Rapid and Interpretable AMR Diagnostics via Genomics and Cell Painting using Differential Geometry-based Directed-Simplicial Neural Networks on Multimodal Data

Abstract Antimicrobial resistance (AMR) remains a critical global health challenge, particularly in high-prevalence regions such as India, where rapid and interpretable diagnostic tools are urgently needed. To address this challenge, we present a computational framework for AMR prediction that integrates genomic and cellular phenotypic data using an in-house developed differential geometry–based Directed Simplicial Neural Network (Dg-Dir-SNNs) applied to multimodal datasets. Using this framework, we analyzed 384 clinically relevant AMR isolates, including Escherichia coli and Klebsiella pneumoniae , integrating 256 genomic k-mer features with 503 cellular morphology descriptors derived from high-content Cell Painting assays. The Dg-Dir-SNNs model constructs an inferred-causal network of top-ranked biomarker-driving features, predicting potential directional dependencies among genomic motifs and phenotypic features. Network analysis identified kmer_TATG as the top-ranked driver associated with predicted resistance, with a local neighborhood including other genomic motifs ( kmer_TTTT , kmer_CGTG , kmer_TCAC , kmer_CGTA , kmer_GAAA , kmer_TAAA , kmer_TACA , kmer_TGTG , kmer_TGAG , kmer_AAAA ) and a key morphological feature ( Cells_correlation_ER_Brightfield ). These relationships suggest potential mechanistic associations in which specific genomic motifs may influence cellular phenotypes linked to antimicrobial resistance. Although not yet clinically deployed, this approach demonstrates the potential of multimodal AI-driven modeling for rapid in silico AMR prediction. By providing interpretable, biologically grounded insights, the framework may support future diagnostic development, targeted surveillance strategies, and experimental validation in high-resistance healthcare settings.