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Mathematical and Computational Oncology [electronic resource] : Third International Symposium, ISMCO 2021, Virtual Event, October 11-13, 2021, Proceedings / edited by George Bebis, Terry Gaasterland, Mamoru Kato, Mohammad Kohandel, Kathleen Wilkie.

Contributor(s): Bebis, George [editor.] | Gaasterland, Terry [editor.] | Kato, Mamoru [editor.] | Kohandel, Mohammad [editor.] | Wilkie, Kathleen [editor.] | SpringerLink (Online service).
Material type: materialTypeLabelBookSeries: Lecture Notes in Bioinformatics: 13060Publisher: Cham : Springer International Publishing : Imprint: Springer, 2021Edition: 1st ed. 2021.Description: XXI, 79 p. 33 illus., 31 illus. in color. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9783030912413.Subject(s): Computer vision | Computer engineering | Computer networks  | Computer Vision | Computer Engineering and Networks | Computer Engineering and NetworksAdditional physical formats: Printed edition:: No title; Printed edition:: No titleDDC classification: 006.37 Online resources: Click here to access online
Contents:
Statistical and Machine Learning Methods for Cancer Research Image Classification of Skin Cancer: Using Deep Learning as a Tool for Skin Self-Examinations -- Predictive Signatures for Lung Adenocarcinoma Prognostic Trajectory by Omics Data Integration and Ensemble Learning -- The Role of Hydrophobicity in Peptide-MHC Binding -- Spatio-temporal tumor modeling and simulation Simulating cytotoxic T-lymphocyte & cancer cells interactions : An LSTM-based approach to surrogate an agent-based model -- General cancer computational biology Strategies to reduce long-term drug resistance by considering effects of differential selective treatments -- Mathematical Modeling for Cancer Research Improved Geometric Configuration for the Bladder Cancer BCG-based Immunotherapy Treatment Model -- Computational methods for anticancer drug development Run for your life - an integrated virtual tissue platform for incorporating exercise oncology into immunotherapy.
In: Springer Nature eBookSummary: This book constitutes the refereed proceedings of the Third International Symposium on Mathematical and Computational Oncology, ISMCO 2021, held in October 2021. Due to COVID-19 pandemic the conference was held virtually. The 3 full papers and 4 short papers presented were carefully reviewed and selected from 20 submissions. The papers are organized in topical sections named: statistical and machine learning methods for cancer research; mathematical modeling for cancer research; spatio-temporal tumor modeling and simulation; general cancer computational biology; mathematical modeling for cancer research; computational methods for anticancer drug development.
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Statistical and Machine Learning Methods for Cancer Research Image Classification of Skin Cancer: Using Deep Learning as a Tool for Skin Self-Examinations -- Predictive Signatures for Lung Adenocarcinoma Prognostic Trajectory by Omics Data Integration and Ensemble Learning -- The Role of Hydrophobicity in Peptide-MHC Binding -- Spatio-temporal tumor modeling and simulation Simulating cytotoxic T-lymphocyte & cancer cells interactions : An LSTM-based approach to surrogate an agent-based model -- General cancer computational biology Strategies to reduce long-term drug resistance by considering effects of differential selective treatments -- Mathematical Modeling for Cancer Research Improved Geometric Configuration for the Bladder Cancer BCG-based Immunotherapy Treatment Model -- Computational methods for anticancer drug development Run for your life - an integrated virtual tissue platform for incorporating exercise oncology into immunotherapy.

This book constitutes the refereed proceedings of the Third International Symposium on Mathematical and Computational Oncology, ISMCO 2021, held in October 2021. Due to COVID-19 pandemic the conference was held virtually. The 3 full papers and 4 short papers presented were carefully reviewed and selected from 20 submissions. The papers are organized in topical sections named: statistical and machine learning methods for cancer research; mathematical modeling for cancer research; spatio-temporal tumor modeling and simulation; general cancer computational biology; mathematical modeling for cancer research; computational methods for anticancer drug development.

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