PMC:4331679 / 8072-9552 JSONTXT

{"project":"2_test","denotations":[{"id":"25707537-22579283-14870714","span":{"begin":1025,"end":1026},"obj":"22579283"}],"text":"The Yaffe's data set contains six types of cellular phenotypic responses from three cancer cell lines, namely triple negative BT-20, Hormone sensitive MCF-7 and HER2 overexpressing MDA-MB-453. The six phenotypes are Apoptosis, Proliferation, G1, S, G2 and M. These phenotypic responses are linked with on 35 signalling proteins (only 32 available in the published data set). The signalling molecule data available contains 8 time points (0, 1, 2, 3, 4, 5, 6 and 7). The phenotypic responses were measured at five time points (0, 6, 7, 8 and 9), over 24 hours in the panel of breast cancer cell lines. The cell fates and signalling data are available for all the three cell lines. The measurements of the signalling and cell fate data have been done for six treatments namely DMSO, TAR, DOX, DT, T-D and D-T. The apoptosis level is reportedly high for the T-D treatment, meaning the cells are now less tumorigenic for this treatment and thereby more susceptible to death induced by DNA damage response pathways. The study in [5] employs several mathematical modelling approaches to relate signalling data to cell phenotypes. Principal component analysis (PCA) was used to identify co-variation between signals, whereas partial least-squares regression (PLSR) was used to identify co-variation between molecular signals and corresponding cellular phenotypic responses. In both PCA and PLSR modelling, the input vectors contained quantitative measurements of the signalling proteins."}