Background

Microbiomics is the study of microbial communities and their interactions within specific environments, such as the human gut, soil, or oceans. These microbial communities play essential roles in health, agriculture, and ecosystems. With the advent of high-throughput sequencing technologies, researchers can generate vast amounts of microbiome data. However, analyzing this data is complex due to its high dimensionality and variability, necessitating advanced analytical tools.

ML offers powerful techniques to uncover patterns and insights within microbiome datasets. By applying ML methods, researchers can identify disease biomarkers, predict disease states, and even develop new therapeutics. Companies such as Karius, Viome, and Nexilico are already bringing ML-driven microbiome tools into clinical and commercial applications, signaling the technology's growing importance. Despite the promise of ML in microbiome research, its application is still maturing. Many studies applying ML to microbiomics lack adherence to best practices, leading to potential biases, unreliable models, and inaccurate conclusions. Addressing these challenges will be key to ensuring that ML-driven microbiome research produces meaningful and clinically useful results.

Challenges in Applying ML to Microbiomics and Mitigation Strategies

In our recent study, we provided an overview of current practices in applying supervised machine learning to microbiome data. Using a data-driven approach, we guided discussions on the strengths and limitations of different experimental designs and provided recommendations for avoiding common pitfalls that can impact model performance, trustworthiness, and reproducibility. Here, we highlight some of the most significant challenges and strategies to address them.

Dataset size

One of the most significant hurdles in applying ML to microbiome research is the relatively small size of available datasets. ML algorithms typically require large volumes of data to produce robust models, yet microbiome studies often work with limited sample sizes. Our analysis found that 73% of microbiome ML studies had fewer than 1,000 samples, with over a third including fewer than 100 samples. Such small datasets can lead to high variability and poor model generalization, ultimately reducing their reliability in clinical settings. Large-scale data collection efforts, supported by data-sharing initiatives and consortia, will be essential to overcoming this limitation. Researchers may mitigate the detrimental effects of small dataset sizes by applying techniques such as data augmentation and ensuring the use of proper validation practices to minimize bias from small sample sizes.

Demographic bias

Another pressing issue is the lack of demographic diversity in microbiome datasets. Most studies report only basic demographic details, such as country of residence, age, and sex, while factors like race, education level, and income are often missing. This lack of representation can lead to biased models that perform well in one population but poorly in others. For example, studies focusing on Asian populations were found to be 21 times more prevalent than those on African or Oceanian populations, raising concerns about how well these models will generalize across global populations. Ensuring demographic diversity in microbiome research will be critical for developing unbiased and effective ML-based microbiome tools. Researchers should actively incorporate diverse populations into study designs, advocate for the inclusion of underrepresented groups, and properly annotate demographic data in repositories to enhance inclusivity​​.

Data leakage and validation issues

A common pitfall in ML applications to microbiomics is test set omission and data leakage — issues that can lead to artificially boosted model performance estimates. Our review of ML microbiome studies found that in 86% of cases, it was unclear whether proper validation techniques had been applied. Without rigorous model evaluation, the ​credibility​ of ​models​ is compromised​, and their commercial deployment could potentially result in significant financial losses and operational challenges​. ​​Raising awareness about proper ML validation techniques and integrating standardized protocols can help prevent these methodological flaws. Researchers should employ best practices such as proper train-test splits, cross-validation strategies, and independent test set evaluation to ensure robust model performance.

​​​The Importance of Explainability in Microbiome ML Research

Beyond technical challenges, the application of ML in microbiome research also highlights the critical need for explainability. Large-scale studies produce complex models that often function as “black boxes,” obscuring how specific features drive predictions. In clinical and research settings, this lack of transparency can hinder understanding and acceptance of ML-driven insights. To address this, many researchers now leverage Explainable AI (XAI) initiatives, which focus on creating tools and methods to illuminate how models arrive at their conclusions. By applying XAI to ML in microbiomics, clinicians and scientists can better trust and interpret predictions, refining diagnostic and therapeutic strategies. This clarity is especially valuable when analyzing highly variable microbiome datasets — XAI methods offer a clearer view of data patterns, guiding more informed decisions and accelerating progress in the field.

Looking Ahead

Machine learning has the potential to revolutionize microbiome research, paving the way for new diagnostics, therapeutics, and personalized medicine approaches. However, realizing this potential requires overcoming significant challenges, including small dataset sizes, demographic biases, and methodological inconsistencies. By fostering interdisciplinary collaboration, implementing standardized best practices, and addressing ethical concerns, the microbiome research community can ensure that ML models are robust, fair, and clinically relevant. As data-sharing initiatives expand and computational methods improve, ML-driven microbiome research will play an increasingly vital role in advancing human health. 

​​​​At Quantori, we create teams of top-tier bioinformaticians, ML engineers, and data scientists to help our clients navigate complex ML model development with efficiency and precision. By adhering to best practices, we save our clients valuable time and resources while delivering robust, reliable solutions. ​For a deeper dive into the topics covered in this blog, we recommend reading our article, ‘Supervised machine learning for microbiomics: Bridging the gap between current and best practices’.

_{Action items for how scientists can improve current practices in the application of ML to microbiomics data. Source: Dudek et al., 2024, Machine Learning with Applications 18, 100607}