TY - BOOK ID - 133918418 TI - Bioinformatics and Machine Learning for Cancer Biology AU - Wan, Shibiao AU - Fan, Yiping AU - Jiang, Chunjie AU - Li, Shengli PY - 2022 PB - Basel MDPI Books DB - UniCat KW - Research & information: general KW - Biology, life sciences KW - tumor mutational burden KW - DNA damage repair genes KW - immunotherapy KW - biomarker KW - biomedical informatics KW - breast cancer KW - estrogen receptor alpha KW - persistent organic pollutants KW - drug-drug interaction networks KW - molecular docking KW - NGS KW - ctDNA KW - VAF KW - liquid biopsy KW - filtering KW - variant calling KW - DEGs KW - diagnosis KW - ovarian cancer KW - PUS7 KW - RMGs KW - CPA4 KW - bladder urothelial carcinoma KW - immune cells KW - T cell exhaustion KW - checkpoint KW - architectural distortion KW - image processing KW - depth-wise convolutional neural network KW - mammography KW - bladder cancer KW - Annexin family KW - survival analysis KW - prognostic signature KW - therapeutic target KW - R Shiny application KW - RNA-seq KW - proteomics KW - multi-omics analysis KW - T-cell acute lymphoblastic leukemia KW - CCLE KW - sitagliptin KW - thyroid cancer (THCA) KW - papillary thyroid cancer (PTCa) KW - thyroidectomy KW - metastasis KW - drug resistance KW - n/a KW - biomarker identification KW - transcriptomics KW - machine learning KW - prediction KW - variable selection KW - major histocompatibility complex KW - bidirectional long short-term memory neural network KW - deep learning KW - cancer KW - incidence KW - mortality KW - modeling KW - forecasting KW - Google Trends KW - Romania KW - ARIMA KW - TBATS KW - NNAR UR - https://www.unicat.be/uniCat?func=search&query=sysid:133918418 AB - 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. ER -