Rosa Aghdam

SimMapNet improves gene regulatory network inference by integrating Gene Ontology similarities, outperforming existing methods on bacterial and fly datasets.

This paper evaluates six microbial network inference methods using real and generated data, showing that synthetic benchmarks can be misleading and proposing a reproducibility-focused framework for better evaluation.

PhyMapNet infers microbiome networks using a phylogeny-guided Bayesian framework, showing improved robustness and reproducibility compared to existing methods.

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

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.

NN-RNALoc predicts mRNA sub-cellular localization using distance-based sub-sequence profiles and protein-protein interaction data, achieving higher accuracy and faster computation than existing models.