Robust gene signatures from microarray data using genetic algorithms enriched with biological pathway keywords.

Genetic algorithms are widely used in the estimation of expression profiles from microarrays data. However, these techniques are unable to produce stable and robust solutions suitable to use in clinical and biomedical studies. This paper presents a novel two-stage evolutionary strategy for gene feature selection combining the genetic algorithm with biological information extracted from the KEGG database. A comparative study is carried out over public data from three different types of cancer (leukemia, lung cancer and prostate cancer). Even though the analyses only use features having KEGG information, the results demonstrate that this two-stage evolutionary strategy increased the consistency, robustness and accuracy of a blind discrimination among relapsed and healthy individuals. Therefore, this approach could facilitate the definition of gene signatures for the clinical prognosis and diagnostic of cancer diseases in a near future. Additionally, it could also be used for biological knowledge discovery about the studied disease.

Addressing critical issues in the development of an Oncology Information System.

PURPOSES: This paper presents the experience on the design and implementation of a user-centered Oncology Information System developed for the Medical Oncology Department at the "Hospital Universitario Virgen de la Victoria", in Málaga, Spain. The project focused on the aspects considered in the literature as critical factors for a successful deployment and usage of a health information system. METHODS: System usability, adequate technology, integration of clinical routines, real-time statistical analysis of data, information confidentiality and standard protocol-based external interconnection were the key aspects considered. RESULTS: The developed system is based on a web application with a modular and layered architecture accounting for usability, ease of maintenance and further system development. Evaluation of system usability was carried at three and fifteen months after system deployment to analyze the advantages/disadvantages experienced by the end-users. CONCLUSIONS: A thorough prior analysis of clinical activities and workflows, the use of the adequate technology, and the availability of data analysis tools will almost guarantee success in the deployment of an Oncology Information System.

WIMP: web server tool for missing data imputation.

The imputation of unknown or missing data is a crucial task on the analysis of biomedical datasets. There are several situations where it is necessary to classify or identify instances given incomplete vectors, and the existence of missing values can much degrade the performance of the algorithms used for the classification/recognition. The task of learning accurately from incomplete data raises a number of issues some of which have not been completely solved in machine learning applications. In this sense, effective missing value estimation methods are required. Different methods for missing data imputations exist but most of the times the selection of the appropriate technique involves testing several methods, comparing them and choosing the right one. Furthermore, applying these methods, in most cases, is not straightforward, as they involve several technical details, and in particular in cases such as when dealing with microarray datasets, the application of the methods requires huge computational resources. As far as we know, there is not a public software application that can provide the computing capabilities required for carrying the task of data imputation. This paper presents a new public tool for missing data imputation that is attached to a computer cluster in order to execute high computational tasks. The software WIMP (Web IMPutation) is a public available web site where registered users can create, execute, analyze and store their simulations related to missing data imputation.