LAB-AID (Laboratory Automated Interrogation of Data): an interactive web application for visualization of multi-level data from biological experiments
Kozic, Z.; Booker, S.; Dando, O.; Kind, P.
Show abstract
A key step in understanding the results of biological experiments is visualization of the data. Many laboratory experiments contain a range of measurements that exist within a hierarchy of interdependence. An automated way to visualise and interrogate experimental data would: 1) lead to improved understanding of the results, 2) help to determine which statistical tests should be performed, and 3) easily identify outliers and sources of batch effects. Unfortunately, existing graphing solutions often demand expertise in programming, require considerable effort to import and examine such multi-level data, or are unnecessarily complex for the task at hand. Here we present LAB-AID (Laboratory Automated Interrogation of Data), an interactive tool specifically designed to automatically visualize and query hierarchical data resulting from biological experiments.
Matching journals
The top 7 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- The Image Data Explorer: interactive exploration of image-derived data 95%
- VICR: A Novel Software for Unbiased Video and Image Analysis in Scientific Research 94%
- Countering reproducibility issues in mathematical models with software engineering techniques: A case study using a one-dimensional mathematical model of the atrioventricular node 94%
Similar papers in this journal
- Slitflow: a Python framework for single-molecule dynamics and localization analysis 96%
- AlliGator: Open Source Fluorescence Lifetime Imaging Analysis in G 93%
- ODAMNet: a Python package to identify molecular relationships between chemicals and rare diseases using overlap, active module and random walk approaches 93%
Similar papers in this journal
- WAVES (Web-based tool for Analysis and Visualization of Environmental Samples) – a web application for visualization of wastewater pathogen sequencing results 94%
- SGI: Automatic clinical subgroup identification in omics datasets 94%
- treeheatr: an R package for interpretable decision tree visualizations 93%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.