Konstantinos Giannakis

The paper develops a method to identify the most parsimonious pathways of binary feature accumulation and convergent evolution, using a hypercubic transition network and cost function minimization.

HyperTraPS-CT infers and predicts accumulation pathways in complex systems with flexible data and model structures, demonstrating its application to cancer and tuberculosis resistance.

The paper examines the relationship between mitochondrial and plastid genome reduction across eukaryotic species, finding a generally positive correlation in gene retention but limited evidence for coevolution of specific gene pairs.

The paper introduces a method to compare transition graphs from different datasets using a metric called WFCC, demonstrating its application to antimicrobial resistance data.

The paper introduces a maximum likelihood approach to analyze mtDNA heteroplasmy data, showing that traditional statistics can lead to false positives in detecting selection and bottleneck effects.