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Objective. Despite many research efforts in recent decades, the major pathogenetic mechanisms of osteoarthritis (OA), including gene alterations occurring during OA cartilage degeneration, are poorly understood, and there is no disease-modifying treatment approach. The present study was therefore initiated in order to identify differentially expressed disease-related genes and potential therapeutic targets.Methods. This investigation consisted of a large gene expression profiling study performed based on 78 normal and disease samples, using a custom-made complementary DNA array covering >4,000 genes.Results. Many differentially expressed genes were identified, including the expected up-regulation of anabolic and catabolic matrix genes. In particular, the down-regulation of important oxidative defense genes, i.e., the genes for superoxide dismutases 2 and 3 and glutathione peroxidase 3, was prominent. This indicates that continuous oxidative stress to the cells and the matrix is one major underlying pathogenetic mechanism in OA. Also, genes that are involved in the phenotypic stability of cells, a feature that is greatly reduced in OA cartilage, appeared to be suppressed.Conclusion. Our findings provide a reference data set on gene alterations in OA cartilage and, importantly, indicate major mechanisms underlying central cell biologic alterations that occur during the OA disease process. These results identify molecular targets that can be further investigated in the search for therapeutic interventions.

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.

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In many applications it is desirable to learn from several kernels. "Multiple kernel learning" (MKL) allows the practitioner to optimize over linear combinations of kernels. By enforcing sparse coefficients, it also generalizes feature selection to kernel selection. We propose MKL for joint feature maps. This provides a convenient and principled way for MKL with multiclass problems. In addition, we can exploit the joint feature map to learn kernels on output spaces. We show the equivalence of several different primal formulations including different regularizers. We present several optimization methods, and compare a convex quadratically constrained quadratic program (QCQP) and two semi-infinite linear programs (SILPs) on toy data, showing that the SILPs are faster than the QCQP. We then demonstrate the utility of our method by applying the SILP to three real world datasets.