# Whole-Genome Phenotype Prediction with Machine Learning: Open Problems in Bacterial Genomics

arXiv: https://arxiv.org/abs/2502.07749 · HTML: https://mlsanity.com/paper/2502.07749/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 45 references that are in the map.
- First posted on arXiv 11 Feb 2025.

## Builds on (most cited first, 2 of 2)

- [Predictive and Causal Implications of using Shapley Value for Model Interpretation](https://mlsanity.com/paper/2008.05052/) (2020)
- [Conditionally Invariant Representation Learning for Disentangling Cellular Heterogeneity](https://mlsanity.com/paper/2307.00558/) (2023)

## Built on by (most cited first, 1 of 1)

- [VAMP-Net: An Interpretable Multi-Path Network of Genomic Permutation-Invariant Set Attention and Quality-Aware 1D-CNN for MTB Drug Resistance](https://mlsanity.com/paper/2512.21786/) (2025)

## Abstract (arXiv)

How can we identify causal genetic mechanisms that govern bacterial traits? Initial efforts entrusting machine learning models to handle the task of predicting phenotype from genotype return high accuracy scores. However, attempts to extract any meaning from the predictive models are found to be corrupted by falsely identified "causal" features. Relying solely on pattern recognition and correlations is unreliable, significantly so in bacterial genomics settings where high-dimensionality and spurious associations are the norm. Though it is not yet clear whether we can overcome this hurdle, significant efforts are being made towards discovering potential high-risk bacterial genetic variants. In view of this, we set up open problems surrounding phenotype prediction from bacterial whole-genome datasets and extending those to learning causal effects, and discuss challenges that impact the reliability of a machine's decision-making when faced with datasets of this nature.