their high antibacterial activity. In this paper, a neural network model is introduced to recognize and detect antimicrobial peptides. Our model consists of an embedded, convolutional, bidirectional LSTM, and full connection layers. The embedded layer is used to code different amino acid residues into different vectors. The convolutional layer and bidirectional LSTM extract peptide amino acid residue sequence information. The full connection layer maps the sequence information linearly to the interval from 0 to 1, as the peptide for the probability of antimicrobial peptides. Training and testing on several different datasets reveal that our model performs better than other proposed models.

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.