Biomedical subjects

Kenneth Boucher

Publications and source records attributed to Kenneth Boucher.

3 recordsLinked to original sources

PubMed2004

Clinical germline genetic testing for melanoma.

Clinical genetic testing for mutations in CDKN2A (cyclin-dependent kinase inhibitor 2A), a melanoma susceptibility gene, is now available. The International Melanoma Genetics Consortium advocates that genetic testing for CDKN2A should be done only as part of a research protocol. Experience with genetic testing for other cancer-susceptibility genes indicates that CDKN2A testing has enormous potential for the prevention and detection of a deadly disease. However, clinicians need to understand the benefits and shortcomings of clinical CDKN2A testing so that it can be used advantageously. Here, we examine whether CDKN2A meets the recommendations of the American Society of Clinical Oncology (ASCO) for cancer-susceptibility genetic testing. Although genetic testing for hereditary melanoma should, whenever possible, occur within research protocols, it might be successfully done outside of research protocols if attention is paid to selection, education, and counselling needs of patients; valid test interpretation; and the changing of medical management in appropriate individuals.

Cyclin-Dependent Kinase Inhibitor p16↗

PubMed2003

Multivariate exploratory tools for microarray data analysis.

The ultimate success of microarray technology in basic and applied biological sciences depends critically on the development of statistical methods for gene expression data analysis. The most widely used tests for differential expression of genes are essentially univariate. Such tests disregard the multidimensional structure of microarray data. Multivariate methods are needed to utilize the information hidden in gene interactions and hence to provide more powerful and biologically meaningful methods for finding subsets of differentially expressed genes. The objective of this paper is to develop methods of multidimensional search for biologically significant genes, considering expression signals as mutually dependent random variables. To attain these ends, we consider the utility of a pertinent distance between random vectors and its empirical counterpart constructed from gene expression data. The distance furnishes exploratory procedures aimed at finding a target subset of differentially expressed genes. To determine the size of the target subset, we resort to successive elimination of smaller subsets resulting from each step of a random search algorithm based on maximization of the proposed distance. Different stopping rules associated with this procedure are evaluated. The usefulness of the proposed approach is illustrated with an application to the analysis of two sets of gene expression data.

Algorithms↗

PubMed2002

Estimating an oncogenetic tree when false negatives and positives are present.

Human solid tumors are believed to be caused by a sequence of genetic abnormalities arising in the tumor cells. The understanding of these sequences is extremely important for improving cancer treatment. Models for the occurrence of the abnormalities include linear structure and a recently proposed tree-based structure. In this paper we extend the pure oncogenetic tree model by introducing false positive and false negative observations. We state conditions sufficient for the reconstruction of the generating tree. As an example we analyze a comparative genomic hybridization data set and show that addition of the error model significantly improves the ability of the model to describe the data.

Adenocarcinoma, Clear Cell↗