University of Mannheim

About: University of Mannheim is a education organization based out in Mannheim, Germany. It is known for research contribution in the topics: Population & European union. The organization has 4448 authors who have published 12918 publications receiving 446557 citations. The organization is also known as: Uni Mannheim & UMA.

Google goes cancer: improving outcome prediction for cancer patients by network-based ranking of marker genes.

Abstract: Predicting the clinical outcome of cancer patients based on the expression of marker genes in their tumors has received increasing interest in the past decade. Accurate predictors of outcome and response to therapy could be used to personalize and thereby improve therapy. However, state of the art methods used so far often found marker genes with limited prediction accuracy, limited reproducibility, and unclear biological relevance. To address this problem, we developed a novel computational approach to identify genes prognostic for outcome that couples gene expression measurements from primary tumor samples with a network of known relationships between the genes. Our approach ranks genes according to their prognostic relevance using both expression and network information in a manner similar to Google's PageRank. We applied this method to gene expression profiles which we obtained from 30 patients with pancreatic cancer, and identified seven candidate marker genes prognostic for outcome. Compared to genes found with state of the art methods, such as Pearson correlation of gene expression with survival time, we improve the prediction accuracy by up to 7%. Accuracies were assessed using support vector machine classifiers and Monte Carlo cross-validation. We then validated the prognostic value of our seven candidate markers using immunohistochemistry on an independent set of 412 pancreatic cancer samples. Notably, signatures derived from our candidate markers were independently predictive of outcome and superior to established clinical prognostic factors such as grade, tumor size, and nodal status. As the amount of genomic data of individual tumors grows rapidly, our algorithm meets the need for powerful computational approaches that are key to exploit these data for personalized cancer therapies in clinical practice.

Segmenting cyberspace: a customer typology for the internet

Abstract: Purpose – This research sets about discovering if it is possible to identify distinct, practice‐relevant and addressable clusters by means of selected criteria for constructing typologies – such as psychographic, culturally‐specific and purchasing behaviour‐relevant features – which permit an online supplier to efficiently and effectively focus on attractive consumer segments.Design/methodology/approach – To answer the research question, the study conducted an online survey with 1,011 participants. The respondents were from three countries, each with culturally‐distinct features: France, Germany and the US. Underpinning the research are theoretically‐secured constructs of purchasing behaviour on the internet.Findings – Cluster analysis confirmed the outstanding validity of a three‐cluster‐solution (97.7 per cent score) – risk‐averse doubters, open‐minded online shoppers, and reserved information‐seekers. Discriminant analysis shows that certain constructs, particularly “neuroticism”, “willingness to buy” ...