Biomedical subjects

J Rahnenführer

Publications and source records attributed to J Rahnenführer.

3 recordsLinked to original sources

PubMed2005

Image analysis for cDNA microarrays.

OBJECTIVES: We characterize typical problems encountered in microarray image analysis and present algorithmic approaches dealing with background estimation, spot identification and intensity extraction. Validation of the quality of resulting measurements is discussed. METHODS: We describe sources for errors in microarray images and present algorithms that have been specifically developed to deal with such experimental imperfections. RESULTS: For the image analysis of hybridization experiments, discriminating spot regions from a background is the most critical step. Spot shape detection algorithms, intensity histogram methods and hybrid approaches have been proposed. The correctness of final intensity estimates is difficult to verify. Nevertheless, the application of sophisticated algorithms provides a significant reduction of the possible information loss. CONCLUSIONS: The initial analysis step for array hybridization experiments is the estimation of expression intensities. The quality of this process is crucial for the validity of interpretations from subsequent analysis steps.

Algorithms↗

PubMed2005

Clustering algorithms and other exploratory methods for microarray data analysis.

OBJECTIVES: We introduce methods for the exploratory analysis of microarray data, especially focusing on cluster algorithms. Benefits and problems are discussed. METHODS: We describe application and suitability of unsupervised learning methods for the classification of gene expression data. Cluster algorithms are treated in more detail, including assessment of cluster quality. RESULTS: When dealing with microarray data, most cluster algorithms must be applied with caution. As long as the structure of the true generating models of such data is not fully understood, the use of simple algorithms seems to be more appropriate than the application of complex black-box algorithms. New methods explicitly targeted to the analysis of microarray data are increasingly being developed in order to increase the amount of useful information extracted from the experiments. CONCLUSIONS: Unsupervised methods can be a helpful tool for the analysis of microarray data, but a critical choice of the algorithm and a careful interpretation of the results are required in order to avoid false conclusions.

Algorithms↗

PubMed2004

Predicting protein structure classes from function predictions.

MOTIVATION: We introduce a new approach to using the information contained in sequence-to-function prediction data in order to recognize protein template classes, a critical step in predicting protein structure. The data on which our method is based comprise probabilities of functional categories; for given query sequences these probabilities are obtained by a neural net that has previously been trained on a variety of functionally important features. On a training set of sequences we assess the relevance of individual functional categories for identifying a given structural family. Using a combination of the most relevant categories, the likelihood of a query sequence to belong to a specific family can be estimated. RESULTS: The performance of the method is evaluated using cross-validation. For a fixed structural family and for every sequence, a score is calculated that measures the evidence for family membership. Even for structural families of small size, family members receive significantly higher scores. For some examples, we show that the relevant functional features identified by this method are biologically meaningful. The proposed approach can be used to improve existing sequence-to-structure prediction methods. AVAILABILITY: Matlab code is available on request from the authors. The data are available at http://www.mpisb.mpg.de/~sommer/Fun2Struc/

Algorithms↗