Turku Centre for Computer Science

About: Turku Centre for Computer Science is a facility organization based out in Turku, Finland. It is known for research contribution in the topics: Decidability & Word (group theory). The organization has 382 authors who have published 1027 publications receiving 19560 citations.

Estimation of Prostate Cancer Probability by Logistic Regression: Free and Total Prostate-specific Antigen, Digital Rectal Examination, and Heredity Are Significant Variables

Abstract: Background: Despite low specificity, serum prostate-specific antigen (PSA) is widely used in screening for prostate cancer. Specificity can be improved by measuring free and total PSA and by combining these results with clinical findings. Methods such as neural networks and logistic regression are alternatives to multistep algorithms for clinical use of the combined findings. Methods: We compared multilayer perceptron (MLP) and logistic regression (LR) analysis for predicting prostate cancer in a screening population of 974 men, ages 55–66 years. The study sample comprised men with PSA values >3 μg/L. Explanatory variables considered were age, free and total PSA and their ratio, digital rectal examination (DRE), transrectal ultrasonography, and a family history of prostate cancer. Results: When at least 90% sensitivity in the training sets was required, the mean sensitivity and specificity obtained were 87% and 41% with LR and 85% and 26% with MLP, respectively. The cancer specificity of an LR model comprising the proportion of free to total PSA, DRE, and heredity as explanatory variables was significantly better than that of total PSA and the proportion of free to total PSA ( P <0.01, McNemar test). The proportion of free to total PSA, DRE, and heredity were used to prepare cancer probability curves. Conclusion: The probability calculated by logistic regression provides better diagnostic accuracy for prostate cancer than the presently used multistep algorithms for estimation of the need to perform biopsy.

Identification of Novel Genes Regulated by IL-12, IL-4, or TGF-β during the Early Polarization of CD4 + Lymphocytes

Abstract: Th1 and Th2 cells arise from a common precursor cell in response to triggering through the TCR and cytokine receptors for IL-12 or IL-4. This leads to activation of complex signaling pathways, which are not known in detail. Disturbances in the balance between type 1 and type 2 responses can lead to certain immune-mediated diseases. Thus, it is important to understand how Th1 and Th2 cells are generated. To clarify the mechanisms as to how IL-12 and IL-4 induce Th1 and Th2 differentiation and how TGF-beta can inhibit this process, we have used oligonucleotide arrays to examine the early polarization of Th1 and Th2 cells in the presence and absence of TGF-beta. In addition to genes previously implicated in the process, we have identified 20 genes with various known and unknown functions not previously associated with Th1/2 polarization. We have also further determined which genes are targets of IL-12, IL-4, and TGF-beta regulation in the cells induced to polarize to Th1 and Th2 directions. Interestingly, a subset of the genes was coregulated by IL-12 or IL-4 and TGF-beta. Among these genes are candidates that may modulate the balance between Th1 and Th2 responses.

Online Purchase Intentions: An Empirical Testing of a Multiple-Theory Model

Abstract: Security and privacy issues have drawn much attention in the electronic commerce research area, and e-vendors have adjusted their online shopping systems to convince customers that vendors and systems are trustworthy. Therefore, this study concentrates on how consumers choose their purchasing channel when the environment is relatively secure. Is the choice based on preferring conversation with customer service, complexity of product, prior online shopping experience, social influence or perception of system usefulness or ease-of-use? The technology acceptance model, media richness theory and social influence model were combined in a research model tested with a Web survey. Based on the responses of 1,501 customers of a passenger cruise company, prior experience of traditional and online channels and perceived usefulness had a substantial effect on behavioral intention to use the online channel in the future.

Artificial neural networks in analytical review procedures

Abstract: This article gives an overview of artificial neural network (ANN) studies conducted in the auditing field. The review pays attention to application domains, data and sample sets, ANN‐architectures and learning parameters. The article argues that these auditing ANN‐applications could serve the analytical review (AR) process. The summary of the findings pays attention to whether authors state that ANNs have potential to improve analytical review (AR) procedures. Furthermore, the article evaluates which are the most influential contributions and which are open ends in the field. The article makes some practical suggestions to motivate academics and practitioners to collaborate in further exploration of the potential of ANNs.