Functional data analysis has established itself as a powerful framework for analyzing data recorded over continuous domains such as time. Within this context, functional motif discovery refers to the identification of recurrent patterns that appear multiple times across different portions of a single curve and/or within misaligned portions of multiple curves. In this study, we explore the...
Quantitative analysis of microbial growth curves is essential for understanding how bacterial popu-
lations respond to environmental cues. Traditional analysis approaches make parametric assumptions
about the functional form of these curves, limiting their usefulness for studying conditions that distort
standard growth curves. In addition, modern robotics platforms enable the...
Population-scale genomic biobanks provide unique opportunities for data-driven drug target discovery. However, these resources often lack detailed data on clinical phenotypes, whereas clinical trials offer rich phenotypic information but are limited in omics coverage and mostly lack genotyping. This imbalance creates gaps in the mechanistic interpretation of clinical findings.
To address...
The estimation of a precision matrix is a crucial problem in various research fields, particularly when working with high dimensional data. In such settings, the most common approach is to use the penalized maximum likelihood. The literature typically employs Lasso, Ridge and Elastic-Net norms, which effectively shrink the entries of the estimated precision matrix. Although these shrinkage...
