1,721,188 research outputs found
Chromosomal Instability in Pathogenesis and Prognosis of Colorectal Cancer
Full text linkMeijer, G.A. [Promotor]Ylstra, B. [Copromotor]Carvalho, B. [Copromotor
Analysis of chromosomal copy number aberrations in gastrointestinal cancer
Full text linkMeijer, G.A. [Promotor]Punt, C.J.A. [Promotor]Ylstra, B. [Copromotor]Nagtegaal, I.D. [Copromotor
Therapeutic targets and biomarkers in lung cancer
Full text linkSmit, E.F. [Promotor]Giaccone, G. [Promotor]Rodriquez, J.A. [Copromotor]Ylstra, B. [Copromotor
Molecular characterization of low-grade glial neoplasms
Full text linkYlstra, B. [Promotor]Heimans, J.J. [Promotor]Wesseling, P. [Promotor]Reijneveld, J.C. [Copromotor
Genetic and functional analysis of head and neck carcinogenesis
Full text linkBrakenhoff, R.H. [Promotor]Leemans, C.R. [Promotor]Braakhuis, B.J.M. [Copromotor]Ylstra, B. [Copromotor
Tumor and patient profiling to predict clinical outcome in patients with colorectal cancer
Full text linkMeijer, G.A. [Promotor]Cuesta Valentin, M.A. [Promotor]Engel, A.F. [Copromotor]Ylstra, B. [Copromotor
Targetable genetic signatures of immune evasion in lymphoma
Full text linkJong, D. de [Promotor]Shipp, M.A. [Promotor]Ylstra, B. [Copromotor]Chapuy, B. [Copromotor
CGHpower: exploring sample size calculations for chromosomal copy number experiments
Full text linkAbstract Background Determining a suitable sample size is an important step in the planning of microarray experiments. Increasing the number of arrays gives more statistical power, but adds to the total cost of the experiment. Several approaches for sample size determination have been developed for expression array studies, but so far none has been proposed for array comparative genomic hybridization (aCGH). Results Here we explore power calculations for aCGH experiments comparing two groups. In a pilot experiment CGHpower estimates the biological diversity between groups and provides a statistical framework for estimating average power as a function of sample size. As the method requires pilot data, it can be used either in the planning stage of larger studies or in estimating the power achieved in past experiments. Conclusions The proposed method relies on certain assumptions. According to our evaluation with public and simulated data sets, they do not always hold true. Violation of the assumptions typically leads to unreliable sample size estimates. Despite its limitations, this method is, at least to our knowledge, the only one currently available for performing sample size calculations in the context of aCGH. Moreover, the implementation of the method provides diagnostic plots that allow critical assessment of the assumptions on which it is based and hence on the feasibility and reliability of the sample size calculations in each case. The CGHpower web application and the program outputs from evaluation data sets can be freely accessed at http://www.cangem.org/cghpower/</p
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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