Biomedical subjects

Magne Aldrin

Publications and source records attributed to Magne Aldrin.

4 recordsLinked to original sources

PubMed2005

The influence of missing value imputation on detection of differentially expressed genes from microarray data.

MOTIVATION: Missing values are problematic for the analysis of microarray data. Imputation methods have been compared in terms of the similarity between imputed and true values in simulation experiments and not of their influence on the final analysis. The focus has been on missing at random, while entries are missing also not at random. RESULTS: We investigate the influence of imputation on the detection of differentially expressed genes from cDNA microarray data. We apply ANOVA for microarrays and SAM and look to the differentially expressed genes that are lost because of imputation. We show that this new measure provides useful information that the traditional root mean squared error cannot capture. We also show that the type of missingness matters: imputing 5% missing not at random has the same effect as imputing 10-30% missing at random. We propose a new method for imputation (LinImp), fitting a simple linear model for each channel separately, and compare it with the widely used KNNimpute method. For 10% missing at random, KNNimpute leads to twice as many lost differentially expressed genes as LinImp. AVAILABILITY: The R package for LinImp is available at http://folk.uio.no/idasch/imp.

Algorithms↗

PubMed2005

Predicting blood donor arrival.

BACKGROUND: Keeping waiting time at blood donation short is important for making donation a good experience for the donors and hence to motivate for repeat donations. At the Blood Bank of Oslo, fixed appointments are used, and few donors arrive without appointments. On average, 59 percent of scheduled donors arrive, but day-to-day variations are large. Methods for predicting the number of donors that will arrive on a given day would be valuable in reducing waiting times. STUDY DESIGN AND METHODS: Information about candidate explanatory variables was collected for all appointments made in a 971-day period (179,121 appointments). A logistic regression model for the prediction of blood donor arrival was fitted. RESULTS: Among 18 explanatory variables, the most important were the time from appointment making to appointment date; the contact medium used; the donor age and total number of donations; and the number of no-shows, arrivals, and deferrals during the preceding 2 years. Compared to taking only the average arrival rate into account, prediction intervals were reduced by 43 percent. CONCLUSION: Statistical modeling can provide useful estimates of blood donor arrival, allowing for better planning of donation sessions.

Appointments and Schedules↗

PubMed2004

Analysis of the humoral immune response to immunoselected phage-displayed peptides by a microarray-based method.

We describe a novel approach for high-throughput analysis of the immune response in cancer patients using phage-based microarray technology. The recombinant phages used for fabricating phage arrays were initially selected via the use of random peptide phage libraries and breast cancer patient serum antibodies. The peptides displayed by the phages retained their ability to be recognized by serum antibodies after immobilization. The recombinant phage microarrays were screened against either breast cancer or healthy donor serum antibodies. A model-based statistical method is proposed to estimate significant differences in serum antibody reactivity between patients and normals. A significant tumor effect was found with most of the selected phage-displayed peptides, suggesting that recombinant phage microarrays can serve as a tool in monitoring humoral responses towards phage-displayed peptides.

Amino Acid Sequence↗

PubMed2003

Comment on Cowling's "Spatial methods for line transect surveys".

We consider the problem of estimating the parameters of a two-dimensional Neyman-Scott process, from data collected through a line transect survey. Cowling (1998, Biometrics 54, 828-839) suggested an estimation method based on a one-dimensional K-function along the transect line. However, her expression for the theoretical K-function is wrong. In this article, we correct her K-function.

Animals↗