Human interpretable artificial intelligence applications for microbial-related diseases

Abstract

The human microbiome is a complex ecosystem that is influenced not only by host genetics but environmental stimuli. With advancements in next-generation sequencing (NGS) technologies, genomics and related meta-omics such as metagenomics, metatranscriptomics and metaproteomics have become increasingly accessible for researchers and clinicians to investigate microbial-related diseases. However, analysis of the outputs of “omics” technologies are often difficult due to variance introduced by biological complexity, batch effects from laboratory protocols/conditions, and the sensitivity/calibration of highly sensitive instruments. The biological complexity of “omics” presents a considerable analytical obstacle as most datasets contain hundreds of thousands to millions of unique features with unknown connections and nested hierarchies. In addition to this inherent complexity, the deluge of data generated from NGS technologies is fundamentally compositional, conveys only relative information, and because of this cannot be robustly analyzed using conventional statistical approaches. Furthermore, meta-omics datasets are typically sparse and the number of biological features often vastly exceeds the number of biological samples which can introduce anomalies in statistical analysis and the downstream findings if not addressed accordingly; a term dubbed as “the curse of dimensionality”. The complexity, compositionality, and dimensionality of “omics” datasets makes it challenging to derive clinical meaning and an understanding of the microbial system with respect to a host phenotype. Although, artificial intelligence and machine-learning methods have progressed substantially in recent years, their applications in domain sciences such as biology, and by extension “omics” technologies, have been limited in terms of human interpretability. In many machine-learning paradigms, interpretability is often sacrificed for analytical performance, or vice versa, but recently a domain-agnostic effort aims to develop explainable artificial intelligence algorithms that have both high modeling performance and human interpretability; a major goal of biomedical sciences. In this dissertation, I develop novel approaches in bridging biological science with machine learning methods at the vanguard of scientific development through the initiative of explainable artificial intelligence. The methods developed are validated on 3 datasets pertaining to microbial-related diseases including antibiotic resistance discovery, acute malnutrition in West African children, and caries pathology in Australian juvenile twins. The combination of methods developed are expected to provide the means for clinical researchers to overcome obstacles in interrogating the complex narratives that determine health and disease.