Biomedical subjects

Staal Vinterbo

Publications and source records attributed to Staal Vinterbo.

4 recordsLinked to original sources

PubMed2004

Multivariate selection of genetic markers in diagnostic classification.

Analysis of gene expression data obtained from microarrays presents a new set of challenges to machine learning modeling. In this domain, in which the number of variables far exceeds the number of cases, identifying relevant genes or groups of genes that are good markers for a particular classification is as important as achieving good classification performance. Although several machine learning algorithms have been proposed to address the latter, identification of gene markers has not been systematically pursued. In this article, we investigate several algorithms for selecting gene markers for classification. We test these algorithms using logistic regression, as this is a simple and efficient supervised learning algorithm. We demonstrate, using 10 different data sets, that a conditionally univariate algorithm constitutes a viable choice if a researcher is interested in quickly determining a set of gene expression levels that can serve as markers for disease. We show that the classification performance of logistic regression is not very different from that of more sophisticated algorithms that have been applied in previous studies, and that the gene selection in the logistic regression algorithm is reasonable in both cases. Furthermore, the algorithm is simple, its theoretical basis is well established, and our user-friendly implementation is now freely available on the internet, serving as a benchmarking tool for the development of new algorithms.

Algorithms↗

PubMed2004

Protecting patient privacy by quantifiable control of disclosures in disseminated databases.

One of the fundamental rights of patients is to have their privacy protected by health care organizations, so that information that can be used to identify a particular individual is not used to reveal sensitive patient data such as diagnoses, reasons for ordering tests, test results, etc. A common practice is to remove sensitive data from databases that are disseminated to the public, but this can make the disseminated database useless for important public health purposes. If the degree of anonymity of a disseminated data set could be measured, it would be possible to design algorithms that can assure that the desired level of confidentiality is achieved. Privacy protection in disseminated databases can be facilitated by the use of special ambiguation algorithms. Most of these algorithms are aimed at making one individual indistinguishable from one or more of his peers. However, even in databases considered "anonymous", it may still be possible to obtain sensitive information about some individuals or groups of individuals with the use of pattern recognition algorithms. In this article, we study the problem of determining the degree of ambiguation in disseminated databases and discuss its implications in the development and testing of "anonymization" algorithms.

Algorithms↗

PubMed2002

Comparing imperfect measurements with the Bland-Altman technique: application in gene expression analysis.

Several problems in medicine and biology involve the comparison of two measurements made on the same set of cases. The problem differs from a calibration problem because no gold standard can be identified. Testing the null hypothesis of no relationship using measures of association is not optimal since the measurements are made on the same cases, and therefore correlation coefficients will tend to be significant. The descriptive Bland-Altman method can be used in exploratory analysis of this problem, allowing the visualization of gross systematic differences between the two sets of measurements. We utilize the method on three sets of matched observations and demonstrate its usefulness in detecting systematic variations between two measurement technologies to assess gene expression.

Bias↗

PubMed2002

Building an asynchronous web-based tool for machine learning classification.

Various unsupervised and supervised learning methods including support vector machines, classification trees, linear discriminant analysis and nearest neighbor classifiers have been used to classify high-throughput gene expression data. Simpler and more widely accepted statistical tools have not yet been used for this purpose, hence proper comparisons between classification methods have not been conducted. We developed free software that implements logistic regression with stepwise variable selection as a quick and simple method for initial exploration of important genetic markers in disease classification. To implement the algorithm and allow our collaborators in remote locations to evaluate and compare its results against those of other methods, we developed a user-friendly asynchronous web-based application with a minimal amount of programming using free, downloadable software tools. With this program, we show that classification using logistic regression can perform as well as other more sophisticated algorithms, and it has the advantages of being easy to interpret and reproduce. By making the tool freely and easily available, we hope to promote the comparison of classification methods. In addition, we believe our web application can be used as a model for other bioinformatics laboratories that need to develop web-based analysis tools in a short amount of time and on a limited budget.

Algorithms↗