Mathematical and Computational Oncology

Second International Symposium, ISMCO 2020, San Diego, CA, USA, October 8–10, 2020, Proceedings

Häftad, Engelska, 2020

Av George Bebis, Max Alekseyev, Heyrim Cho, Jana Gevertz, Maria Rodriguez Martinez

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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.

Produktinformation

Utgivningsdatum2020-12-03
Mått155 x 235 x 9 mm
Vikt230 g
FormatHäftad
SpråkEngelska
SerieLecture Notes in Computer Science
Antal sidor119
FörlagSpringer Nature Switzerland AG
ISBN9783030645106

Tillhör följande kategorier

Systemvetenskap och AI inom Data och it
Biovetenskap inom Naturvetenskap och teknik
Artificiell intelligens inom Data och it

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.