Movement analysis provides a vast amount of data, which, frequently, are not used in the clinical decision-making process. For example, traditional gait data visualization is based on a time-based display of joint angles, but part of the information is lost when these time-series are averaged across different gait strides. Horizon graph is a data display method that increases the density of time-series data by horizontally dividing and layering multiple filled line graphs. This higher data density increases the amount of information displayed in the same graph and, consequently, enables visual data comparisons between multiple time series. Horizon graph of kinematic data allows displaying several cycles of different joints and their respective continuous symmetry ratio between sides. The aim of this work is to introduce the Horizon graph as a method to analyze kinematic gait data and help to characterize its symmetry. Examples of Horizon graph application to running is offered. Horizon graph may prove to be a useful clinical tool to visualize kinematic time-series and facilitate their clinical interpretation.•Continuous gait time series is a powerful tool for clinical analysis.•Horizon graph, higher data density graph, increases the information displayed.•Horizon graph is a clinical tool to visualize kinematic curves.
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http://dx.doi.org/10.1016/j.mex.2021.101361 | DOI Listing |
Proc Natl Acad Sci U S A
December 2024
Department of Biology, University of Oxford, Oxford OX1 3SZ, United Kingdom.
Bioinformatics
December 2024
Institute of Bioengineering, School of Life Sciences, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne CH-1015, Switzerland.
Motivation: Identifying interacting partners from two sets of protein sequences has important applications in computational biology. Interacting partners share similarities across species due to their common evolutionary history, and feature correlations in amino acid usage due to the need to maintain complementary interaction interfaces. Thus, the problem of finding interacting pairs can be formulated as searching for a pairing of sequences that maximizes a sequence similarity or a coevolution score.
View Article and Find Full Text PDFBioinformatics
December 2024
Department of Biochemistry, School of Medicine, Universidad Autonoma de Madrid, Madrid 28029, Spain.
Motivation: Accumulation models, where a system progressively acquires binary features over time, are common in the study of cancer progression, evolutionary biology, and other fields. Many approaches have been developed to infer the accumulation pathways by which features (e.g.
View Article and Find Full Text PDFInt J Med Inform
December 2024
The College of Nursing, Chonnam National University Hospital, Gwangju, South Korea.
Objective: In hospitals globally, the occurrence of clinical deterioration within the hospital setting poses a significant healthcare burden. Rapid clinical intervention becomes a crucial task in such cases. In this research, we propose an end-to-end deep learning architecture that interpolates high-dimensional sequential data for the early detection of clinical deterioration events.
View Article and Find Full Text PDFJ Cheminform
December 2024
Osmo Labs, PBC, 450 E 29th St, New York, USA.
Hyperparameter optimization is very frequently employed in machine learning. However, an optimization of a large space of parameters could result in overfitting of models. In recent studies on solubility prediction the authors collected seven thermodynamic and kinetic solubility datasets from different data sources.
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