Biomedical and Health Sciences
This is the protein PDB dataset for the article "Novel Algorithm for Improved Protein Classification Using Graph Similarity". This dataset consists of 9 classes of proteins.
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<p>All life on Earth is related, so that some molecular interactions are common across almost all living cells, with the number of common interactions increasing as we look at more closely related species. In particular, we expect the {\it protein-protein interaction} (PPI) networks of closely-related species to share high levels of similarity. This similarity may facilitate the transfer of functional knowledge between model species and human. {\it Multiple Network Alignment} is the process of uncovering the connection similarity between 3 or more networks simultaneously.
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All life on Earth is related, so that some molecular interactions are common across almost all living cells, with the number of common interactions increasing as we look at more closely related species. In particular, we expect the {\it protein-protein interaction} (PPI) networks of closely-related species to share high levels of similarity. This similarity may facilitate the transfer of functional knowledge between model species and human. {\it Multiple Network Alignment} is the process of uncovering the connection similarity between 3 or more networks simultaneously.
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These simulated live cell microscopy sequences were generated by the CytoPacq web service https://cbia.fi.muni.cz/simulator [R1]. The dataset is composed of 51 2D sequences and 41 3D sequences. The 2D sequences are divided into distinct 44 training and 7 test sets. The 3D sequences are divided into distinct 34 training and 7 test sets. Each sequence contains up to 200 frames.
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The dataset represents the negative interaction dataset of the Drugbank that has been generated from our proposed machine learning method based on drug similarity, which achieved an average accuracy of 95% compared to the randomly generated negative datasets in the literature. Drugbank was used as the drug target interaction dataset from https://go.drugbank.com/.
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Biomechanics has predominantly relied upon the trajectory optimization method for the analysis and prediction of the movement of the limbs. Such approaches have paved the way for the motion planning of biped and quadruped robots as well. Most of these methods are deterministic, utilizing first-order iterative gradient-based algorithms incorporating the constrained differentiable objective functions.
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Please cite the following paper when using this dataset:
N. Thakur, "Twitter Big Data as a Resource for Exoskeleton Research: A Large-Scale Dataset of about 140,000 Tweets from 2017–2022 and 100 Research Questions", Journal of Analytics, Volume 1, Issue 2, 2022, pp. 72-97, DOI: https://doi.org/10.3390/analytics1020007
Abstract
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Skeleton datasets for Normal, Antalgic, Stiff legged, Lurching, Steppage, and Trendelenburg gaits.
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Sequential skeleton and average foot pressure data for normal and five pathological gaits (i.e., antalgic, lurching, steppage, stiff-legged, and Trendelenburg) were simultaneously collected. The skeleton data were collected by using Azure Kinect (Microsoft Corp. Redmond, WA, USA). The average foot pressure data were collected by GW1100 (GHIWell, Korea). 12 healthy subjects participated in data collection. They simulated the pathological gaits under strict supervision. A total of 1,440 data instances (12 people x 6 gait types x 20 walkings) were collected.
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This dataset was created for an Eli Lilly and Company employee information management training program. It was part of a project that explored the potential use of ClinicalTrials.gov (CT.gov) as a tool to evaluate the severity of changes to inclusion and exclusion criteria on clinical trial operations. CT.gov is a public clinical study registry that records summary data about clinical trials. The registry includes a historical record of changes (change history) to inclusion and exclusion criteria and other data that is accessible by users.
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