This paper outlines the major components and function of the technologically integrated oncosimulator developed primarily within the Advancing Clinico Genomic Trials on Cancer (ACGT) project. The Oncosimulator is defined as an information technology system simulating in vivo tumor response to therapeutic modalities within the clinical trial context. Chemotherapy in the neoadjuvant setting, according to two real clinical trials concerning nephroblastoma and breast cancer, has been considered. The spatiotemporal simulation module embedded in the Oncosimulator is based on the multiscale, predominantly top-down, discrete entity-discrete event cancer simulation technique developed by the In Silico Oncology Group, National Technical University of Athens. The technology modules include multiscale data handling, image processing, invocation of code execution via a spreadsheet-inspired environment portal, execution of the code on the grid, and the visualization of the predictions. A refining scenario for the eventual coupling of the oncosimulator with immunological models is also presented. Parameter values have been adapted to multiscale clinical trial data in a consistent way, thus supporting the predictive potential of the oncosimulator. Indicative results demonstrating various aspects of the clinical adaptation and validation process are presented. Completion of these processes is expected to pave the way for the clinical translation of the system.

Biomedical research has recently seen a vast growth in publicly and instantly available information, which are often complementary or overlapping. As the available resources become more specialized, there is a growing need for multidisciplinary collaborations between biomedical researchers to address complex research questions. We present an application of a data-mining algorithm to gene-expression data in a collaborative decision-making support environment, as a typical example of how multidisciplinary researchers can collaborate in analyzing and biologically interpreting gene-expression micro array data. Through the proposed approach, researchers can easily decide about which data repositories should be considered, analyze the algorithmic results, discuss the weaknesses of the patterns identified, and set up new iterations of the data mining algorithm by defining other descriptive attributes or integrating other relevant data.

Supervised local pattern discovery aims to find subsets of a database with a high statistical unusualness in the distribution of a target attribute. Local pattern discovery is often used to generate a human-understandable representation of the most interesting dependencies in a data set. Hence, the more crisp and concise the output is, the better. Unfortunately, standard algorithm often produce very large and redundant outputs.

Subgroup discovery is the task of identifying the top k patterns in a database with most significant deviation in the distribution of a target attribute Y. Subgroup discovery is a popular approach for identifying interesting patterns in data, because it combines statistical significance with an understandable representation of patterns as a logical formula. However, it is often a problem that some subgroups, even if they are statistically highly significant, are not interesting to the user. We present an approach based on the work on ranking Support Vector Machines that ranks subgroups with respect to the user's concept of interestingness, and finds more interesting subgroups. This approach can significantly increase the quality of the subgroups.

In this paper, we describe an extension to the ACGT GridR environment which allows the parallelization of loops in R scripts in view of their distributed execution on a computational grid. The ACGT GridR service is extended by a component that uses a set of preprocessor-like directives to organize and distribute calculations. The use of parallelization directives as special R comments provides users with the potential to accelerate lengthy calculations with changes to preexisting code. The GridR service and its extension are developed as components of the ACGT platform, one aim of which is to facilitate the data mining of clinical trials involving large datasets. In ACGT, GridR scripts are executed in the framework of a specifically developed workflow environment, which is also briefly outlined in the present article.

In this paper, we describe an analysis tool based on the statistical environment R, GridR, which allows using the collection of methodologies available as R packages in a grid environment. The aim of GridR, which was initiated in the context of the EU project Advancing Clinico-Genomics Trials on Cancer (ACGT), is to provide a powerful framework for the analysis of clinico-genomic trials involving large amount of data (e.g. microarray-based clinical trials). As a proof of concept, an example of microarray-based analysis taken from the literature was reproduced using GridR. As GridR will ultimately be made available to the ACGT community as a web service, we are sketching the ACGT project and its architecture in the present article as well.

With the completion of the human genome and the entrance into the post-genomic era, translational research rises as a major need. In this paper, we present a knowledge discovery workflow (KDw) and its utilization in the context of clinico-genomic trials. KDw aims towards the discovery of 'evidential' correlations between patients' genomic and clinical profiles. Application of KDw on a real-world clinico-genomic (breast cancer) study demonstrates the reliability, efficacy, and efficiency of the approach.

The analysis of temporal data is an important issue in current research, because most real-world data either explicitly or implicitly contains some information about time. The key to successfully solving temporal learning tasks is to analyze the assumptions and prior knowledge that can be made about the temporal process of the learning problem and find a representation of the data and a learning algorithm that makes effective use of this knowledge. The paper presents a concise overview of the application of support vector machines to different temporal learning tasks and the corresponding temporal representations.

Support vector machines (SVMs) have become a popular tool for machine learning with large amounts of high dimensional data. In this paper an approach for incremental learning with support vector machines is presented, that improves the existing approach of Syed et al. (1999). An insight into the interpretability of support vectors is also given.