Title: Predicting Antimicrobial MICs for Nontyphoidal Salmonella Using Multitask Representations Learning . Abstract: The antimicrobial resistance (AMR) pathogens have become an increasingly worldwide issue, posing a significant threat to global public health. To obtain an optimized therapeutic effect, the antibiotic sensitivity is usually evaluated in a clinical setting, whereas traditional culture-dependent antimicrobial sensitivity tests are labor-intensive and relatively slow. Rapid methods can greatly optimize antimicrobial therapeutic strategies and improve patient outcomes by reducing the time it takes to test antibiotic sensitivity. The booming development of sequencing technology and machine learning techniques provide promising alternative approaches for antimicrobial resistance prediction based on sequencing. In this study, we used a lightweight Multitask Learning Transformer to predict the MIC of 14 antibiotics for Salmonella strains based on the genomic information, including point mutations, pan-genome structure, and the profile of antibiotic resistance genes from 5,278 publicly available whole genomes of nontyphoidal Salmonella. And we got better prediction results (improved more than 10% for raw accuracy and 3% for accuracy within ±1 2-fold dilution step) and provided better interpretability than the other ML models. Besides the potential clinical application, our models would cast light on mechanistic understanding of key genetic regions influencing AMR.