On the other hand, Ray and Teizer (2012) utilize a Kinect for the pose analysis of construction workers to classify awkward postures based on ergonomic rules during safety and health training, and Han et al. (2013) study the unsafe action detection of workers for safety behavior monitoring with motion capture data from a Kinect.
By docking pose analysis, it is evident that all the inactive compounds were poorly occupied in the binding site.
A review paper [21] provided an overview of usage of first-generation Kinect 360 sensor for human activity analysis, including body pose and activity recognition, and hand gesture analysis; however, they do not include any references on using head gaze information.
In addition, there are the more common problems related to heterogeneity within clinical samples, the complex, non-standardized confounding variables associated with human subjects and the complexities posed by the analysis and validation of highly parallel data.
Such studies are critical in that they analyse disease-relevant tissue [ e.g. whole mouse aorta (Skogsberg et al, 2008) or human coronary and carotid arteries (Cagnin et al, 2009)] but they pose numerous analysis challenges.
Applications of these methods not only produce a great bounty of data and potential for enhancing our understanding of the budding yeast and other organisms, but also pose a considerable data analysis and interpretation challenge.
The high variability of study methods and settings as well as the poor quality of data derived from the published literature pose major difficulties for analysis and the drawing of firm, generalizable conclusions based on study results.
Emergent technologies from molecular biology that record phenotypes of single and double mutants at a large, possibly genomic scale, prompt for the development of systematic approaches for epistasis analysis and pose the need to devise computational tools that support gene network inference.
A detailed analysis of pose prediction and virtual screening results in dependence of the number of protein structures considered in the conformational ensemble will be presented and limitations of the approach will be highlighted.
The emergence of high-dimensional datasets poses challenges for the analysis and identifications of dynamical network patterns, because many traditional statistical tools may lose their efficiency dramatically as the numbers of dimensions increases.
