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.

Keywords: Machine learning; Microbiome; Multi-label classification; Single-label classification.

Fig. 1

Comparison of single-label classification and multi-label classification. a. Single-label classification requires a sample has one label (status). b. Multi-label classification can detect more than one status for each sample.

Fig. 2

Microbial biomarkers of autoimmune selected from SD and MD by distribution-free independence test.

Fig. 3

Decision tree of GBDT binary classifier constructed from SD (A) was less complicated than that from MD (B). In each tree internal nodes represent taxa on genus-level, leaf nodes represent labels, and branch weights represent criteria for decision.

Fig. 4

Three key technical issues in multi-label classification. a. Too many labels in training data leads to unexpected high computational cost. b. Missed label reduces the detection sensitivity. c. Ambiguous label introduces false positive results.