Wen, Q, Kim, C, Hamilton, P and Zhang, S. 2016. Additional file 4 of A gene-signature progression approach to identifying candidate small-molecule cancer therapeutics with connectivity mapping. [Online]. Figshare. Available from: https://doi.org/10.6084/m9.figshare.c.3624356_D1.v1
Wen, Q, Kim, C, Hamilton, P and Zhang, S. Additional file 4 of A gene-signature progression approach to identifying candidate small-molecule cancer therapeutics with connectivity mapping [Internet]. Figshare; 2016. Available from: https://doi.org/10.6084/m9.figshare.c.3624356_D1.v1
Wen, Q, Kim, C, Hamilton, P and Zhang, S (2016). Additional file 4 of A gene-signature progression approach to identifying candidate small-molecule cancer therapeutics with connectivity mapping. [Data Collection]. Figshare. https://doi.org/10.6084/m9.figshare.c.3624356_D1.v1
Description
Breast cancer gene signature. The full list of 232 genes (probesets) and their ranks for the optimal gene signature obtained from the breast cancer dataset GSE15852.
| Data capture method | Unknown |
|---|---|
| Date (Date published in a 3rd party system) | 11 May 2016 |
| Language(s) of written materials | English |
| Data Creators | Wen, Q, Kim, C, Hamilton, P and Zhang, S |
|---|---|
| LSHTM Faculty/Department | Faculty of Epidemiology and Population Health > Dept of Non-Communicable Disease Epidemiology |
| Participating Institutions | Queen’s University Belfast, Belfast, United Kingdom, STFC Daresbury Laboratory, Daresbury, Warrington, United Kingdom, University of Ulster, C-TRIC Building, Altnagelvin Hospital campus, Glenshane Road, BT47 6SB, Derry/Londonderry, United Kingdom |
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| Date Deposited | 04 Jan 2024 17:14 |
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| Last Modified | 04 Jan 2024 17:14 |
| Publisher | Figshare |
