Hamim Zafar

TL;DR: The performance of the proposed statistical inference method on synthetic and experimental data sets from two colorectal cancer patients to trace evolutionary lineages in primary and metastatic tumors suggests that employing a finite-sites model leads to improved inference of tumor phylogenies.

TL;DR: Monovar, a statistical method for detecting and genotyping single-nucleotide variants in single-cell data, exhibited superior performance over standard algorithms on benchmarks and in identifying driver mutations and delineating clonal substructure in three different human tumor data sets.

TL;DR: A nonparametric Bayesian method that reconstructs the clonal populations as clusters of single cells, genotypes of each clone, and the evolutionary relationship between the clones in a fully Bayesian framework, suggesting that joint reconstruction of tumor clones and clonal phylogeny under a finite-site model of evolution leads to more accurate inferences.

TL;DR: LinTIMaT lineages have better cell type coherence, improve the functional significance of gene sets and provide new insights on progenitors and differentiation pathways, and enables the learning of a species-invariant lineage tree.

TL;DR: This paper proposes an improved and adaptive variant of the differential evolution algorithm for the design of two-channel quadrature mirror filters with linear phase characteristics that is able to perform better than the other existing design methods.