Artificial Intelligence for Precision Oncology
Advances in Experimental Medicine and Biology, ISSN: 2214-8019, Vol: 1361, Page: 249-268
2022
- 23Citations
- 37Captures
Metric Options: CountsSelecting the 1-year or 3-year option will change the metrics count to percentiles, illustrating how an article or review compares to other articles or reviews within the selected time period in the same journal. Selecting the 1-year option compares the metrics against other articles/reviews that were also published in the same calendar year. Selecting the 3-year option compares the metrics against other articles/reviews that were also published in the same calendar year plus the two years prior.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Metrics Details
- Citations23
- Citation Indexes23
- 23
- Captures37
- Readers37
- 37
Book Chapter Description
Precision oncology is an innovative approach to cancer care in which diagnosis, prognosis, and treatment are informed by the individual patient’s genetic and molecular profile. The rapid development of novel high-throughput omics technologies in recent years has led to the generation of massive amount of complex patient data, which in turn has prompted the development of novel computational infrastructures, platforms, and tools to store, retrieve, and analyze this data efficiently. Artificial intelligence (AI), and in particular its subfield of machine learning, is ideal for deciphering patterns in large datasets and offers unique opportunities for advancing precision oncology. In this chapter, we provide an overview of the various public data resources and applications of AI in precision oncology and cancer research, from subtype identification to drug prioritization, using multi-omics datasets. We also discuss the impact of AI-powered medical image analysis in oncology and present the first diagnostic FDA-approved AI-powered tools.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85125552665&origin=inward; http://dx.doi.org/10.1007/978-3-030-91836-1_14; http://www.ncbi.nlm.nih.gov/pubmed/35230693; https://link.springer.com/10.1007/978-3-030-91836-1_14; https://dx.doi.org/10.1007/978-3-030-91836-1_14; https://link.springer.com/chapter/10.1007/978-3-030-91836-1_14
Springer Science and Business Media LLC
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