EcoToxXplorer: Leveraging Design Thinking to Develop a Standardized Web-Based Transcriptomics Analytics Platform for Diverse Users
Bibliographic record
Abstract
The generation and use of transcriptomics data across the life sciences have risen sharply in recent years driven largely by advances in biotechnology and computational biology. Within the field of environmental toxicology, the data being generated from these efforts are providing important insights into stressor‐induced perturbations at the molecular level and helping increase understanding of causal linkages to connect such molecular perturbations with adverse outcomes at the whole‐organism level (Villeneuve et al., 2014). Despite impressive advances in these areas, the scope and pace of adoption of transcriptomics approaches in the practices of chemical risk assessment and environmental management have generally not met the expectations of their proponents (Mondou et al., 2021; Pain et al., 2020). A major challenge with transcriptomics data is that they can be complex and difficult for users to distill and synthesize into clear and actionable insights. Transcriptomics technologies can generate a tremendous amount of data, and accordingly the handling and analysis of these data require powerful computers and comprehensive databases along with bioinformatics and programming know‐how. Even studies of a few dozen genes can prove difficult for many users in terms of data management, analysis, and interpretation. These challenges are compounded for ecological species, which have far fewer and less developed knowledgebases and user‐friendly software tools compared to common model organisms. Further, tools that do exist are generally designed for ‘omics specialists rather than novice users.
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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 | 0.016 | 0.016 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.008 | 0.013 |
| Open science | 0.006 | 0.011 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.012 | 0.015 |
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".