Omics BioAnalytics: Reproducible Research using R Shiny and Alexa
Bibliographic record
Abstract
Abstract Summary High-throughput technologies produce complex high-dimensional datasets which are analyzed using a variety of ever-evolving bioinformatics tools. Well-designed web frameworks enable more intuitive and efficient analysis such that less time is spent on coding and more time is spent on interpretation of results and addressing insightful biological questions aided by interactive visualizations. Here, we present Omics BioAnalytics, a full-service Web framework that enables comprehensive, multi-level characterization, analysis, and integration of omics datasets. Blending web-based (R Shiny) and voice-based (Amazon’s Alexa) analytics, Omics BioAnalytics can be used both by expert computational biologists and non-coding biological domain experts, alike. Our web framework can be utilized to explore complex datasets and identify biosignatures and discriminative biomarkers of health and disease processes, and generate testable hypotheses relating to underlying molecular mechanisms. Availability Omics BioAnalytics is freely available at https://github.com/singha53/omicsBioAnalytics and the web app is deployed at https://amritsingh.shinyapps.io/omicsBioAnalytics/ . The source code for the companion Alexa Skill can be found at https://github.com/singha53/omics-bioanalytics-alexa-skill .
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.022 | 0.059 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.046 | 0.058 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".