Because of the high risk of recurrence in high-grade serous ovarian carcinoma (HGS-OvCa), the development of outcome predictors could be valuable for patient stratification. Using the catalog of The Cancer Genome Atlas (TCGA), we developed subtype and survival gene expression signatures, which, when combined, provide a prognostic model of HGS-OvCa classification, named “
Roel G.W. Verhaak, Pablo Tamayo, Ji-Yeon Yang, Diana Hubbard, Hailei Zhang, Chad J. Creighton, Sian Fereday, Michael Lawrence, Scott L. Carter, Craig H. Mermel, Aleksandar D. Kostic, Dariush Etemadmoghadam, Gordon Saksena, Kristian Cibulskis, Sekhar Duraisamy, Keren Levanon, Carrie Sougnez, Aviad Tsherniak, Sebastian Gomez, Robert Onofrio, Stacey Gabriel, Lynda Chin, Nianxiang Zhang, Paul T. Spellman, Yiqun Zhang, Rehan Akbani, Katherine A. Hoadley, Ari Kahn, Martin Köbel, David Huntsman, Robert A. Soslow, Anna Defazio, Michael J. Birrer, Joe W. Gray, John N. Weinstein, David D. Bowtell, Ronny Drapkin, Jill P. Mesirov, Gad Getz, Douglas A. Levine, Matthew Meyerson
Usage data is cumulative from October 2021 through October 2022.
Usage information is collected from two different sources: this site (JCI) and Pubmed Central (PMC). JCI information (compiled daily) shows human readership based on methods we employ to screen out robotic usage. PMC information (aggregated monthly) is also similarly screened of robotic usage.
Various methods are used to distinguish robotic usage. For example, Google automatically scans articles to add to its search index and identifies itself as robotic; other services might not clearly identify themselves as robotic, or they are new or unknown as robotic. Because this activity can be misinterpreted as human readership, data may be re-processed periodically to reflect an improved understanding of robotic activity. Because of these factors, readers should consider usage information illustrative but subject to change.