Towards multi-label classification: Next step of machine learning for microbiome research

Abstract

Machine learning (ML) has been widely used in microbiome research for biomarker selection and disease prediction. By training microbial profiles of samples from patients and healthy controls, ML classifiers constructs data models by community features that highly correlated with the target diseases, so as to determine the status of new samples. To clearly understand the host-microbe interaction of specific diseases, previous studies always focused on well-designed cohorts, in which each sample was exactly labeled by a single status type. However, in fact an individual may be associated with multiple diseases simultaneously, which introduce additional variations on microbial patterns that interferes the status detection. More importantly, comorbidities or complications can be missed by regular ML models, limiting the practical application of microbiome techniques. In this review, we summarize the typical ML approaches of single-label classification for microbiome research, and demonstrate their limitations in multi-label disease detection using a real dataset. Then we prospect a further step of ML towards multi-label classification that potentially solves the aforementioned problem, including a series of promising strategies and key technical issues for applying multi-label classification in microbiome-based studies.

Parallel‐Meta Suite: Interactive and rapid microbiome data analysis on multiple platforms

“…output can be directly and seamlessly taken by our previously developed tools like Microbiome Search Engine [35] or Meta-Apo [36], which greatly promotes the data-driven science [37] in this field.…”

Comprehensive Assessment of 16S rRNA Gene Amplicon Sequencing for Microbiome Profiling across Multiple Habitats

“…The microbiome plays important roles in various fields such as environment protection ( 1 ), bioenergy ( 2 ), food ( 3 ), and human health ( 4 , 5 ). In the past decade, amplicon sequencing of the 16S rRNA gene has been regarded as a fast and low-cost approach to studying the microbiome composition and diversity; hence, it has been employed in numerous works such as the Human Microbiome Project (HMP) ( 6 – 8 ), Earth Microbiome Project (EMP) ( 9 ), and Metagenomics of the Human Intestinal Tract (MetaHIT) ( 10 ).…”

Host‐Variable‐Embedding Augmented Microbiome‐Based Simultaneous Detection of Multiple Diseases by Deep Learning

“…Our recent study has shown that gut microbiomes with comorbidities can have distinct microbial patterns from those with a single disease, even though they share common biomarkers. [ 9 ] As a result, comorbidities can significantly disrupt disease detection.…”

“…Our recent study has shown that gut microbiomes with comorbidities can have distinct microbial patterns from those with a single disease, even though they share common biomarkers. [9] As a result, comorbidities can significantly disrupt disease detection. On the other hand, the lifestyle and physiological variables of human hosts have strong connections to various diseases, which can also interfere with the recognition of microbiome-based status.…”