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Class imbalance (where one class has many more instances than the other class(es)) and high dimensionality (large number of features per instance) are two prevalent problems that are frequently present in patient response datasets. In addition to these problems, these datasets are notoriously difficult to build effective models from. This paper introduces a new hybrid boosting algorithm named SelectRUSBoost...
The ability to predict a patient's response to a treatment has long been a goal in the fields of medicine andpharmacology. This is especially true for cancer treatments, as many of these incur extreme side effects as a consequenceof destroying healthy cells along with cancerous ones. Geneprofiles such as DNA microarrays could potentially containinformation on which treatments are most likely to work...
As the field of bioinformatics has grown in importance, more and more studies have investigated the use of gene microarray datasets to understand cancer. Although much of this research has focused on which genes are differently-expressed between cancerous and non-cancerous tissues, an equally important question is which genes are most useful for predicting the success of cancer treatment. How well...
Many cancer treatments destroy healthy cells along with cancerous ones, and can leave patients fatigued and with a compromised immune system. This makes it especially important to determine whether or not a given cancer treatment will work for the patient or will just cause further harm. Recently there has been work on using gene expression profiles (DNA microarrays) to predict how a patient will...
The goal of classification is to reliably identify instances that are members of the class of interest. This is especially important for predicting patient response to drugs. However, with high dimensional datasets, classification is both complicated and enhanced by the feature selection process. When designing a classification experiment there are a number of decisions which need to be made in order...
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