Biomedical and Health Sciences

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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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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The research were incorporated an extended cohort monitoring campaign, validation of an existing exposure model and development of a predictive model for COPD exacerbations evaluated against historical electronic health records.A miniature personal sensor unit were manufactured for the study from a prototype developed at the University of Cambridge. The units monitored GPS position, temperature, humidity, CO, NO, NO2, O3, PM10 and PM2.5.Three 6-month cohort monitoring campaigns were carried out, each including of 65 COPD patients.

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Columns are genes, miRNAs, drugs, or cnv. Rows are patient identifiers or cell lines.

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Supplementary materials for paper 

'In silico drug repositioning using integrated target information'

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  • supplemental materials for paper "
  • Three dimensional (3D) optical imaging system of muon beams using a silver activated zinc sulfide (ZnS(Ag)) sheet combined with a mirror"
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