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Titlebook: Mathematical and Computational Oncology; First International George Bebis,Takis Benos,Ernesto Lima Conference proceedings 2019 Springer Na

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书目名称Mathematical and Computational Oncology
副标题First International
编辑George Bebis,Takis Benos,Ernesto Lima
视频video
丛书名称Lecture Notes in Computer Science
图书封面Titlebook: Mathematical and Computational Oncology; First International  George Bebis,Takis Benos,Ernesto Lima Conference proceedings 2019 Springer Na
描述.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)..
出版日期Conference proceedings 2019
关键词artificial intelligence; Bayes theorem; bioinformatics; clustering algorithms; image analysis; image proc
版次1
doihttps://doi.org/10.1007/978-3-030-35210-3
isbn_softcover978-3-030-35209-7
isbn_ebook978-3-030-35210-3Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightSpringer Nature Switzerland AG 2019
The information of publication is updating

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https://doi.org/10.1007/978-3-030-35210-3artificial intelligence; Bayes theorem; bioinformatics; clustering algorithms; image analysis; image proc
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Modeling the Evolution of Ploidy in a Resource Restricted Environmenttoma (grade IV). Progression of lower-grade gliomas (LGG) to Glioblastoma (GBM) is accompanied by a phenotypic switch to a highly invasive tumor cell phenotype. Converging evidence from different cancer types, including colorectal-, breast-, and lung- cancers, suggests a strong enrichment of high pl
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Accurate and Flexible Bayesian Mutation Call from Multi-regional Tumor Samples To improve detection performance, our method is based on the assumption of mutation sharing: if we can predict at least one tumor region has the mutation, then we can be more confident to detect a mutation in more tumor regions by lowering the original threshold of detection. We find two drawbacks
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