03145nam 22006373 450 991111994740332120250313080342.09783839471463383947146X10.1515/9783839471463(MiAaPQ)EBC31953928(Au-PeEL)EBL31953928(CKB)37819881800041(DE-B1597)677930(DE-B1597)9783839471463(OCoLC)1511113454(ScCtBLL)cb647e36-3958-470c-8595-fca02df0f30b(DE-B1597)BR1301155(OCoLC)1511113454(EXLCZ)993781988180004120250313d2025 uy 0gerurcnu||||||||txtrdacontentcrdamediacrrdacarrierUn/Reale Interaktionsräume Formen Sozialer Ordnung Im Spektrum Medienspezifischer Interaktion1st ed.Bielefeld :transcript Verlag,2025.©2024.1 online resource (271 pages)Edition Medienwissenschaft ;110Frontmatter -- Inhalt -- Danksagung -- Un/Reale Interaktionsräume -- Sektion 1: Konstruktion -- Orbital Mirage -- »I am, in fact, a person.« -- Sprachassistenzsysteme und ihre Interfaces -- Zwischen Technologie und Ideologie -- Sektion 2: Erfahrung -- L.I.S.A.: Akt 1 -- Von der Non-Linearität der Zeit -- Erfahrungsraum Interaktion -- Ludische Dividuationen -- Sektion 3: Partizipation -- Excuse Us While We Improve Your View, Atlantis -- Mobile Crowdsensing -- Digital Fashion im Online-Shop -- Appropriating the Ape -- Die Autor*innenWie entstehen medienspezifische Interaktionsräume und wie wirken sie sich auf Formen sozialer Ordnung aus? Die Beiträger*innen des Bandes betrachten das Spektrum medienspezifischer Interaktion und unterstreichen dabei die Komplexität der wechselseitig verbundenen Schnittstellen zwischen Menschen und Maschinen. Ansätze aus Medienwissenschaft, -linguistik und -kunst machen sozio-kulturelle Transformationen sichtbar, die durch oder in diesen sich prozessual entfaltenden Interaktionsräumen als Un/Realitäten entstehen. Der Band eröffnet damit einen interdisziplinären Blick auf die Entstehung, Gestaltung und Bedingung von medienspezifischen Interaktionen.SOCIAL SCIENCE / Media StudiesbisacshLinguistik.Soziale Medien.Technologie.Technology, social media, aesthetics, digitality, linguistics._Digitalität.Ästhetik.SOCIAL SCIENCE / Media Studies.CC 7270rvkKindler-Mathôt Clara1799554Leblebici Didem1799555Marinsalta Giacomo1799556Rückwart Till1799557Zaglyadnova Anna1799558Nanni Giacomo1799559MiAaPQMiAaPQMiAaPQBOOK9911119947403321Un4343575UNINA05477nam 2200709Ia 450 991113976120332120251116191433.01-280-74728-597866107472830-08-046803-9(CKB)1000000000357713(EBL)284006(OCoLC)181845279(SSID)ssj0000216939(PQKBManifestationID)11185759(PQKBTitleCode)TC0000216939(PQKBWorkID)10197825(PQKB)10244408(Au-PeEL)EBL284006(CaPaEBR)ebr10158401(CaONFJC)MIL74728(MiAaPQ)EBC284006(EXLCZ)99100000000035771320061222d2007 uy 0engur|n|---|||||txtccrOutcome prediction in cancer /editors, Azzam F.G. Taktak and Anthony C. Fisher1st ed.Amsterdam ;Boston Elsevier20071 online resource (483 p.)Description based upon print version of record.0-444-52855-5 Includes bibliographical references and index.Front cover; Title page; Copyright page; Foreword; Table of Contents; Contributors; Introduction; Section 1: The Clinical Problem; Chapter 1: The Predictive Value of Detailed Histological Staging of Surgical Resection Specimens in Oral Cancer; 1. INTRODUCTION; 2. PREDICTIVE FEATURES RELATED TO THE PRIMARY TUMOUR; 3. PREDICTIVE FEATURES RELATED TO THE REGIONAL LYMPH NODES; 4. DISTANT (SYSTEMIC) METASTASES; 5. GENERAL PATIENT FEATURES; 6. MOLECULAR AND BIOLOGICAL MARKERS; 7. THE WAY AHEAD?; REFERENCES; Chapter 2: Survival after Treatment of Intraocular Melanoma; 1. INTRODUCTION2. INTRAOCULAR MELANOMA3. STATISTICAL METHODS FOR PREDICTING METASTATIC DISEASE; 4. PREDICTING METASTATIC DEATH WITH NEURAL NETWORKS; 5. MISCELLANEOUS ERRORS; 6. A NEURAL NETWORK FOR PREDICTING SURVIVAL IN UVEAL MELANOMA PATIENTS; 7. CAVEATS REGARDING INTERPRETATION OF SURVIVAL STATISTICS; 8. FURTHER STUDIES; 9. CONCLUSIONS; REFERENCES; Chapter 3: Recent Developments in Relative Survival Analysis; 1. INTRODUCTION; 2. CAUSE-SPECIFIC SURVIVAL; 3. INDEPENDENCE ASSUMPTION; 4. EXPECTED SURVIVAL; 5. RELATIVE SURVIVAL; 6. POINT OF CURE; 7. REGRESSION ANALYSIS; 8. PERIOD ANALYSIS9. AGE STANDARDIZATION10. PARAMETRIC METHODS; 11. MULTIPLE TUMOURS; 12. CONCLUSION; REFERENCES; Section 2: Biological and Genetic Factors; Chapter 4: Environmental and Genetic Risk Factors of Lung Cancer; 1. INTRODUCTION; 2. LUNG CANCER INCIDENCE AND MORTALITY; 3. CONCLUSION; REFERENCES; Chapter 5: Chaos, Cancer, the Cellular Operating System and the Prediction of Survival in Head and Neck Cancer; 1. INTRODUCTION; 2. CANCER AND ITS CAUSATION; 3. FUNDAMENTAL CELL BIOLOGY AND ONCOLOGY; 4. A NEW DIRECTION FOR FUNDAMENTAL CELL BIOLOGY AND ONCOLOGY5. COMPLEX SYSTEMS ANALYSIS AS APPLIED TO BIOLOGICAL SYSTEMS AND SURVIVAL ANALYSIS6. METHODS OF ANALYSING FAILURE IN BIOLOGICAL SYSTEMS; 7. A COMPARISON OF A NEURAL NETWORK WITH COX'S REGRESSION IN PREDICTING SURVIVAL IN OVER 800 PATIENTS; 8. THE NEURAL NETWORK AND FUNDAMENTAL BIOLOGY AND ONCOLOGY; 9. THE DIRECTION OF FUTURE WORK; 10. SUMMARY; REFERENCES; Section 3: Mathematical Background of Prognostic Models; Chapter 6: Flexible Hazard Modelling for Outcome Prediction in Cancer: Perspectives for the Use of Bioinformatics Knowledge; 1. INTRODUCTION; 2. FAILURE TIME DATA3. PARTITION AND GROUPING OF FAILURE TIMES4. COMPETING RISKS; 5. GLMs AND FFANNs; 6. APPLICATIONS TO CANCER DATA; 7. CONCLUSIONS; REFERENCES; Chapter 7: Information Geometry for Survival Analysis and Feature Selection by Neural Networks; 1. INTRODUCTION; 2. SURVIVAL FUNCTIONS; 3. STANDARD MODELS FOR SURVIVAL ANALYSIS; 4. THE NEURAL NETWORK MODEL; 5. LEARNING IN THE CPENN MODEL; 6. THE BAYESIAN APPROACH TO MODELLING; 7. VARIABLE SELECTION; 8. THE LAYERED PROJECTION ALGORITHM; 9. A SEARCH STRATEGY; 10. EXPERIMENTS; 11. CONCLUSION; REFERENCESChapter 8: Artificial Neural Networks Used in the Survival Analysis of Breast Cancer Patients: A Node-Negative StudyThis book is organized into 4 sections, each looking at the question of outcome prediction in cancer from a different angle. The first section describes the clinical problem and some of the predicaments that clinicians face in dealing with cancer. Amongst issues discussed in this section are the TNM staging, accepted methods for survival analysis and competing risks. The second section describes the biological and genetic markers and the rôle of bioinformatics. Understanding of the genetic and environmental basis of cancers will help in identifying high-risk populations and developing effectivCancerDiagnosisCancerPrognosisNeural networks (Computer science)Survival analysis (Biometry)CancerDiagnosis.CancerPrognosis.Neural networks (Computer science)Survival analysis (Biometry)362.196994616.994616.994075Taktak Azzam F. GFisher Anthony C.Dr.MiAaPQMiAaPQMiAaPQBOOK9911139761203321UNINA