UID:
almahu_9949227929402882
Format:
XXI, 79 p. 33 illus., 31 illus. in color.
,
online resource.
Edition:
1st ed. 2021.
ISBN:
9783030912413
Series Statement:
Lecture Notes in Bioinformatics ; 13060
Content:
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.
Note:
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 eBook
Additional Edition:
Printed edition: ISBN 9783030912406
Additional Edition:
Printed edition: ISBN 9783030912420
Language:
English
DOI:
10.1007/978-3-030-91241-3
URL:
https://doi.org/10.1007/978-3-030-91241-3
