Nothing Special   »   [go: up one dir, main page]

Mancini et al., 2020 - Google Patents

The bladder EpiCheck test as a non-invasive tool based on the identification of DNA methylation in bladder cancer cells in the urine: a review of published evidence

Mancini et al., 2020

View HTML
Document ID
8435188577806159024
Author
Mancini M
Righetto M
Zumerle S
Montopoli M
Zattoni F
Publication year
Publication venue
International journal of molecular sciences

External Links

Snippet

Recently, there has been a great effort to develop tests based on non-invasive urinary biomarkers (NMIBCs). These tests are based on the fact that NMIBCs are heterogeneous at the molecular level and can be divided into different molecular groups useful to predict …
Continue reading at www.mdpi.com (HTML) (other versions)

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F19/00—Digital computing or data processing equipment or methods, specially adapted for specific applications
    • G06F19/10—Bioinformatics, i.e. methods or systems for genetic or protein-related data processing in computational molecular biology
    • G06F19/28—Bioinformatics, i.e. methods or systems for genetic or protein-related data processing in computational molecular biology for programming tools or database systems, e.g. ontologies, heterogeneous data integration, data warehousing or computing architectures
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F19/00—Digital computing or data processing equipment or methods, specially adapted for specific applications
    • G06F19/30—Medical informatics, i.e. computer-based analysis or dissemination of patient or disease data
    • G06F19/32—Medical data management, e.g. systems or protocols for archival or communication of medical images, computerised patient records or computerised general medical references
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F19/00—Digital computing or data processing equipment or methods, specially adapted for specific applications
    • G06F19/10—Bioinformatics, i.e. methods or systems for genetic or protein-related data processing in computational molecular biology
    • G06F19/24—Bioinformatics, i.e. methods or systems for genetic or protein-related data processing in computational molecular biology for machine learning, data mining or biostatistics, e.g. pattern finding, knowledge discovery, rule extraction, correlation, clustering or classification
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Systems or methods specially adapted for a specific business sector, e.g. utilities or tourism
    • G06Q50/10—Services
    • G06Q50/22—Health care, e.g. hospitals; Social work
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by the preceding groups
    • G01N33/48—Investigating or analysing materials by specific methods not covered by the preceding groups biological material, e.g. blood, urine; Haemocytometers
    • G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Systems or methods specially adapted for a specific business sector, e.g. utilities or tourism
    • G06Q50/01—Social networking
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N99/00—Subject matter not provided for in other groups of this subclass
    • G06N99/005—Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run

Similar Documents

Publication Publication Date Title
Mancini et al. The bladder EpiCheck test as a non-invasive tool based on the identification of DNA methylation in bladder cancer cells in the urine: a review of published evidence
Maggi et al. SelectMDx and multiparametric magnetic resonance imaging of the prostate for men undergoing primary prostate biopsy: a prospective assessment in a multi-institutional study
Ruffion et al. PCA3 and PCA3-based nomograms improve diagnostic accuracy in patients undergoing first prostate biopsy
Divate et al. Deep learning-based pan-cancer classification model reveals tissue-of-origin specific gene expression signatures
Modhukur et al. Machine learning approaches to classify primary and metastatic cancers using tissue of origin-based DNA methylation profiles
Kalasauskas et al. Identification of high-risk atypical meningiomas according to semantic and radiomic features
Kaushik et al. A machine learning-based framework for the prediction of cervical cancer risk in women
Mazo et al. Application of artificial intelligence techniques to predict risk of recurrence of breast cancer: a systematic review
Akbulut et al. Prediction of perforated and nonperforated acute appendicitis using machine learning-based explainable artificial intelligence
Choi et al. Atypical histiocytoid cells and multinucleated giant cells in fine-needle aspiration cytology of the thyroid predict lymph node metastasis of papillary thyroid carcinoma
Consiglio et al. Explaining ovarian cancer gene expression profiles with fuzzy rules and genetic algorithms
Committeri et al. Support tools in the differential diagnosis of salivary gland tumors through inflammatory biomarkers and radiomics metrics: a preliminary study
Morote et al. Multiparametric magnetic resonance imaging grades the aggressiveness of prostate cancer
Di Pierro et al. Comparison of four validated nomograms (memorial Sloan Kettering cancer center, briganti 2012, 2017, and 2019) predicting lymph node invasion in patients with high-risk prostate cancer candidates for radical prostatectomy and extended pelvic lymph node dissection: clinical experience and review of the literature
Allenet et al. Can pre-operative neutrophil-to-lymphocyte ratio (NLR) help predict non-metastatic renal carcinoma recurrence after nephrectomy?(UroCCR-61 Study)
Pchejetski et al. Circulating chromosome conformation signatures significantly enhance PSA positive predicting value and overall accuracy for prostate cancer detection
Rodrigo et al. Thermal Liquid Biopsy (TLB): A predictive score derived from serum thermograms as a clinical tool for screening lung cancer patients
Gentile et al. A neural network model combining [-2] proPSA, freePSA, total PSA, cathepsin D, and thrombospondin-1 showed increased accuracy in the identification of clinically significant prostate cancer
Liu et al. A Systematic Review on Prostate-Specific Membrane Antigen Positron Emission Tomography (PSMA PET) Evaluating Localized Low-to Intermediate-Risk Prostate Cancer: A Tool to Improve Risk Stratification for Active Surveillance?
Pershad et al. Using naïve bayesian analysis to determine imaging characteristics of KRAS mutations in metastatic colon cancer
Rummel et al. Should genetic testing for cancer predisposition be standard-of-Care for women with invasive breast cancer? The murtha cancer center experience
Kang et al. Lymph Node Ratio Predicts Recurrence in Patients with Papillary Thyroid Carcinoma with Low Lymph Node Yield
Ferriero et al. The Impact of Metastasectomy on Survival Outcomes of Renal Cell Carcinoma: A 10-Year Single Center Experience
Morris et al. Automated computational detection of disease activity in ANCA-associated glomerulonephritis using Raman spectroscopy: a pilot study
Grani et al. Preoperative ultrasonography in the evaluation of suspected familial non-medullary thyroid cancer: are we able to predict multifocality and extrathyroidal extension?