There has been increasing concern within the machine learning community and beyond that
Artificial Intelligence (AI) faces a bias and discrimination crisis which needs AI fairness with …

Fairness in machine learning (ML) has gained attention within the ML community and the
broader society beyond with many fairness definitions and algorithms being proposed …

Algorithmic fairness, the research field of making machine learning (ML) algorithms fair, is
an established area in ML. As ML technologies expand their application domains, including …

Identifying relationships between genetic variations and their clinical presentations has
been challenged by the heterogeneous causes of a disease. It is imperative to unveil the …

Deconvolution of mouse transcriptomic data is challenged by the fact that mouse models
carry various genetic and physiological perturbations, making it questionable to assume …

Single cell RNA-sequencing (scRNA-seq) technology enables comprehensive
transcriptomic profiling of thousands of cells with distinct phenotypic and physiological states …

Boolean matrix factorization (BMF) is a combinatorial problem arising from a wide range of
applications including recommendation system, collaborative filtering, and dimensionality …

Gene expression is an intricate process that determines different cell types and functions in
metazoans, where most of its regulation is communicated through discrete signals, like …

Boolean matrix factorization (BMF) has been widely utilized in fields such as
recommendation systems, graph learning, text mining, and-omics data analysis. Traditional …

Biological functions in cells are highly complicated and heterogenous, and can be reflected
by omics data, such as gene expression levels. Detecting subspace structures in omics data …