01128nam a22002531i 450099100054728970753620021016105212.0021016s1949 it |||||||||||||||||ita b12020849-39ule_instARCHE-011064ExLDip.to Filologia Ling. e Lett.itaA.t.i. Arché s.c.r.l. Pandora Sicilia s.r.l.Sada, Luigi242339L'elemento storico topografico nella genesi delle leggende del Salento /Luigi Sada ; prefazione di Francesco BabudriToritto :F. Pecoraro,1949151 p. ;25 cmSalentoLeggendeBabudri, Francesco.b1202084903-10-1801-04-03991000547289707536LE008 FL.M. (TR.P.) I G 3412008000359676le008-E0.00-no 01010.i1230947301-04-03LE001 AN V 17212001000193165le001gE5.16-l- 00000.i1586259803-10-18Elemento storico topografico nella genesi delle leggende del Salento142358UNISALENTOle00801-04-03ma -itait 2104544nam 2201201z- 450 991059507740332120231214133242.0(CKB)5680000000080752(oapen)https://directory.doabooks.org/handle/20.500.12854/92045(EXLCZ)99568000000008075220202209d2022 |y 0engurmn|---annantxtrdacontentcrdamediacrrdacarrierBioinformatics and Machine Learning for Cancer BiologyBaselMDPI Books20221 electronic resource (196 p.)3-0365-4814-9 3-0365-4813-0 Cancer is a leading cause of death worldwide, claiming millions of lives each year. Cancer biology is an essential research field to understand how cancer develops, evolves, and responds to therapy. By taking advantage of a series of “omics” technologies (e.g., genomics, transcriptomics, and epigenomics), computational methods in bioinformatics and machine learning can help scientists and researchers to decipher the complexity of cancer heterogeneity, tumorigenesis, and anticancer drug discovery. Particularly, bioinformatics enables the systematic interrogation and analysis of cancer from various perspectives, including genetics, epigenetics, signaling networks, cellular behavior, clinical manifestation, and epidemiology. Moreover, thanks to the influx of next-generation sequencing (NGS) data in the postgenomic era and multiple landmark cancer-focused projects, such as The Cancer Genome Atlas (TCGA) and Clinical Proteomic Tumor Analysis Consortium (CPTAC), machine learning has a uniquely advantageous role in boosting data-driven cancer research and unraveling novel methods for the prognosis, prediction, and treatment of cancer.Research & information: generalbicsscBiology, life sciencesbicssctumor mutational burdenDNA damage repair genesimmunotherapybiomarkerbiomedical informaticsbreast cancerestrogen receptor alphapersistent organic pollutantsdrug-drug interaction networksmolecular dockingNGSctDNAVAFliquid biopsyfilteringvariant callingDEGsdiagnosisovarian cancerPUS7RMGsCPA4bladder urothelial carcinomaimmune cellsT cell exhaustioncheckpointarchitectural distortionimage processingdepth-wise convolutional neural networkmammographybladder cancerAnnexin familysurvival analysisprognostic signaturetherapeutic targetR Shiny applicationRNA-seqproteomicsmulti-omics analysisT-cell acute lymphoblastic leukemiaCCLEsitagliptinthyroid cancer (THCA)papillary thyroid cancer (PTCa)thyroidectomymetastasisdrug resistancebiomarker identificationtranscriptomicsmachine learningpredictionvariable selectionmajor histocompatibility complexbidirectional long short-term memory neural networkdeep learningcancerincidencemortalitymodelingforecastingGoogle TrendsRomaniaARIMATBATSNNARResearch & information: generalBiology, life sciencesWan Shibiaoedt1128379Fan YipingedtJiang ChunjieedtLi ShengliedtWan ShibiaoothFan YipingothJiang ChunjieothLi ShengliothBOOK9910595077403321Bioinformatics and Machine Learning for Cancer Biology3035011UNINA02854nam 2200733z- 450 991055761740332120220321(CKB)5400000000045227(oapen)https://directory.doabooks.org/handle/20.500.12854/79575(oapen)doab79575(EXLCZ)99540000000004522720202203d2022 |y 0engurmn|---annantxtrdacontentcrdamediacrrdacarrierUnderstanding School Success of Migrant Students: An International PerspectiveBaselMDPI - Multidisciplinary Digital Publishing Institute20221 online resource (106 p.)3-0365-3100-9 3-0365-3101-7 The aim of this book is to empirically identify the school success pathways of migrants for policy actions in schools and communities in order to tackle barriers to migrant students' school success. These resilience pathways highlight differences in individual and social risks and identify protective factors for young migrants to overcome obstacles linked to discrimination and low educational outcomes. It presents international empirical research comparing and explaining school success factors for migrant students in various countries, namely, Germany, Greece, Russia, and Switzerland.Understanding School Success of Migrant StudentsResearch & information: generalbicsscacademic achievementacademic self-conceptacademic supportacculturationadjustmentbicultural identityethnic identityimmigrant childrenimmigrant studentsinclusioninequality at schoolintersectionalitylanguage supportmigrationminority youthn/anational identityrecognition by peersrecognition by teacherrecognitive justiceresilienceschool engagementschool successself-esteemsuccess at schoolteachers' educational practicesupper secondary educationVET educationwelcoming school climateyouthResearch & information: generalMakarova Elenaedt1297541Kassis WassilisedtMakarova ElenaothKassis WassilisothBOOK9910557617403321Understanding School Success of Migrant Students: An International Perspective3024516UNINA