Claudia Solís‐Lemus

The paper introduces a latent Gaussian dynamic graphical model for longitudinal non-Gaussian data, showing it can recover both contemporaneous and lag-one temporal dependence structures in microbiome data.

This study tests using a variational autoencoder to reconstruct ancestral sequences by interpolating in its latent space, finding it less accurate than standard methods despite containing evolutionary signal.

This study evaluates SNaQ's performance on non-level-1 phylogenetic networks and introduces new metrics to assess structural recovery.

This paper extends the SNaQ method to infer complex semi-directed phylogenetic networks, showing improved accuracy and scalability for reconstructing reticulate evolutionary histories.

CMiNet constructs consensus microbiome networks by integrating multiple inference methods, improving robustness and biological interpretability.

SNaQ.jl improves scalability for phylogenetic network inference by enhancing computational efficiency without altering method accuracy.

This paper introduces Qsin, a sparse learning method that reduces the size of concordance factors tables for phylogenetic network inference while maintaining accuracy and reducing computational time.

This paper introduces InPhyNet, a method for scalable phylogenetic network inference that merges level-1 networks to achieve linear scalability and high accuracy.

The paper introduces CMIMN, a Bayesian network method for microbiome network inference and a consensus approach to improve reliability, identifying key taxa associated with potato common scab disease.

This study identifies keystone taxa in Lake Mendota's microbial networks and shows their role in nutrient cycling and adaptability to environmental changes.

This paper evaluates high dimensional mixed-effect models using simulated and real omics data, showing SCAD penalty outperforms LASSO in variable selection and estimation accuracy.

The paper introduces a unified likelihood framework for mediation analysis in case-control studies to improve efficiency of indirect effect estimates by leveraging exposure-mediator relationships.

The paper introduces a kernel method to dissect genetic signals in high-dimensional phenotypes, showing it controls type I error and identifies direct effects in GWAS data.

This paper introduces a new rare-variant association test that incorporates family history information, showing improved performance over standard methods in simulated data and a study of cleft lip and palate.