Antimicrobial resistance (AMR) is a growing global health challenge that necessitates accurate computational methods for predicting resistant phenotypes from genomic data. In this study, we evaluate the performance of traditional machine learning (ML) models-Logistic Regression (LR), Random Forest (RF), Support Vector Machine (SVM), and kNearest Neighbors (k-NN)—alongside deep learning (DL) approaches, including Multilayer Perceptron (MLP), Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN). We also introduce TabTransformer as an additional model for AMR classification. Our models leverage k-mer-based features, resistance gene presence/absence, and SNP data across three feature sets. Our results show that LR, RF, and SVM consistently achieved an over 90% accuracy, while k -NN underperformed (77.78 %) due to sensitivity to high-dimensional feature spaces. Among deep learning models, RNN demonstrated superior performance in high-dimensional settings (97.22 % accuracy on 1024 features), while CNN and MLP experienced performance declines. TabTransformer also exhibited robust accuracy across different feature sets, outperforming CNN and MLP. Increasing the number of selected features beyond 256 did not significantly improve performance, emphasizing the importance of feature selection. Future work will explore nucleotide models and further integration of biological metadata to enhance model interpretability and prediction accuracy.

MOTIVATION: Tuberculosis (TB), caused by Mycobacterium tuberculosis (Mtb), still claims around 1.25 million lives each year. The growing threat of drug resistance-often driven by single‑nucleotide polymorphisms (SNPs) in Mtb genomes underscores the need for high‑quality genomic data and powerful bioinformatics tools. We present FuNTB, a python‑based pipeline that detects non‑synonymous SNPs in Mtb and builds functional network clusters to reveal genotype-phenotype relationships. RESULTS: FuNTB profiles non‑synonymous SNPs at the gene level across user‑defined phenotypes, pinpointing both shared and unique mutations. It ingests annotated Variant Call Format (VCF) files or MTBseq outputs and merges them with clinical metadata to produce network‑XML files compatible with Cytoscape and Gephi. When applied to the CRyPTIC Mtb collection, FuNTB rapidly recovered established resistance genes and surfaced novel candidates, validating its utility for mapping genotype-phenotype associations. AVAILABILITY AND IMPLEMENTATION: FuNTB is implemented in Python 3.8+ and is freely available under the MIT license at https://doi.org/10.5281/zenodo.15399917.