Bioinformatics and Machine Learning for Cancer Biology

• Format: eBook
• Language: English
• Published: Basel MDPI - Multidisciplinary Digital Publishing Institute 2022
• Subjects: Annexin family; architectural distortion; ARIMA; bidirectional long short-term memory neural network; Biology, life sciences; biomarker; biomarker identification; biomedical informatics; bladder cancer; bladder urothelial carcinoma; breast cancer; cancer; CCLE; checkpoint; CPA4; ctDNA; deep learning; DEGs; depth-wise convolutional neural network; diagnosis; DNA damage repair genes; drug resistance; drug-drug interaction networks; estrogen receptor alpha; filtering; forecasting; Google Trends; image processing; immune cells; immunotherapy; incidence; liquid biopsy; machine learning; major histocompatibility complex; mammography; Mathematics and Science; metastasis; modeling; molecular docking; mortality; multi-omics analysis; n/a; NGS; NNAR; ovarian cancer; papillary thyroid cancer (PTCa); persistent organic pollutants; prediction; prognostic signature; proteomics; PUS7; R Shiny application; Reference, Information and Interdisciplinary subjects; Research and information: general; RMGs; RNA-seq; Romania; sitagliptin; survival analysis; T cell exhaustion; T-cell acute lymphoblastic leukemia; TBATS; therapeutic target; thyroid cancer (THCA); thyroidectomy; transcriptomics; tumor mutational burden; VAF; variable selection; variant calling
• ISBN: 9783036548142; 9783036548135; 3036548149; 3036548130
• DOI: 10.3390/books978-3-0365-4813-5

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.