LEADER 01226nam a2200313 i 4500 001 991001973379707536 008 061122r19652005fr b 001 0 fre d 020 $a2204058203 035 $ab13456404-39ule_inst 040 $aBiblioteca Interfacoltà$bita 041 0 $afre$agrc$hgrc 082 0 $a239.1 240 10$aEpistula ad Diognetum.$lFrancese$955827 245 00$aA Diognète /$cintroduction, édition critique, traduction et commentaire par Henri Irénée Marrou 250 $a2. éd. revue et augmentée 260 $aParis :$bLes éditions du Cerf,$c2005 300 $a296 p. ;$c20 cm 440 0$aSources chrétiennes ;$v33 bis 500 $aRistampa dell'ed.: 1965 500 $aTesto greco a fronte 504 $aBibliografia: p. 43-46 650 4$aTeologia cristiana$xApologetica$xTempi apostolici 700 1 $aMarrou, Henri Irénée 907 $a.b13456404$b02-04-14$c22-11-06 912 $a991001973379707536 945 $aLE007 Sala A Sourc. Chrét. Epistula Diogn. 02$g1$i2007000111857$lle007$nLE007 2006 Ugenti PRIN$op$pE17.80$q-$rn$s- $t0$u1$v0$w1$x0$y.i14323060$z22-11-06 996 $aEpistula ad Diognetum$955827 997 $aUNISALENTO 998 $ale007$b01-01-00$cm$da $e-$ffre$gfr $h2$i0 LEADER 05118nam 22005655 450 001 9911007352003321 005 20250530130716.0 010 $a3-031-92206-9 024 7 $a10.1007/978-3-031-92206-0 035 $a(CKB)39124606500041 035 $a(DE-He213)978-3-031-92206-0 035 $a(MiAaPQ)EBC32142476 035 $a(Au-PeEL)EBL32142476 035 $a(OCoLC)1524424011 035 $a(EXLCZ)9939124606500041 100 $a20250530d2025 u| 0 101 0 $aeng 135 $aur||||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aArtificial Intelligence and Bioinformatics in Cancer: An Interdisciplinary Approach /$fedited by Nima Rezaei 205 $a1st ed. 2025. 210 1$aCham :$cSpringer Nature Switzerland :$cImprint: Springer,$d2025. 215 $a1 online resource (XI, 435 p. 97 illus., 94 illus. in color.) 225 1 $aInterdisciplinary Cancer Research,$x2731-457X ;$v18 311 08$a3-031-92205-0 327 $aDigital 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. 330 $aThe ?Artificial Intelligence and Bioinformatics in Cancer: An Interdisciplinary Approach? is the eighteenth volume of the ?Interdisciplinary Cancer Research? series, publishes comprehensive volume on the advances of machine learning and bioinformatics in cancer. The volume starts with a chapter on application of artificial intelligence for early diagnosis of cancer. Then digital health technologies in cancer care and research is discussed. Unveiling cancer complexity: machine learning insights into multi-omics data and the role of integrated bioinformatics in cancer research are also discussed. In silico and biophysical approaches in cancer research and in silico methods and targeted receptors used in cancer studies are explained in the following chapters. The modeling uncertain growth and diffusion in cancer tumors with heterogeneous cell mutations, imaging tumor metabolism and its heterogeneity with special focus on radiomics and artificial intelligence are also discussed. Mathematical modeling of cancer tumor dynamics as well as recent advances in artificial intelligence for cancer treatment are presented, while signature-based drug repositioning for drug discovery employing machine learning tools is also discussed. After a chapter on mathematical analysis of cancer-tumor models, the subsequent chapters discuss on the role of artificial intelligence in colorectal cancer, breast cancer, lung cancer, brain tumor, and cervical cancer. This is the main concept of Cancer Immunology Project (CIP), which is a part of Universal Scientific Education and Research Network (USERN). This interdisciplinary book will be of special value for oncologists who wish to have an update on application of artificial intelligence in diagnosis and treatment of cancers. 410 0$aInterdisciplinary Cancer Research,$x2731-457X ;$v18 606 $aCancer 606 $aOncology 606 $aCancer Biology 606 $aOncology 606 $aCancers 615 0$aCancer. 615 0$aOncology. 615 14$aCancer Biology. 615 24$aOncology. 615 24$aCancers. 676 $a571.978 676 $a616.994 702 $aRezaei$b Nima$4edt$4http://id.loc.gov/vocabulary/relators/edt 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9911007352003321 996 $aArtificial Intelligence and Bioinformatics in Cancer: An Interdisciplinary Approach$94389703 997 $aUNINA