The many microbial communities around us form interactive and dynamic ecosystems called
microbiomes. Though concealed from the naked eye, microbiomes govern and influence …

In this review, we examine the problem of designing interpretable and explainable machine
learning models. Interpretability and explainability lie at the core of many machine learning …

The plant-associated microbiome is a key component of plant systems, contributing to their
health, growth, and productivity. The application of machine learning (ML) in this field …

The convergence of artificial intelligence (AI) and microbial therapeutics offers promising
avenues for novel discoveries and therapeutic interventions. With the exponential growth of …

Motivation The automatic discovery of sparse biomarkers that are associated with an
outcome of interest is a central goal of bioinformatics. In the context of high-throughput …

Data augmentation plays a key role in modern machine learning pipelines. While numerous
augmentation strategies have been studied in the context of computer vision and natural …

Simplex-valued data appear throughout statistics and machine learning, for example in the
context of transfer learning and compression of deep networks. Existing models for this class …

In this paper, we consider lasso problems with zero-sum constraint, commonly required for
the analysis of compositional data in high-dimensional spaces. A novel algorithm is …

The majority of machine learning methods and algorithms give high priority to prediction
performance which may not always correspond to the priority of the users. In many cases …

Compositional data refers to simplex-valued data, or equivalently, nonnegative vectors
whose totals are uninformative. This data modality is of relevance across several scientific …