MOTIVATION: Antibiotic resistance in Mycobacterium tuberculosis (MTB) poses a significant challenge to global public health. Rapid and accurate prediction of antibiotic resistance can inform treatment strategies and mitigate the spread of resistant strains. In this study, we present a novel approach leveraging large language models (LLMs) to predict antibiotic resistance in MTB (LLMTB). Our model is trained and evaluated on genomic data from 12 185 CRyPTIC isolates and their associated resistance profiles, utilizing natural language processing techniques to capture patterns and mutations linked to resistance. The model's architecture integrates state-of-the-art transformer-based LLMs, enabling the analysis of complex genomic sequences and the extraction of critical features relevant to antibiotic resistance. RESULTS: We evaluate our model's performance using a comprehensive dataset of MTB strains, demonstrating its ability to achieve high performance in predicting resistance to various antibiotics. Unlike traditional machine learning methods, fine-tuning or few-shot learning opens avenues for LLMs to adapt to new or emerging drugs, thereby reducing reliance on extensive data curation. Beyond predictive accuracy, LLMTB uncovers deeper biological insights, identifying critical genes, intergenic regions, and novel resistance mechanisms. This method marks a transformative shift in resistance prediction and offers significant potential for enhancing diagnostic capabilities and guiding personalized treatment plans, ultimately contributing to the global effort to combat tuberculosis and antibiotic resistance. AVAILABILITY AND IMPLEMENTATION: All source code is publicly available at https://github.com/ctestagrose/LLMTB.

Patronizing and Condescending Language (PCL) is the language used by an individual that denotes a superior attitude toward other individuals, especially those that are members of minority or marginalized groups. Due to the prevalence of patronizing and condescending language in today's society, there has been a focus on attempting to automate its detection and classification. In this study, we develop classifiers for this problem using the data from the previously concluded “SemEval 2022 Task 4: Patronizing and Condescending Language Detection” competition. We explore the implementation of three traditional machine learning algorithms and three transformer-based algorithms for both binary and multi-label PCL classification. Our results are in line with that of the original SemEval findings and help demonstrate the need for additional work. The development of a larger, more-balanced dataset would ensure more consistent and transferable results.

Breast density is an indicator of a patient’s predisposed risk of breast cancer. Although not fully understood, increased breast density increases the likelihood of developing breast cancer. Accurate assessment of breast density from mammogram images is a challenging task for the radiologist. A patient’s breast density is assigned to one of four categories outlined by Breast Imaging and Reporting Data Systems (BIRADS). There have been efforts to identify automated approaches to assist radiologists in the classification of a patient’s breast density. The interest in using deep learning to fulfill this need for an automated approach has seen a significant increase in recent years. The preprocessing techniques used to develop these deep learning approaches often have a profound impact on the model’s accuracy and clinical viability. In this paper, we outline a novel image preprocessing technique where we concatenate individual mammogram images and compare the results using this technique between Inception-v3 and a vision transformer (ViT). The results are compared using the area under (AUC) the receiver operator characteristics (ROC) curves and traditional accuracy metrics.