Biomedical subjects

A Zien

Publications and source records attributed to A Zien.

6 recordsLinked to original sources

PubMed2001

Anabolic and catabolic gene expression pattern analysis in normal versus osteoarthritic cartilage using complementary DNA-array technology.

OBJECTIVE: To understand changes in gene expression levels that occur during osteoarthritic (OA) cartilage degeneration, using complementary DNA (cDNA)-array technology. METHODS: Nine normal, 6 early degenerated, and 6 late-stage OA cartilage samples of human knee joints were analyzed using the Human Cancer 1.2 cDNA array and TaqMan analysis. RESULTS: In addition to a large variability of expression levels between different patients, significant expression patterns were detectable for many genes. Cartilage types II and VI collagen were strongly expressed in late-stage specimens, reflecting the high matrix-remodeling activity of advanced OA cartilage. The increase in fibronectin expression in early degeneration suggests that fibronectin is a crucial regulator of matrix turnover activity of chondrocytes during early disease development. Of the matrix metalloproteinases (MMPs), MMP-3 appeared to be strongly expressed in normal and early degenerative cartilage and down-regulated in the late stages of disease. This indicates that other degradation pathways might be more important in late stages of cartilage degeneration, involving other enzymes, such as MMP-2 and MMP-11, both of which were up-regulated in late-stage disease. MMP-11 was up-regulated in OA chondrocytes and, interestingly, also in the early-stage samples. Neither MMP-1 nor MMP-8 was detectable, and MMP-13 and MMP-2 were significantly detectable only in late-stage specimens, suggesting that early stages are characterized more by degradation of other matrix components, such as aggrecan and other noncollagenous molecules, than by degradation of type II collagen fibers. CONCLUSION: This investigation allowed us to identify gene expression profiles of the disease process and to get new insights into disease mechanisms, for example, to develop a picture of matrix proteinases that are differentially involved in different phases of the disease process.

Adult↗

PubMed2001

Centralization: a new method for the normalization of gene expression data.

Microarrays measure values that are approximately proportional to the numbers of copies of different mRNA molecules in samples. Due to technical difficulties, the constant of proportionality between the measured intensities and the numbers of mRNA copies per cell is unknown and may vary for different arrays. Usually, the data are normalized (i.e., array-wise multiplied by appropriate factors) in order to compensate for this effect and to enable informative comparisons between different experiments. Centralization is a new two-step method for the computation of such normalization factors that is both biologically better motivated and more robust than standard approaches. First, for each pair of arrays the quotient of the constants of proportionality is estimated. Second, from the resulting matrix of pairwise quotients an optimally consistent scaling of the samples is computed.

Computational Biology↗

PubMed2000

A simple iterative approach to parameter optimization.

Various bioinformatics problems require optimizing several different properties simultaneously. For example, in the protein threading problem, a scoring function combines the values for different parameters of possible sequence-to-structure alignments into a single score to allow for unambiguous optimization. In this context, an essential question is how each property should be weighted. As the native structures are known for some sequences, a partial ordering on optimal alignments to other structures, e.g., derived from structural comparisons, may be used to adjust the weights. To resolve the arising interdependence of weights and computed solutions, we propose a heuristic approach: iterating the computation of solutions (here, threading alignments) given the weights and the estimation of optimal weights of the scoring function given these solutions via systematic calibration methods. For our application (i.e., threading), this iterative approach results in structurally meaningful weights that significantly improve performance on both the training and the test data sets. In addition, the optimized parameters show significant improvements on the recognition rate for a grossly enlarged comprehensive benchmark, a modified recognition protocol as well as modified alignment types (local instead of global and profiles instead of single sequences). These results show the general validity of the optimized weights for the given threading program and the associated scoring contributions.

Algorithms↗

PubMed2000

Engineering support vector machine kernels that recognize translation initiation sites.

MOTIVATION: In order to extract protein sequences from nucleotide sequences, it is an important step to recognize points at which regions start that code for proteins. These points are called translation initiation sites (TIS). RESULTS: The task of finding TIS can be modeled as a classification problem. We demonstrate the applicability of support vector machines for this task, and show how to incorporate prior biological knowledge by engineering an appropriate kernel function. With the described techniques the recognition performance can be improved by 26% over leading existing approaches. We provide evidence that existing related methods (e.g. ESTScan) could profit from advanced TIS recognition.

Algorithms↗

PubMed2000

Analysis of gene expression data with pathway scores.

We present a new approach for the evaluation of gene expression data. The basic idea is to generate biologically possible pathways and to score them with respect to gene expression measurements. We suggest sample scoring functions for different problem specifications. We assess the significance of the scores for the investigated pathways by comparison to a number of scores for random pathways. We show that simple scoring functions can assign statistically significant scores to biologically relevant pathways. This suggests that the combination of appropriate scoring functions with the systematic generation of pathways can be used in order to select the most interesting pathways based on gene expression measurements.

Animals↗

PubMed1999

Application of parameter optimization to molecular comparison problems.

Various bioinformatics comparison problems require optimizing several different properties simultaneously. Often linear objective functions combine the values for different properties of solution candidates into a single score to allow for multivariate optimization. In this context, an essential question is how each property should be weighted. Frequently, no apparent measure is available to serve as a model for the score. However, if preferences of certain solution candidates over others in a training set are available, the implied partial ordering may be used to best possibly adjust the weights. We apply different strategies to optimize the parameterization of empirical scoring functions used for two molecular comparison problems, protein threading and small molecule superposition. Using well established evaluation methods, it can be shown that the results of both comparison methods are significantly improved by systematically choosing appropriate weights for the scoring function contributions.

Algorithms↗