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Mathematical and Computational Oncology: Third International Symposium, ISMCO 2021, Virtual Event, October 1113, 2021, Proceedings 1st ed. 2021 [Mīkstie vāki]

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  • Formāts: Paperback / softback, 79 pages, height x width: 235x155 mm, weight: 174 g, 31 Illustrations, color; 2 Illustrations, black and white; XXI, 79 p. 33 illus., 31 illus. in color., 1 Paperback / softback
  • Sērija : Lecture Notes in Bioinformatics 13060
  • Izdošanas datums: 12-Dec-2021
  • Izdevniecība: Springer Nature Switzerland AG
  • ISBN-10: 303091240X
  • ISBN-13: 9783030912406
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  • Mīkstie vāki
  • Cena: 51,37 €*
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Mathematical and Computational Oncology: Third International Symposium, ISMCO 2021, Virtual Event, October 1113, 2021, Proceedings 1st ed. 2021Palielināt
  • Formāts: Paperback / softback, 79 pages, height x width: 235x155 mm, weight: 174 g, 31 Illustrations, color; 2 Illustrations, black and white; XXI, 79 p. 33 illus., 31 illus. in color., 1 Paperback / softback
  • Sērija : Lecture Notes in Bioinformatics 13060
  • Izdošanas datums: 12-Dec-2021
  • Izdevniecība: Springer Nature Switzerland AG
  • ISBN-10: 303091240X
  • ISBN-13: 9783030912406
Citas grāmatas par šo tēmu:
Permanent link: https://www.kriso.lv/db/9783030912406.html
Keywords:
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.

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.