Transforming Cancer Research through Informatics Software implementing informatics methods is now essential to all areas and aspects of cancer research It is used to control instruments, track experimental parameters and protocols, manage and process raw data, perform data analysis, enable exploratory visualization, and more
Computational biologist uses big data, AI and math to find patterns in . . . By applying advanced computational techniques, including AI, to these datasets, she helps researchers explore questions on everything from basic biological processes, such as development and cellular differentiation, to disease progression and how to target therapies and improve outcomes
Cancer modeling: From mechanistic to data-driven approaches, and from . . . In basic cancer research, a vast number of mathematical and computational models has been implemented in the past decades, allowing for a better understanding of these complex diseases, generating new hypotheses and predictions, and guiding scientists towards the most impactful experiments
Computational Genomics Research - NCI - National Cancer Institute NCI’s Genomic Data Analysis Network (GDAN) is a collaborative team that develops and applies computational analysis methods to large-scale datasets The GDAN’s goal is to help the research community leverage the genomic data produced by NCI and other programs
Informatics at the Frontier of Cancer Research We review current and emerging informatics technology developments for cancer research and discovery, spanning molecular and cellular characterizations, image analysis, informatics, and therapeutics
Cancer computational biology - PMC High-throughput measurement technologies, such as microarray-based profiling, mass spectrometry screens, and high-throughput sequencing, give rise to several computational challenges On one hand, they require a rigorous approach to assay design