Goalkeeper Game: A New Assessment Tool for Prediction of Gait Performance Under Complex Condition in People With Parkinson's Disease.

Background: People with Parkinson's disease (PD) display poorer gait performance when walking under complex conditions than under simple conditions. Screening tests that evaluate gait performance changes under complex walking conditions may be valuable tools for early intervention, especially if allowing for massive data collection. Objectives: To investigate the use of the Goalkeeper Game (GG) to predict impairment in gait performance under complex conditions in people with Parkinson's disease (PPD) and compare its predictive power with the one of the Montreal Cognitive Assessment (MoCA) test. Methods: 74 PPD (HY stages: 23 in stage 1; 31 in stage 2; 20 in stage 3), without dementia (MoCA cut-off 21), tested in ON period with dopaminergic medication were submitted to single individual cognitive/motor evaluation sessions. MoCA and GG were used to assess cognition, and the dynamic gait index (DGI) test was used to assess gait performance under complex condition. GG test resulted in 9 measures extracted via a statistical model. The predictive power of the GG measures and the MoCA score with respect to gait performance, as assessed by DGI, were compared. Results: The predictive models based on GG obtained a better score of prediction (65%) then MoCA (56%) for DGI scores (at a 50% specificity). Conclusion: GG is a novel tool for noninvasive screening that showed a superior predictive power in assessing gait performance under complex condition in people with PD than the well-established MoCa test.

A New Approach for Identification of Cancer-related Pathways using Protein Networks and Genomic Data.

Cancer cells have anomalous development and proliferation due to disturbances in their control systems. The study of the behavior of cellular control system requires high-throughput dynamical data. Unfortunately, this type of data is not largely available. This fact motivates the main issue of this article: how to use static omics data and available biological knowledge to get new information about the elements of the control system in cancer cells. Two important measures to access the state of the cellular control system are the gene expression profile and the signaling pathways. This article uses a combination of these two static omics data to gain insights on the states of a cancer cell. To extract information from this kind of data, a statistical computational model was formalized and implemented. In order to exemplify the application of some aspects of the developed conceptual framework, we verified the hypothesis that different types of cancer cells have different disturbed signaling pathways. To this end, we developed a method that recovers small protein networks, called motifs, which are differentially represented in some subtypes of breast cancer. These differentially represented motifs are enriched with specific gene ontologies as well as with new putative cancer genes.

An environment for knowledge discovery in biology.

This paper describes a data mining environment for knowledge discovery in bioinformatics applications. The system has a generic kernel that implements the mining functions to be applied to input primary databases, with a warehouse architecture, of biomedical information. Both supervised and unsupervised classification can be implemented within the kernel and applied to data extracted from the primary database, with the results being suitably stored in a complex object database for knowledge discovery. The kernel also includes a specific high-performance library that allows designing and applying the mining functions in parallel machines. The experimental results obtained by the application of the kernel functions are reported.