This method has been applied to two well known prostate cancer data sets: hormone sensitive versus hormone resistant, and healthy versus cancerous.
A meta-analysis across breast cancer data sets shows that aggressive subtypes are most likely to be sensitive to VPA, but all subtypes have sensitive tumors.
Andersen, C. L., Jensen, J. L. & Orntoft, T. F. Normalization of real-time quantitative reverse transcription-PCR data: a model-based variance estimation approach to identify genes suited for normalization, applied to bladder and colon cancer data sets.
In this volume, four lung cancer data sets are the focus of analysis.
If the classifier performs particularly well (i.e. NN&MUSK2, SVM&MUSK2) or particularly bad (Liver and Cancer data sets), the ranges tend to be small.
Here, we present a systematic study to answer this question specifically for breast cancer data sets.
Four of the breast cancer data sets have clinical annotation pertaining to treatment with Tamoxifen.
We conduct experiments using 6 well known cancer data sets including leukemia data set [6], two prostate cancer data sets [20], lymphoma [21], diffuse large B-cell lymphoma (DLBCL) [22], small round blue cell tumors (SRBCT) [8].
Permutations of both MG-thymoma and colon cancer data sets verified those global expression change difference (Figure S3).
The first contribution is that we implement an effective optimization based classifier that gives very high performance and valuable insight into different type of cancer data sets.
These sites are in agreement with sites previously identified from multiple cancer data sets as areas where acquired somatic mutations led to cancer progression and drug resistance.
