Artificial Intelligence: Decoding the Microbiome or Complicating It?

The skin microbiome, a complex ecosystem of bacteria, fungi, viruses, and other microorganisms living on our skin, plays a crucial role in maintaining skin health (Berg et al., 2020). The microbiome acts as a protective barrier, helps in wound healing, and regulates the immune system. An imbalance in the microbiome can result in various skin conditions, such as acne, eczema, and psoriasis. 

Historically, traditional skincare formulations have often taken a one-size-fits-all approach, which may not be effective for everyone due to individual differences in skin microbiomes. As a result, new approaches are being adopted within the industry to facilitate the transition to better researched solutions. With growing demand for unique formulations, and diagnostic tools, the industry has opened its arms to new technologies that can facilitate research within the space. Artificial Intelligence (AI) has become a central player in transforming our understanding and treatment of the skin microbiome, leading to innovative solutions in product development and clinical research (Sun et al., 2023). 

AI and machine learning is now being adopted on a global scale in various industries as a way of redefining workflows and increasing efficiency. This article will outline how AI can be applied to microbiome research, evaluating its potential uses as well as constraints. As this technology continues to develop, AI powered insights will likely play an important role in microbiome research and intervention in the future. 

What is artificial Intelligence? 

Artificial intelligence (AI) “refers to the ability of any machines which can stimulate the intelligence of higher organisms” (Bhardwaj et al., 2022). AI is a branch of computer science focused on creating systems capable of performing tasks that typically require human intelligence. These tasks include learning from data, recognizing patterns, making decisions, understanding natural language, and even mimicking human interactions. AI encompasses a wide range of technologies and methodologies, such as:

• Machine learning: where algorithms improve through experience
• Deep learning: which involves neural networks with many layers
• Artificial neural networks (ANN): computing systems inspired by biological neural networks, designed to recognize patterns and solve problems through a layered architecture of interconnected nodes or "neurons" that process information and learn from data
• Natural language processing: which allows machines to understand and respond to human language
• Computer vision: enabling machines to interpret and make decisions based on visual inputs

At its core, AI operates by processing large amounts of data, identifying correlations and patterns, and making predictions or decisions based on this analysis. This capability has led to AI's integration into various fields, including healthcare and wellness. For example, AI is used in medical diagnostics to analyse medical images, predict disease outcomes, drug discovery, and even radiography (Al-Antari, 2023). The development of AI continues to advance, pushing the boundaries of what machines can achieve and transforming numerous aspects of different industries.

AI’s application in microbiome work 

AI and classical machine learning methodologies have been used in microbiome studies for more than a decade, with research articles highlighting its uses as far back as 2013. Microbiome research at its core is driven by large amounts of data centred around different types of sequencing technologies:

• Amplicon sequencing - 16S, 18S, ITS gene sequencing for taxonomic identification
• Metagenomics - deep sequencing to characterise the collective genomes of microorganisms, and infer function
• Metatranscriptomics - RNA sequencing on multiple organisms (human, bacteria, fungi, viruses etc) to understand gene activity across organisms
• Metaproteomics - Assess protein expression to characterise multiple organisms (human, bacteria, fungi, viruses etc)
• Metabolomics - Sequence small molecule production/consumption on multiple organisms (human, bacteria, fungi, viruses etc)

The results from these bioinformatic tools can be used effectively as inputs for AI models. AI excels with large and complex datasets and can manage data gaps that usually pose problems for traditional statistics. By employing AI and machine learning, we can efficiently process the huge amounts of data generated by microbiome testing. AI models use embedded feature selection to identify the most relevant data features during training, eliminating the need to analyse the entire dataset each time.

Potential applications for AI in skin microbiome testing 

1. Microbiome Profiling and Diversity Analysis
2. Disease Biomarker Discovery
3. Microbiome-Host Interaction Analysis
4. Skin Microbiome-Based Therapeutics Development
5. Personalised Skincare and Treatment Optimization
6. Microbiome-Based Product Development and Formulation Optimization
7. Longitudinal Monitoring and Predictive Modeling

Study 1: Microbiome Profiling and Neural Networks 

Recent advances in high-throughput sequencing technologies have made microbiome profiles publicly accessible, revealing distinct profiles for healthy and diseased individuals and suggesting their potential as diagnostic tools. However, the complexity of metagenomic data poses challenges for current machine learning models. To address this, the study proposes MetaNN, a neural network framework that uses a new data augmentation technique to reduce overfitting. MetaNN significantly improves classification accuracy for both synthetic and real metagenomic data, outperforming existing models and paving the way for personalised treatments for microbiome-related diseases (Lo & Marculescu, 2019).

Study 2: AI and Longitudinal Data

The study investigated how the human microbiome changes dynamically over time due to factors like diet and medical interventions. It introduced 'phyLoSTM,' a deep learning framework that combined Convolutional Neural Networks and Long Short Term Memory Networks (LSTM) to extract features and analyse temporal dependencies in longitudinal microbiome data along with environmental factors for disease prediction. The framework also managed variable time points and balanced weights between imbalanced cases and controls. Testing on 100 simulated datasets and two real longitudinal studies demonstrated that phyLoSTM achieved higher predictive accuracy, with AUC improvements of 5% in simulated studies and significant gains in real studies compared to Random Forest, enhancing the prediction of disease outcomes from microbiome data (Sharma & Shu, 2021).

Limitations 

While it is undeniable that AI presents a promising future when it comes to advancing the workflows in microbiome testing, several notable limitations need to be addressed before AI models can reach their full potential in the field. 

Interpretability: “but why?” 

Transitioning from input to output in AI systems is straightforward, but in healthcare understanding the "why" behind decisions is crucial. AI, deep learning especially, often struggles with transparency, making it difficult to interpret how conclusions are reached. This lack of interpretability can lead to legal issues and the possibility of unknown factors influencing decisions. Without insight into the model's logic, trust in its output is limited. There's also the risk of confusing correlation with causation, meaning that just because data points are correlated, it doesn't imply one causes the other. Proper study design and longitudinal data are essential to provide context and distinguish between cases and controls.

Efforts are underway to improve AI interpretability by identifying the importance of different predictors within models. Incorporating prior knowledge into model creation can help guide the AI, adding constraints, and enhancing performance. This approach not only improves the AI's accuracy but also makes its decision-making process more transparent and trustworthy.

Data quality: “garbage in, garbage out” 

While the method used in AI is important, it's equally crucial to examine how the data is structured to ensure it is relevant and useful. Blindly trusting data produced by AI, especially in personal care and healthcare, can lead to significant problems.

The quality of AI outputs heavily depends on the quality and quantity of input data.In the context of the microbiome, gathering suitable datasets can be challenging: the field is still evolving, and much remains unexplained. The field is still evolving, and much remains unexplained. Additionally, microbiome data is highly complex and influenced by numerous contextual factors. We need comprehensive datasets to train AI models effectively, and without them, the application of AI in microbiome research will be limited. Thus, it's vital to continue uncovering the intricacies of the microbiome to enhance the effectiveness of AI models in this area.

Conclusion 

Moving forward, AI is poised to become increasingly prevalent in microbiome research, significantly simplifying the processes for bioinformaticians. By automating the analysis of complex datasets and enhancing the precision of predictive models, AI will streamline workflows, reducing the time and effort required for data interpretation and hypothesis generation. This technological advancement not only accelerates the pace of discovery but also enables more personalised and effective interventions, heralding a new era in microbiome research where scientists can leverage AI to unlock deeper insights and drive innovation in health and wellness. 

References

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Lo C, Marculescu R. MetaNN: accurate classification of host phenotypes from metagenomic data using neural networks. BMC Bioinformatics. 2019 Jun 20;20(Suppl 12):314. doi: 10.1186/s12859-019-2833-2. PMID: 31216991; PMCID: PMC6584521.

Sharma D, Xu W. phyLoSTM: a novel deep learning model on disease prediction from longitudinal microbiome data. Bioinformatics. 2021 Nov 5;37(21):3707-3714. doi: 10.1093/bioinformatics/btab482. PMID: 34213529.

Sun T, Niu X, He Q, Chen F, Qi RQ. Artificial Intelligence in microbiomes analysis: A review of applications in dermatology. Front Microbiol. 2023 Feb 1;14:1112010. doi: 10.3389/fmicb.2023.1112010. PMID: 36819026; PMCID: PMC9929457.