Differential gene expression detection and sample classification using microarray data have received much research interest recently. Owing to the large number of genes p and small number of samples n (p >> n), microarray data analysis poses big challenges for statistical analysis. An obvious problem owing to the 'large p small n' is over-fitting. Just by chance, we are likely to find some non-...

Chemotherapy and hormonal therapy as adjuvant systemic therapies to inhibit breast cancer recurrence are not necessary for each patient. In Veer‟s paper “Gene expression profiling predicts clinical outcome of breast cancer” (Nature 2002, PMID: 11823860), they introduced a method based on DNA microarray technology, which tried to identify a gene expression profile with high cancer recurrence pot...

We have devised an approach to cancer class prediction from gene expression profiling, based on an enhancement of the simple nearest prototype (centroid) classifier. We shrink the prototypes and hence obtain a classifier that is often more accurate than competing methods. Our method of "nearest shrunken centroids" identifies subsets of genes that best characterize each class. The technique is g...

Support vector machines(SVMs) have demonstrated good performance to correctly classify samples into appropriate classes which contain tens of thousands of genes. The key to the success of using SVMs is choosing an appropriate kernel. Widely used kernels are linear, polynomial, radial basis function and sigmoidal. We compared the performance of kernels when all genes were used and when fewer num...

Null linear discriminant analysis (LDA) is a well-known dimensionality reduction technique for the small sample size problem. When the null LDA technique projects the samples to a lower dimensional space, the covariance matrices of individual classes become zero, i.e. all the projected vectors of a given class merge into a single vector. In this case, only the nearest centroid classifier (NCC) ...

Nearest shrunken centroids (NSC) is a popular classification method for microarray data. NSC calculates centroids for each class and "shrinks" the centroids toward 0 using soft thresholding. Future observations are then assigned to the class with the minimum distance between the observation and the (shrunken) centroid. Under certain conditions the soft shrinkage used by NSC is equivalent to a L...

The abundance of skeptical theories about who wrote the Book of Mormon has led many scholars to seek scientific data to discover the answer. One technique is stylometry. Having first been developed in the 1850s, stylometry seeks to find the “wordprint” of a text. Although these stylistic studies are not as accurate as a human’s fingerprint, they can give researchers a good idea either of differ...

There are various types of classifiers that can be trained on gene expression data with class labels. Many of them have an embedded mechanism for feature selection, by which they distinguish a subset of significant genes that are used for future prediction. When dealing with more than two class labels, especially when the number goes up to a dozen or more, people find it useful to know the rela...