Mathematical and Computational Oncology

1. Front Matter

   Pages i-xxii

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2. Invited Talk

   1. Front Matter

      Pages 1-1

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   2. Plasticity in Cancer Cell Populations: Biology, Mathematics and Philosophy of Cancer

      • Jean Clairambault

      Pages 3-9

3. Statistical and Machine Learning Methods for Cancer Research

   1. Front Matter

      Pages 11-11

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   2. CHIMERA: Combining Mechanistic Models and Machine Learning for Personalized Chemotherapy and Surgery Sequencing in Breast Cancer

      • Cristian Axenie, Daria Kurz

      Pages 13-24

   3. Fine-Tuning Deep Learning Architectures for Early Detection of Oral Cancer

      • Roshan Alex Welikala, Paolo Remagnino, Jian Han Lim, Chee Seng Chan, Senthilmani Rajendran, Thomas George Kallarakkal et al.

      Pages 25-31

   4. Discriminative Localized Sparse Representations for Breast Cancer Screening

      • Sokratis Makrogiannis, Chelsea E. Harris, Keni Zheng

      Pages 32-43

   5. Activation vs. Organization: Prognostic Implications of T and B Cell Features of the PDAC Microenvironment

      • Elliot Gray, Shannon Liudahl, Shamilene Sivagnanam, Courtney Betts, Jason Link, Dove Keith et al.

      Pages 44-55

   6. On the Use of Neural Networks with Censored Time-to-Event Data

      • Elvire Roblin, Paul-Henry Cournede, Stefan Michiels

      Pages 56-67

4. Mathematical Modeling for Cancer Research

   1. Front Matter

      Pages 69-69

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   2. tugHall: A Tool to Reproduce Darwinian Evolution of Cancer Cells for Simulation-Based Personalized Medicine

      • Iurii Nagornov, Jo Nishino, Mamoru Kato

      Pages 71-76

5. General Cancer Computational Biology

   1. Front Matter

      Pages 77-77

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   2. The Potential of Single Cell RNA-Sequencing Data for the Prediction of Gastric Cancer Serum Biomarkers

      • Kirill E. Medvedev, Anna V. Savelyeva, Aditya Bagrodia, Nick V. Grishin

      Pages 79-84

6. Posters

   1. Front Matter

      Pages 85-85

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   2. Theoretical Foundation of the Performance of Phylogeny-Based Somatic Variant Detection

      • Takuya Moriyama, Seiya Imoto, Satoru Miyano, Rui Yamaguchi

      Pages 87-101

   3. Detecting Subclones from Spatially Resolved RNA-Seq Data

      • Phillip B. Nicol

      Pages 102-107

   4. Novel Driver Synonymous Mutations in the Coding Regions of GCB Lymphoma Patients Improve the Transcription Levels of BCL2

      • Ofek Shami-Schnitzer, Zohar Zafir, Tamir Tuller

      Pages 108-118

7. Back Matter

   Pages 119-119

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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.

• artificial intelligence
• bioinformatics
• computational methods for anticancer drug development
• computer vision
• genetic algorithms
• image analysis
• image processing
• image processing for cancer research
• machine learning
• network protocols
• signal processing
• signal to noise ratio
• telecommunication systems
• wireless telecommunication systems

• University of Nevada Reno, Reno, USA

  George Bebis

• George Washington University, Ashburn, USA

  Max Alekseyev

• University of California, Riverside, Riverside, USA

  Heyrim Cho

• The College of New Jersey, Ewing, USA

  Jana Gevertz

• IBM Research - Zurich, Rüschlikon, Switzerland

  Maria Rodriguez Martinez

Policies and ethics