  Accurately predicting drug responses to cancer is an important problem
hindering oncologists' efforts to find the most effective drugs to treat
cancer, which is a core goal in precision medicine. The scientific community
has focused on improving this prediction based on genomic, epigenomic, and
proteomic datasets measured in human cancer cell lines. Real-world cancer cell
lines contain noise, which degrades the performance of machine learning
algorithms. This problem is rarely addressed in the existing approaches. In
this paper, we present a noise-filtering approach that integrates techniques
from numerical linear algebra and information retrieval targeted at filtering
out noisy cancer cell lines. By filtering out noisy cancer cell lines, we can
train machine learning algorithms on better quality cancer cell lines. We
evaluate the performance of our approach and compare it with an existing
approach using the Area Under the ROC Curve (AUC) on clinical trial data. The
experimental results show that our proposed approach is stable and also yields
the highest AUC at a statistically significant level.
