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| Link zu diesem Datensatz | https://d-nb.info/1367332184 |
| Titel | Artificial Intelligence and Bioinformatics in Cancer: An Interdisciplinary Approach / edited by Nima Rezaei |
| Person(en) | Rezaei, Nima (Herausgeber) |
| Organisation(en) | SpringerLink (Online service) (Sonstige) |
| Ausgabe | 1st ed. 2025 |
| Verlag | Cham : Springer Nature Switzerland, Imprint: Springer |
| Zeitliche Einordnung | Erscheinungsdatum: 2025 |
| Umfang/Format | Online-Ressource, XI, 435 p. 97 illus., 94 illus. in color. : online resource. |
| Andere Ausgabe(n) |
Printed edition:: ISBN: 978-3-031-92205-3 Printed edition:: ISBN: 978-3-031-92207-7 Printed edition:: ISBN: 978-3-031-92208-4 |
| Inhalt | Digital Pathology and Artificial Intelligence for Early Diagnosis of Pediatric Solid Tumors: Implication For Improved Healthcare Strategies -- Digital Health Technologies in Cancer Care and Research -- Unveiling Cancer Complexity: Machine Learning Insights into Multi-Omics Data -- The Role of Integrated Bioinformatics in Cancer Research: Transforming Genomic Insights into Precision Medicine -- In Silico and Biophysical Techniques in Anticancer Drug Discovery Research -- In Silico Methods and Targeted Receptors Used in Cancer Studies -- Modeling Uncertain Growth and Diffusion in Cancer Tumors with Heterogeneous Cell Mutations -- Imaging Tumor Metabolism and Its Heterogeneity: Special Focus on Radiomics and AI -- Mathematical Modeling of Cancer Tumor Dynamics with Multiple Fuzzification Approaches in Fractional Environment -- Is Cancer Our Equal or Our Better? Artificial Intelligence in Cancer Drug Discovery -- Recent Advances in Artificial Intelligence and Cancer Treatment -- Signature-Based Drug Repositioning: Tackling Speeding Up Drug Discovery of Anticancer Drugs Employing Recently Developed Machine Learning Tools -- Mathematical Analysis of Cancer-Tumor Models with Variable Depression Effects and Integrated Treatment Strategies -- Emerging Role of Artificial Intelligence in Colorectal Cancer: Screening and Diagnosis -- Measuring the Performance of Supervised Machine Learning Approaches Using Cancer Data -- VRTumor: Integrating AI-Based Segmentation with Virtual Reality for Precise Tumor Analysis. Artificial Intelligence Applications to Detect Pediatric Brain Tumor Biomarkers |
| Persistent Identifier |
URN: urn:nbn:de:101:1-2505310414167.375268522260 DOI: 10.1007/978-3-031-92206-0 |
| URL | https://doi.org/10.1007/978-3-031-92206-0 |
| ISBN/Einband/Preis | 978-3-031-92206-0 |
| Sprache(n) | Englisch (eng) |
| Beziehungen | Interdisciplinary Cancer Research ; 18 |
| DDC-Notation | 616.994 (maschinell ermittelte DDC-Kurznotation) |
| Sachgruppe(n) | 610 Medizin, Gesundheit |
| Online-Zugriff | Archivobjekt öffnen |
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