Salem Malikić

GenCore uses Locally Consistent Parsing to sketch and estimate genomic distances between closely related large genomes, successfully reconstructing simulated progression trees and recapitulating primate phylogeny.

This paper introduces omlta, an algorithm for aligning clonal trees to compare tumor progression, and applies it to cancer datasets.

Wakhan reconstructs chromosome-scale copy number profiles of tumor genomes using long-read sequencing, showing better consistency with data than existing methods.

The paper uses long-read sequencing to analyze genomic and epigenomic changes in melanoma subclones, revealing parallel evolutionary paths and lineage-specific methylation patterns.

Severus detects and characterizes somatic structural variations in tumor genomes using long reads, outperforming existing methods in accuracy and revealing clinically relevant rearrangements.

The paper models gene expression adaptation in tumor subclones using stochastic processes and finds non-genetic mechanisms drive subclonal evolution and treatment response.

This paper introduces DETOPT, a method for accurately placing SNVs affected by CNAs in tumor progression trees, showing improved accuracy on simulated data and biologically plausible results on breast cancer metastases.

This paper introduces Sgootr, a method to infer tumor lineage trees from single-cell methylation data, revealing metastatic progression models and identifying lineage-informative CpG sites.

This paper reviews methods for inferring tumor evolutionary trees using binary matrix optimization and discusses a deep learning approach for topological inference.

Trisicell infers subclonal expansion patterns and therapeutic impact of intratumor heterogeneity from scRNAseq data, revealing correlations between mutational composition and tumor aggressiveness.

This paper introduces deep learning methods to infer tumor phylogeny topology and feasibility from single-cell genotype data, demonstrating the potential of data-driven approaches in cancer evolution analysis.

PhISCS-BnB computes the most likely perfect tumor phylogeny from single-cell sequencing data, offering optimality guarantees and significant speed improvements over existing methods.

This paper introduces CONETT, a method to identify conserved tumor evolution trajectories across multiple cancers, finding both previously reported and novel trajectories in renal and lung cancer datasets.

PhISCS integrates single-cell and bulk sequencing data to reconstruct sub-perfect tumor phylogenies by minimizing false negatives, false positives, and ISA violations.

This paper develops a computational method to infer tumour evolution trees from combined single-cell and bulk sequencing data, showing improved accuracy in subclone identification and mutation history reconstruction.