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230811 ||| eng |
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|a 9783036548142
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|a books978-3-0365-4813-5
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|a 9783036548135
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| 100 |
1 |
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|a Wan, Shibiao
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| 245 |
0 |
0 |
|a Bioinformatics and Machine Learning for Cancer Biology
|h Elektronische Ressource
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| 260 |
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|a Basel
|b MDPI - Multidisciplinary Digital Publishing Institute
|c 2022
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| 300 |
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|a 1 electronic resource (196 p.)
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| 653 |
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|a machine learning
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| 653 |
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|a incidence
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| 653 |
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|a TBATS
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| 653 |
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|a biomedical informatics
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| 653 |
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|a CCLE
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| 653 |
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|a forecasting
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| 653 |
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|a filtering
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| 653 |
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|a image processing
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| 653 |
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|a n/a
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| 653 |
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|a diagnosis
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| 653 |
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|a T cell exhaustion
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| 653 |
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|a prognostic signature
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| 653 |
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|a deep learning
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| 653 |
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|a T-cell acute lymphoblastic leukemia
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| 653 |
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|a bladder urothelial carcinoma
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| 653 |
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|a architectural distortion
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| 653 |
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|a ARIMA
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| 653 |
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|a drug resistance
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| 653 |
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|a NGS
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| 653 |
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|a PUS7
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| 653 |
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|a RNA-seq
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| 653 |
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|a immunotherapy
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| 653 |
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|a estrogen receptor alpha
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| 653 |
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|a molecular docking
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| 653 |
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|a ctDNA
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| 653 |
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|a liquid biopsy
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| 653 |
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|a VAF
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| 653 |
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|a mortality
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| 653 |
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|a modeling
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| 653 |
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|a bladder cancer
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| 653 |
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|a Annexin family
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| 653 |
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|a Romania
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| 653 |
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|a persistent organic pollutants
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| 653 |
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|a RMGs
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| 653 |
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|a breast cancer
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| 653 |
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|a tumor mutational burden
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| 653 |
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|a mammography
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| 653 |
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|a papillary thyroid cancer (PTCa)
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| 653 |
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|a immune cells
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| 653 |
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|a Biology, life sciences / bicssc
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| 653 |
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|a sitagliptin
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| 653 |
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|a survival analysis
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| 653 |
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|a cancer
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| 653 |
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|a prediction
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| 653 |
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|a therapeutic target
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| 653 |
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|a thyroid cancer (THCA)
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| 653 |
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|a biomarker
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| 653 |
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|a proteomics
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| 653 |
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|a depth-wise convolutional neural network
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| 653 |
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|a major histocompatibility complex
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| 653 |
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|a variant calling
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| 653 |
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|a metastasis
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| 653 |
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|a ovarian cancer
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| 653 |
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|a transcriptomics
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| 653 |
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|a Google Trends
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| 653 |
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|a drug-drug interaction networks
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| 653 |
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|a multi-omics analysis
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| 653 |
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|a CPA4
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| 653 |
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|a bidirectional long short-term memory neural network
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| 653 |
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|a NNAR
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| 653 |
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|a checkpoint
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| 653 |
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|a biomarker identification
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| 653 |
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|a variable selection
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| 653 |
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|a thyroidectomy
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| 653 |
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|a Research and information: general / bicssc
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| 653 |
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|a DEGs
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| 653 |
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|a DNA damage repair genes
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| 653 |
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|a R Shiny application
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| 700 |
1 |
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|a Fan, Yiping
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| 700 |
1 |
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|a Jiang, Chunjie
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| 700 |
1 |
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|a Li, Shengli
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| 041 |
0 |
7 |
|a eng
|2 ISO 639-2
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| 989 |
|
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|b DOAB
|a Directory of Open Access Books
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| 500 |
|
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|a Creative Commons (cc), https://creativecommons.org/licenses/by/4.0/
|
| 028 |
5 |
0 |
|a 10.3390/books978-3-0365-4813-5
|
| 856 |
4 |
2 |
|u https://directory.doabooks.org/handle/20.500.12854/97414
|z DOAB: description of the publication
|
| 856 |
4 |
0 |
|u https://www.mdpi.com/books/pdfview/book/5918
|7 0
|x Verlag
|3 Volltext
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| 082 |
0 |
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|a 000
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| 520 |
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|a 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.
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