Luay K. Nakhleh

This paper introduces a family of dissimilarity measures for phylogenetic networks that better capture reticulation differences than existing methods.

KLONE assigns single-cell RNA-seq cells to DNA-seq identified clones using a k-NN ensemble, achieving high accuracy and minority clone recall in colorectal cancer data.

DIMPLE infers phylogenetic networks from large-scale data using a divide-and-conquer approach, achieving accurate merging and improved reticulation recovery over existing methods.

Kente detects horizontal gene transfer in microbiomes using a pangenome graph-based approach, achieving higher precision and reducing false positives in simulated and real human gut metagenomes.

The paper introduces a new microbiome diversity measure based on graph sheaf Laplacian spectral energy, showing improved discrimination between healthy and IBD gut microbiomes.

This study examines how data errors affect phylogenetic network inference and shows that many methods are vulnerable to these errors, leading to unreliable results.

This study proves that gene tree parsimony estimators using duplication, loss, and deep coalescence costs are statistically inconsistent under the multispecies coalescent process.

This paper evaluates pseudo-likelihood-based Bayesian inference for species tree estimation under the multispecies coalescent, showing theoretical guarantees and empirical performance on synthetic and biological data.

GRNITE improves gene regulatory network inference by combining text embeddings and graph neural networks, enhancing AUROC and recall across methods.

This paper introduces NestedBD-Long, a method for inferring cancer evolution phylogenetic trees from longitudinal single-cell copy number data, showing improved accuracy with more temporal samples.

tMHG-Finder uses Mash guide trees and consensus representative sequences to find maximal homologous groups in bacterial genomes with linear scaling, reaching F-score 0.932 on simulated data.

The paper analyzes scRNA-seq data to infer clone-specific gene regulatory networks in tumors and shows distinct network architectures and treatment-induced rewiring across cancer types.

The paper introduces a divide-and-conquer method for large-scale evolutionary analysis of single-cell DNA data, demonstrating its effectiveness through experiments on clustering and dimensionality reduction methods.

This paper improves the computational efficiency of Bayesian species tree inference by restricting gene tree space without sacrificing accuracy.

SiCloneFit jointly reconstructs tumor clones and their phylogeny from single-cell genome sequencing data using a Bayesian framework to account for technical errors.

This paper introduces scVILP, a combinatorial method for detecting mutations in single-cell sequencing data by reconstructing phylogenies, achieving better runtime than existing methods.

OMIndel detects large indels using optical map data, outperforming existing tools by reducing false discoveries and identifying indels missed by other technologies.

SiFit infers tumor phylogenies from single-cell sequencing data using a finite-sites model, improving accuracy over infinite-sites methods in colorectal cancer datasets.