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Mathematical and Computational Oncology de George Bebis

Mathematical and Computational Oncology: Lecture Notes in Computer Science, cartea 12508

Editat de George Bebis, Max Alekseyev, Heyrim Cho, Jana Gevertz, Maria Rodriguez Martinez
en Limba Engleză Paperback – 3 dec 2020
This book constitutes the refereed proceedings of the Second International Symposium on Mathematical and Computational Oncology, ISMCO 2020, which was supposed to be held in San Diego, CA, USA, in October 2020, but was instead held virtually due to the COVID-19 pandemic. The 6 full papers and 4 short papers presented together with 1 invited talk were carefully reviewed and selected from 28 submissions. The papers are organized in topical sections named: statistical and machine learning methods for cancer research; mathematical modeling for cancer research; general cancer computational biology; and posters.
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Specificații

ISBN-13: 9783030645106
ISBN-10: 303064510X
Pagini: 144
Ilustrații: XXII, 119 p. 34 illus., 25 illus. in color.
Dimensiuni: 155 x 235 x 9 mm
Greutate: 0.23 kg
Ediția:1st edition 2020
Editura: Springer
Colecția Lecture Notes in Computer Science
Seria Lecture Notes in Computer Science

Locul publicării:Cham, Switzerland

Cuprins

Invited.- Plasticity in cancer cell populations: biology, mathematics and philosophy of cancer.- Statistical and Machine Learning Methods for Cancer Research.- CHIMERA: Combining Mechanistic Models and Machine Learning for Personalized Chemotherapy and Surgery Sequencing in Breast Cancer.- Fine-Tuning Deep Learning Architectures for Early Detection of Oral Cancer.- Discriminative Localized Sparse Representations for Breast Cancer Screening.- Activation vs. Organization: Prognostic Implications of T and B cell Features of the PDAC Microenvironment.- On the use of neural networks with censored time-to-event data.- Mathematical Modeling for Cancer Research.- tugHall: a tool to reproduce Darwinian evolution of cancer cells for simulation-based personalized medicine.- General Cancer Computational Biology.- The potential of single cell RNA-sequencing data for the prediction of gastric cancer serum biomarkers.- Poster.- Theoretical Foundation of the Performance of Phylogeny-Based Somatic Variant Detection.- Detecting subclones from spatially resolved RNA-seq data.- Novel driver synonymous mutations in the coding regions of GCB lymphoma patients improve the transcription levels of BCL2.