Title |
Modeling precision treatment of breast cancer
|
---|---|
Published in |
Genome Biology, January 2013
|
DOI | 10.1186/gb-2013-14-10-r110 |
Pubmed ID | |
Authors |
Anneleen Daemen, Obi L Griffith, Laura M Heiser, Nicholas J Wang, Oana M Enache, Zachary Sanborn, Francois Pepin, Steffen Durinck, James E Korkola, Malachi Griffith, Joe S Hur, Nam Huh, Jongsuk Chung, Leslie Cope, Mary Fackler, Christopher Umbricht, Saraswati Sukumar, Pankaj Seth, Vikas P Sukhatme, Lakshmi R Jakkula, Yiling Lu, Gordon B Mills, Raymond J Cho, Eric A Collisson, Laura J van’t Veer, Paul T Spellman, Joe W Gray |
Abstract |
First-generation molecular profiles for human breast cancers have enabled the identification of features that can predict therapeutic response; however, little is known about how the various data types can best be combined to yield optimal predictors. Collections of breast cancer cell lines mirror many aspects of breast cancer molecular pathobiology, and measurements of their omic and biological therapeutic responses are well-suited for development of strategies to identify the most predictive molecular feature sets. |
X Demographics
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 7 | 33% |
Germany | 2 | 10% |
United Kingdom | 2 | 10% |
Serbia | 1 | 5% |
France | 1 | 5% |
Bahrain | 1 | 5% |
Sweden | 1 | 5% |
Spain | 1 | 5% |
India | 1 | 5% |
Other | 0 | 0% |
Unknown | 4 | 19% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Scientists | 16 | 76% |
Members of the public | 4 | 19% |
Science communicators (journalists, bloggers, editors) | 1 | 5% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 11 | 3% |
United Kingdom | 4 | 1% |
Spain | 3 | <1% |
Italy | 2 | <1% |
Denmark | 2 | <1% |
Brazil | 2 | <1% |
France | 1 | <1% |
Canada | 1 | <1% |
Norway | 1 | <1% |
Other | 4 | 1% |
Unknown | 317 | 91% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 99 | 28% |
Researcher | 83 | 24% |
Student > Master | 36 | 10% |
Student > Bachelor | 28 | 8% |
Student > Doctoral Student | 16 | 5% |
Other | 55 | 16% |
Unknown | 31 | 9% |
Readers by discipline | Count | As % |
---|---|---|
Agricultural and Biological Sciences | 107 | 31% |
Biochemistry, Genetics and Molecular Biology | 84 | 24% |
Computer Science | 41 | 12% |
Medicine and Dentistry | 31 | 9% |
Engineering | 13 | 4% |
Other | 26 | 7% |
Unknown | 46 | 13% |