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Título de Acceso Abierto

Bioinformatics and Machine Learning for Cancer Biology

Resumen/Descripción – provisto por la editorial

No disponible.

Palabras clave – provistas por la editorial

tumor mutational burden; DNA damage repair genes; immunotherapy; biomarker; biomedical informatics; breast cancer; estrogen receptor alpha; persistent organic pollutants; drug-drug interaction networks; molecular docking; NGS; ctDNA; VAF; liquid biopsy; filtering; variant calling; DEGs; diagnosis; ovarian cancer; PUS7; RMGs; CPA4; bladder urothelial carcinoma; immune cells; T cell exhaustion; checkpoint; architectural distortion; image processing; depth-wise convolutional neural network; mammography; bladder cancer; Annexin family; survival analysis; prognostic signature; therapeutic target; R Shiny application; RNA-seq; proteomics; multi-omics analysis; T-cell acute lymphoblastic leukemia; CCLE; sitagliptin; thyroid cancer (THCA); papillary thyroid cancer (PTCa); thyroidectomy; metastasis; drug resistance; n/a; biomarker identification; transcriptomics; machine learning; prediction; variable selection; major histocompatibility complex; bidirectional long short-term memory neural network; deep learning; cancer; incidence; mortality; modeling; forecasting; Google Trends; Romania; ARIMA; TBATS; NNAR

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Información

Tipo de recurso:

libros

ISBN electrónico

978-3-0365-4813-5

País de edición

Suiza