Mathematical and computational oncology : first International Symposium, ISMCO 2019, Lake Tahoe, NV, USA, October 14-16, 2019, Proceedings / George Bebis, Takis Benos, Ken Chen, Katharina Jahn, Ernesto Lima (eds.).
ISMCO (Symposium) (1st : 2019 : Lake Tahoe, Nev.); Bebis, George, editor.; Benos, Takis, editor.; Chen, Ken, editor.; Jahn, Katharina, editor.; Lima, Ernesto, editor.
2019
RC254.5
Available Online

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Title Mathematical and computational oncology : first International Symposium, ISMCO 2019, Lake Tahoe, NV, USA, October 14-16, 2019, Proceedings / George Bebis, Takis Benos, Ken Chen, Katharina Jahn, Ernesto Lima (eds.).
Meeting Name ISMCO (Symposium) (1st : 2019 : Lake Tahoe, Nev.)
ISBN 9783030352103 (electronic book)
3030352102 (electronic book)
9783030352097
DOI https://doi.org/10.1007/978-3-030-35210-3
Published Cham, Switzerland : Springer, 2019.
Language English
Description 1 online resource (xxiii, 99 pages) : illustrations.
Other Standard Identifiers 10.1007/978-3-030-35210-3 doi
Call Number RC254.5
Dewey Decimal Classification 616.99/40015118
Summary This book constitutes the refereed proceedings of the First International Symposium on Mathematical and Computational Oncology, ISMCO'2019, held in Lake Tahoe, NV, USA, in October 2019. The 7 full papers presented were carefully reviewed and selected from 30 submissions. The papers are organized in topical sections named: Tumor evolvability and intra-tumor heterogeneity; Imaging and scientific visualization for cancer research; Statistical methods and data mining for cancer research (SMDM); Spatio-temporal tumor modeling and simulation (STTMS). -- Provided by publisher.
Note International conference proceedings.
Includes author index.
Access Note Access limited to authorized users.
Source of Description Online resource; title from PDF title page (SpringerLink, viewed November 20, 2019).
Added Author Bebis, George, editor.
Benos, Takis, editor.
Chen, Ken, editor.
Jahn, Katharina, editor.
Lima, Ernesto, editor.
Series Lecture notes in computer science ; 11826.
LNCS sublibrary. SL 6, Image processing, computer vision, pattern recognition, and graphics.
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[I]. Tumor evolvability and intra-tumor heterogeneity: Phylogenies derived from matched transcriptome reveal the evolution of cell populations and temporal order of perturbed pathways in breast cancer brain metastases / Yifeng Tao, Haoyun Lei, Adrian V. Lee, Jian Ma, and Russell Schwartz
Modeling the evolution of Ploidy in a resource restricted environment / Gregory Kimmel, Jill Barnholtz-Sloan, Hanlee Ji, Philipp Altrock, and Noemi Andor.

[II]. Imaging and scientific visualization for cancer research: cmIF : a Python library for scalable multiplex imaging pipelines / Jennifer Eng, Elmar Bucher, Elliot Gray, Lydia Grace Campbell, Guillaume Thibault, Laura Heiser, Summer Gibbs, Joe W. Gray, Koei Chin, and Young Hwan Chang.

[III]. Statistical methods and data mining for cancer research (SMDM): Accurate and flexible Bayesian mutation call from multi-regional tumor samples / Takuya Moriyama, Seiya Imoto, Satoru Miyano, and Rui Yamaguchi
Flexible data trimming for different machine learning methods in omics-based personalized oncology / Victor Tkachev, Anton Buzdin, and Nicolas Borisov.

[IV]. Spatio-temporal tumor modeling and simulation (STTMS): Towards model-based characterization of biomechanical tumor growth phenotypes / Daniel Abler, Philippe Büchler, and Russell C. Rockne
Population modeling of tumor growth curves, the reduced Gompertz model and prediction of the age of a tumor / Cristina Vaghi, Anne Rodallec, Raphaelle Fanciullino, Joseph Ciccolini, Jonathan Mochel, Michalis Mastri, John M. L. Ebos, Clair Poignard, and Sebastien Benzekry.

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