Info
- Utilizzare la checkbox di selezione a fianco di ciascun documento per attivare le funzionalità di stampa, invio email, download nei formati disponibili del (i) record.
Mechanisms and Novel Therapeutic Approaches for Gynecologic Cancer
| Mechanisms and Novel Therapeutic Approaches for Gynecologic Cancer |
| Autore | Nakayama Naomi |
| Pubbl/distr/stampa | Basel, : MDPI - Multidisciplinary Digital Publishing Institute, 2022 |
| Descrizione fisica | 1 online resource (258 p.) |
| Soggetto topico | Public health and preventive medicine |
| Soggetto non controllato |
adoptive immunotherapy
anti-angiogenic therapy aryl hydrocarbon receptor BEN (BANP, E5R and NAC1) domain biological planning biomarkers borderline ovarian tumors BRCA1 mutant ovarian cancer cancer progression CD44 cervical cancer cervical neoplasia chimeric antigen receptor clear cell carcinoma differentially expressed genes DNA damage response DNA mismatch repair (MMR) drug repurposing endometrial cancer endometrial carcinoma endometrioid endometrial cancer epithelial ovarian cancer epithelial ovarian cancers epithelial-mesenchymal transition exosome gene ontology gynecological cancer HDR brachytherapy human cytomegalovirus immune checkpoints inhibitors immune micro-environment immunohistochemistry immunosuppression in vivo dosimetry inflammation integrative analysis interventional radiotherapy isothermal titration calorimetry (ITC) liquid biopsy long-term survival melanoma treatment microsatellite instability miRNA mismatch repair deficiency MMR deficient (dMMR) n/a nanocarriers NK cells non-coding RNAs nucleus accumbens-associated protein 1 (NAC1) ovarian cancer p16 protein pGSK3β PLA2G7) platelet-activating factor acetylhydrolase (PAF-AH prognosis sequence-specific DNA-binding protein solution NMR structure somatic mutation targeted therapy testin vaginal melanoma vaginal-cuff brachytherapy vulvar melanoma Wnt signaling β-catenin |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910580209103321 |
Nakayama Naomi
|
||
| Basel, : MDPI - Multidisciplinary Digital Publishing Institute, 2022 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Statistical Methods for the Analysis of Genomic Data
| Statistical Methods for the Analysis of Genomic Data |
| Autore | Jiang Hui |
| Pubbl/distr/stampa | Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2020 |
| Descrizione fisica | 1 online resource (136 p.) |
| Soggetto topico |
Mathematics and Science
Research and information: general |
| Soggetto non controllato |
Bayes factor
Bayesian mixed-effect model boosting classification classification boundary clustering analysis convolutional neural networks CpG sites deep learning DNA methylation expectation-maximization algorithm false discovery rate control feed-forward neural networks gaussian finite mixture model GEE gene expression gene regulatory network gene set enrichment analysis integrative analysis kernel method lipid-environment interaction longitudinal lipidomics study machine learning multiple cancer types n/a network substructure nonparanormal graphical model omics data Ordinal responses penalized variable selection prognosis modeling RNA-seq uncertainty |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910557545803321 |
|
Jiang Hui
|
||
| Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2020 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||