A pioneer work using machine-learning approaches to predict drug response on cancer cell lines was by Menden et al. [2]. The authors used a neural network to analyze the response of drugs to cancer cell lines on the GDSC dataset. Their main result was the achievement of 0.72 for the coef...
The GSCA database was exploited to analyze the relationship between the drug sensitivity and the expression of LARS and DNAJC17 based on the data from the Cancer Drug Sensitivity Genomics Database (GDSC) (Fig. 6, Additional file 1: S3). The result revealed that high LARS expression was assoc...
Further research beyond these common features is warranted to precisely elucidate the specific meaning of our gene signature, invasiveness score, and all of the molecular alterations, in each cancer type. Conclusion In summary, by integrating multi-omics data, our large-cohort pan-cancer study ...
respectively, phenocopyTP53loss: they are likely deficient in the activity of the p53 pathway, while not bearing obviousTP53inactivating mutations. While some of these cases are explained by amplifications
(http://pubchem.ncbi.nlm.nih.gov/). To avoid disturbing from noises and make sure the network has a clear biological meaning, the elements smaller than 0.2 in the similarity matrix were set as 0. The drug similarity network consists of 226 vertices and 24456 edges, where each vertex ...
, NA could act on MODULE-218 and HALLMARK-9 compared with HO, indicating an essential role in DNA integrity and damage checkpoint signaling before mitosis. This could be the potential synergistic mechanism underlying the improved inhibition of BC cells, meaning that HO and NA could synergistically...