Advances In Mathematical And Computational Oncology Volume Iii
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Advances in mathematical and computational oncology volume III
Author | : George Bebis,Dinler Amaral Antunes,Ken Chen,Mohammad Kohandel,Kathleen Wilkie,Mamoru Kato,Jinzhuang Dou |
Publsiher | : Frontiers Media SA |
Total Pages | : 374 |
Release | : 2023-10-25 |
Genre | : Medical |
ISBN | : 9782832536643 |
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Advances in Mathematical and Computational Oncology Volume II
Author | : George Bebis,Max A. Alekseyev,Heyrim Cho,Jana Gevertz,David A. Hormuth, II,Maria Rodriguez Martinez |
Publsiher | : Frontiers Media SA |
Total Pages | : 179 |
Release | : 2022-06-27 |
Genre | : Science |
ISBN | : 9782889764136 |
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Advances in Mathematical and Computational Oncology
Author | : Doron Levy,George Bebis,Russell C. Rockne,Ernesto Augusto Bueno Da Fonseca Lima,Katharina Jahn,Panayiotis V. Benos |
Publsiher | : Frontiers Media SA |
Total Pages | : 337 |
Release | : 2022-05-05 |
Genre | : Science |
ISBN | : 9782889741786 |
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Mathematical and Computational Oncology
Author | : George Bebis,Takis Benos,Ken Chen,Katharina Jahn,Ernesto Lima |
Publsiher | : Springer Nature |
Total Pages | : 99 |
Release | : 2019-11-14 |
Genre | : Computers |
ISBN | : 9783030352103 |
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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).
Introduction to Mathematical Oncology
Author | : Yang Kuang,John D. Nagy,Steffen E. Eikenberry |
Publsiher | : CRC Press |
Total Pages | : 470 |
Release | : 2016-06-14 |
Genre | : Mathematics |
ISBN | : 9781498785532 |
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Introduction to Mathematical Oncology presents biologically well-motivated and mathematically tractable models that facilitate both a deep understanding of cancer biology and better cancer treatment designs. It covers the medical and biological background of the diseases, modeling issues, and existing methods and their limitations. The authors introduce mathematical and programming tools, along with analytical and numerical studies of the models. They also develop new mathematical tools and look to future improvements on dynamical models. After introducing the general theory of medicine and exploring how mathematics can be essential in its understanding, the text describes well-known, practical, and insightful mathematical models of avascular tumor growth and mathematically tractable treatment models based on ordinary differential equations. It continues the topic of avascular tumor growth in the context of partial differential equation models by incorporating the spatial structure and physiological structure, such as cell size. The book then focuses on the recent active multi-scale modeling efforts on prostate cancer growth and treatment dynamics. It also examines more mechanistically formulated models, including cell quota-based population growth models, with applications to real tumors and validation using clinical data. The remainder of the text presents abundant additional historical, biological, and medical background materials for advanced and specific treatment modeling efforts. Extensively classroom-tested in undergraduate and graduate courses, this self-contained book allows instructors to emphasize specific topics relevant to clinical cancer biology and treatment. It can be used in a variety of ways, including a single-semester undergraduate course, a more ambitious graduate course, or a full-year sequence on mathematical oncology.
Mathematical and Computational Oncology
Author | : George Bebis,Terry Gaasterland,Mamoru Kato,Mohammad Kohandel,Kathleen Wilkie |
Publsiher | : Springer Nature |
Total Pages | : 91 |
Release | : 2021-12-11 |
Genre | : Computers |
ISBN | : 9783030912413 |
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This book constitutes the refereed proceedings of the Third International Symposium on Mathematical and Computational Oncology, ISMCO 2021, held in October 2021. Due to COVID-19 pandemic the conference was held virtually. The 3 full papers and 4 short papers presented were carefully reviewed and selected from 20 submissions. The papers are organized in topical sections named: statistical and machine learning methods for cancer research; mathematical modeling for cancer research; spatio-temporal tumor modeling and simulation; general cancer computational biology; mathematical modeling for cancer research; computational methods for anticancer drug development.
Mathematical and Computational Studies on Progress Prognosis Prevention and Panacea of Breast Cancer
Author | : Suhrit Dey,Charlie Dey |
Publsiher | : Springer Nature |
Total Pages | : 377 |
Release | : 2022-03-25 |
Genre | : Computers |
ISBN | : 9789811660771 |
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This book’s aim is to study the mathematical and computational models to analyze the progress, prognosis, prevention, and panacea of breast cancer. The book discusses application of Markov chains and transient mappings, Charlie–Simpson numerical algorithm, models represented by nonlinear reaction–diffusion-type partial differential equations, and related techniques. The book also attempts to design mathematical model of targeted strategic treatments by using Skilled Killer Drugs (SKD1 and SKD2) to suggest the improvisation of future cancer treatments. Both graduate students and researchers of computational biology and oncologists will benefit by studying this book. Researchers of cancer studies and biological sciences will also find this work helpful.
Computational Biology
Author | : Tuan Pham |
Publsiher | : Springer |
Total Pages | : 310 |
Release | : 2012-02-25 |
Genre | : Medical |
ISBN | : 146142478X |
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This volume covers techniques in computational biology and their applications in oncology. It details advanced statistical methods, heuristic algorithms, cluster analysis, data modeling, and image and pattern analysis applied to cancer research.