Fast intratumor heterogeneity inference from single-cell sequencing data.

Autor: Kızılkale C; Department of Electrical Engineering and Computer Sciences UC Berkeley, Berkeley, CA, USA.; Computational Research Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA., Rashidi Mehrabadi F; Cancer Data Science Laboratory, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA.; Department of Computer Science, Indiana University, Bloomington, IN, USA., Sadeqi Azer E; Department of Computer Science, Indiana University, Bloomington, IN, USA.; Google LLC, Sunnyvale, CA, USA., Pérez-Guijarro E; Laboratory of Cancer Biology and Genetics, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA., Marie KL; Laboratory of Cancer Biology and Genetics, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA., Lee MP; Laboratory of Cancer Biology and Genetics, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA., Day CP; Laboratory of Cancer Biology and Genetics, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA., Merlino G; Laboratory of Cancer Biology and Genetics, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA., Ergün F; Department of Computer Science, Indiana University, Bloomington, IN, USA., Buluç A; Department of Electrical Engineering and Computer Sciences UC Berkeley, Berkeley, CA, USA.; Computational Research Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA., Sahinalp SC; Cancer Data Science Laboratory, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA. cenk.sahinalp@nih.gov., Malikić S; Cancer Data Science Laboratory, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA. salem.malikic@nih.gov.

Publikováno v: Nature computational science [Nat Comput Sci] 2022 Sep; Vol. 2 (9), pp. 577-583. Date of Electronic Publication: 2022 Sep 08.

PhISCS-BnB: a fast branch and bound algorithm for the perfect tumor phylogeny reconstruction problem.

Autor: Sadeqi Azer E; Department of Computer Science, Indiana University, Bloomington, IN 47408, USA., Rashidi Mehrabadi F; Department of Computer Science, Indiana University, Bloomington, IN 47408, USA.; Cancer Data Science Laboratory, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA., Malikić S; Department of Computer Science, Indiana University, Bloomington, IN 47408, USA., Li XC; Cancer Data Science Laboratory, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA.; Program in Computational Biology, Bioinformatics and Genomics, University of Maryland, College Park, MD 20742, USA., Bartok O; Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel., Litchfield K; Cancer Evolution and Genome Instability Laboratory, Francis Crick Institute, London NW1 1AT, UK.; Cancer Research UK Lung Cancer Centre of Excellence London, University College London Cancer Institute, London WC1E 6DD, UK., Levy R; Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel., Samuels Y; Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel., Schäffer AA; Cancer Data Science Laboratory, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA., Gertz EM; Cancer Data Science Laboratory, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA., Day CP; Laboratory of Cancer Biology and Genetics, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA., Pérez-Guijarro E; Laboratory of Cancer Biology and Genetics, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA., Marie K; Laboratory of Cancer Biology and Genetics, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA., Lee MP; Laboratory of Cancer Biology and Genetics, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA., Merlino G; Laboratory of Cancer Biology and Genetics, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA., Ergun F; Department of Computer Science, Indiana University, Bloomington, IN 47408, USA., Sahinalp SC; Cancer Data Science Laboratory, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA.

Publikováno v: Bioinformatics (Oxford, England) [Bioinformatics] 2020 Jul 01; Vol. 36 (Suppl_1), pp. i169-i176.

Three Metaheuristic Approaches for Tumor Phylogeny Inference: An Experimental Comparison.

Autor: Ciccolella, Simone¹ (AUTHOR) simone.ciccolella@unimib.it, Della Vedova, Gianluca¹ (AUTHOR) gianluca.dellavedova@unimib.it, Filipović, Vladimir² (AUTHOR) vladimir.filipovic@math.rs, Soto Gomez, Mauricio³ (AUTHOR) mauricio.soto@unimi.it

Publikováno v: Algorithms. Jul2023, Vol. 16 Issue 7, p333. 20p.

Oncotree2vec — a method for embedding and clustering of tumor mutation trees.

Autor: Baciu-Drăgan, Monica-Andreea, Beerenwinkel, Niko

Publikováno v: Bioinformatics; 2024 Supplement, Vol. 40, pi180-i188, 9p

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