scholarly journals CalR: A Web-based Analysis Tool for Indirect Calorimetry Experiments

2018 ◽  
Author(s):  
Amir I. Mina ◽  
Raymond A. LeClair ◽  
Katherine B. LeClair ◽  
David E. Cohen ◽  
Louise Lantier ◽  
...  
Keyword(s):  
Indirect Calorimetry ◽  
Analysis Tool ◽  
Web Based

scholarly journals CalR: A Web-Based Analysis Tool for Indirect Calorimetry Experiments

2018 ◽  
Vol 28 (4) ◽  
pp. 656-666.e1 ◽  
Author(s):  
Amir I. Mina ◽  
Raymond A. LeClair ◽  
Katherine B. LeClair ◽  
David E. Cohen ◽  
Louise Lantier ◽  
...  
Keyword(s):  
Indirect Calorimetry ◽  
Analysis Tool ◽  
Web Based

scholarly journals CalR: A Web-based Analysis Tool for Indirect Calorimetry Experiments

2017 ◽  
Author(s):  
Amir I. Mina ◽  
Raymond A. LeClair ◽  
Katherine B. LeClair ◽  
David E. Cohen ◽  
Louise Lantier ◽  
...  
Keyword(s):  
Energy Balance ◽  
Energy Expenditure ◽  
Indirect Calorimetry ◽  
Statistical Tests ◽  
General Linear Model ◽  
Analysis Tool ◽  
Analysis Software ◽  
Web Based ◽  
Standard Tool ◽  
Data Files

AbstractWe report a web-based tool for analysis of indirect calorimetry experiments which measure physiological energy balance. CalR easily imports raw data files, generates plots, and determines the most appropriate statistical tests for interpretation. Analysis with the general linear model (which includes ANOVA and ANCOVA) allows for flexibility to interpret experiments of obesity and thermogenesis. Users may also produce standardized output files of an experiment which can be shared and subsequently re-evaluated using CalR. This framework will provide the transparency necessary to enhance consistency and reproducibility in experiments of energy expenditure. CalR analysis software will greatly increase the speed and efficiency with which metabolic experiments can be organized, analyzed according to accepted norms, and reproduced—and will likely become a standard tool for the field. CalR is accessible at https://CalR.bwh.harvard.edu.Graphical Abstract

scholarly journals Omics AnalySIs System for PRecision Oncology (OASISPRO): a web-based omics analysis tool for clinical phenotype prediction

2017 ◽  
Vol 34 (2) ◽  
pp. 319-320 ◽  
Author(s):  
Kun-Hsing Yu ◽  
Michael R Fitzpatrick ◽  
Luke Pappas ◽  
Warren Chan ◽  
Jessica Kung ◽  
...  
Keyword(s):  
Clinical Phenotype ◽  
Analysis Tool ◽  
Precision Oncology ◽  
Phenotype Prediction ◽  
Web Based ◽  
Omics Analysis ◽  
Analysis System

Interactive Web‐based Data Visualization and Analysis Tool for Synthetizing on‐farm Research Networks Data

2020 ◽  
Author(s):  
Anabelle Laurent ◽  
Xiaodan Lyu ◽  
Peter Kyveryga ◽  
David Makowski ◽  
Heike Hofmann ◽  
...  
Keyword(s):  
Data Visualization ◽  
Analysis Tool ◽  
Research Networks ◽  
Web Based ◽  
On Farm

scholarly journals KMplot.com: a survival analysis tool tailored for medical research (Preprint)

2021 ◽  
Author(s):  
András Lánczky ◽  
Balázs Győrffy
Keyword(s):  
Survival Analysis ◽  
Medical Research ◽  
Proportional Hazards ◽  
Cox Proportional Hazards ◽  
Multiple Hypothesis Testing ◽  
Analysis Tool ◽  
Web Based ◽  
False Discovery ◽  
Using Data ◽  
Basic Medical

UNSTRUCTURED Survival analysis is a cornerstone of medical research enabling the assessment of clinical outcome for disease progression and treatment efficiency. Despite its central importance, neither commonly used spreadsheet software can handle it nor is there a web server for its computation. Here we introduce a web-based tool capable to perform uni- and multivariate Cox proportional hazards survival analysis using data generated by genomic, transcriptomic, proteomic, or metabolomics studies. We implemented different methods to establish cutoff values for trichotomization or for the dichotomization of continuous data. False discovery rate is computed to correct for multiple hypothesis testing. Multivariate analysis option enables comparing omics data with clinical variables. The registration-free web-service is available at https://kmplot.com/custom_data. The tool fills a gap and will be an invaluable help for basic medical and clinical research.

scholarly journals iCn3D: from Web-based 3D Viewer to Structure Analysis Tool in Batch Mode

2021 ◽  
Author(s):  
Jiyao Wang ◽  
Philippe Youkharibache ◽  
Aron Marchler-Bauer ◽  
Christopher Lanczycki ◽  
Dachuan Zhang ◽  
...  
Keyword(s):  
Protein Interaction ◽  
Large Data ◽  
Accessible Surface Area ◽  
Solvent Accessible Surface Area ◽  
Analysis Tool ◽  
Batch Mode ◽  
Data Set ◽  
Web Based ◽  
Protein Protein Interaction ◽  
Link Type

AbstractiCn3D was originally released as a web-based 3D viewer, which allows users to create a custom view in a life-long, shortened URL to share with colleagues. Recently, iCn3D was converted to use JavaScript classes and could be used as a library to write Node.js scripts. Any interactive features in iCn3D can be converted to Node.js scripts to run in batch mode for a large data set. Currently the following Node.js script examples are available at https://github.com/ncbi/icn3d/tree/master/icn3dnode: ligand-protein interaction, protein-protein interaction, change of interactions due to residue mutations, DelPhi electrostatic potential, and solvent accessible surface area. iCn3D PNG images can also be exported in batch mode using a Python script. Other recent features of iCn3D include the alignment of multiple chains from different structures, realignment, dynamic symmetry calculation for any subsets, 2D cartoons at different levels, and interactive contact maps. iCn3D can also be used in Jupyter Notebook as described at https://pypi.org/project/icn3dpy.

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