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Titlebook: Cancer, Complexity, Computation; Igor Balaz,Andrew Adamatzky Book 2022 The Editor(s) (if applicable) and The Author(s), under exclusive li

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發(fā)表于 2025-3-21 18:29:48 | 只看該作者 |倒序瀏覽 |閱讀模式
書目名稱Cancer, Complexity, Computation
編輯Igor Balaz,Andrew Adamatzky
視頻videohttp://file.papertrans.cn/222/221276/221276.mp4
概述Gives an unequivocal slice of all areas that relate to a quest for understanding cancer.Appeals to medical scientists, computer scientists, biologists, and mathematicians.Presents attractive reading f
叢書名稱Emergence, Complexity and Computation
圖書封面Titlebook: Cancer, Complexity, Computation;  Igor Balaz,Andrew Adamatzky Book 2022 The Editor(s) (if applicable) and The Author(s), under exclusive li
描述.This book presents unique compendium of groundbreaking ideas where scientists from many different backgrounds are united in their interest in interdisciplinary approaches towards origins and development of cancers, innovative ways of searching for cancer treatment and the role of cancer in the evolution. Chapters give an unequivocal slice of all areas that relate to a quest for understanding cancer and its origin as many-fold nonlinear system, complexity of the cancer developments, a search for cancer treatment using? artificial intelligence and evolutionary optimisation, novel modelling techniques, molecular origin of cancer, the role of cancer in evolution of species, interpretation of cancer in terms of artificial life and artificial immune systems, swarm intelligence, cellular automata, computational systems biology, genetic networks, cellular computing,? validation through in vitro/vivo tumour models and tumour on chip devices.?The book is an inspiring blend of theoretical and experimental results, concepts and paradigms...Distinctive features..The book advances widely popular topics of? cancer origin, treatment and understanding of its progress..The book is comprised of uniq
出版日期Book 2022
關鍵詞Oncology; Computation; Complexity; Algorithms; Optimisation; Nanotechnology
版次1
doihttps://doi.org/10.1007/978-3-031-04379-6
isbn_softcover978-3-031-04381-9
isbn_ebook978-3-031-04379-6Series ISSN 2194-7287 Series E-ISSN 2194-7295
issn_series 2194-7287
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl
The information of publication is updating

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https://doi.org/10.1007/978-3-7908-1762-1ver, cancers exhibit an inherent evolving heterogeneity that requires an individual approach based on rigorous and precise predictions of cancer growth and treatment response. To this end, we advocate the use of quantitative in vivo imaging data to calibrate mathematical models for the personalized
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https://doi.org/10.1007/978-3-319-38915-8he network, regulated by p300 and HDAC1, is modeled by a system of 14 ordinary differential equations. Based on this model, an oncogenic condition of Mdm2 overexpression is derived and the functional suppression of p53 is simulated as one possible carcinogenic behavior. Then, control action is intro
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H?vard Haarstad,Cecilia Camperoions in tumour system to timing and dosage of new treatments testing. When properly calibrated and validated, models can significantly improve our knowledge and contribute to narrowing clinical trials. In this study, we used the ODE model that includes only stem cells (wild and mutated) and differen
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https://doi.org/10.1007/BFb0043058e small number of clinically approved nanoparticles. Nanoparticle design is evolving in complexity, yet most experimental methods cannot keep up since they lack the proper resolution for accurate characterization and testing necessary for clinical approval. The computational approach can advance res
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Telechelic oligomers by radical reactions,t differs from all previous known work using diploid representations. A form of the Baldwin effect has been identified as inherent to the evolutionary mechanisms of eukaryotes and a simplified version is presented here which maintains such behaviour. Using a well-known abstract tuneable model, it is
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