Jing Ma is a biostatistician who specializes in statistical machine learning and high-dimensional data analysis. In particular, Ma develops new statistical methods for problems in the emerging “omics” fields – such as genomics, metabolomics and metagenomics. She believes that these new methods and computational tools have the potential to accelerate our mechanistic understanding of complex biological processes and help develop vital resources to address biological, clinical and public health problems.
Networks are important in this learning process because they are well-suited to representing interactions between biomolecules and microbes. For example, Ma has developed differential network enrichment analysis, which distills large amounts of biological data down to a smaller set of concepts, to identify and validate lipid subnetworks that potentially differentiate chronic kidney diseases by severity or progression. She has also used differential analysis of microbial community structures to detect age-related gut microbial interactions.
Jing Ma is a biostatistician who specializes in statistical machine learning and high-dimensional data analysis. In particular, Ma develops new statistical methods for problems in the emerging “omics” fields – such as genomics, metabolomics and metagenomics. She believes that these new methods and computational tools have the potential to accelerate our mechanistic understanding of complex biological processes and help develop vital resources to address biological, clinical and public health problems.
Networks are important in this learning process because they are well-suited to representing interactions between biomolecules and microbes. For example, Ma has developed differential network enrichment analysis, which distills large amounts of biological data down to a smaller set of concepts, to identify and validate lipid subnetworks that potentially differentiate chronic kidney diseases by severity or progression. She has also used differential analysis of microbial community structures to detect age-related gut microbial interactions.