Visual analytics for supporting evidence-based interpretation of molecular cytogenomic findings
Bibliographic record
Abstract
Interpreting molecular cytogenomic findings that cover the human genome (e.g., microarray results) is challenging, as it requires accessing and working with multiple, diverse sources of data that are often large and heterogeneous. These data need to be accessed, queried, and simultaneously integrated to achieve open-ended goals, such as interpreting findings to make diagnoses and engage in genetic counselling. Currently, typical workflows of users are laborious, as data sources are often not integrated and must be accessed separately. Furthermore, large document sets often have to be combed through to assist in interpretation. Analytics tools are needed to help users process and distill large bodies of information into manageable sizes so the most relevant portions can be focused on. Current tools typically do not offer support for interactively exploring and engaging with visual representations of important entities and relationships (e.g., chromosomes, gene-phenotype relationships, and scientific articles). We present VErdICT, a visual analytics tool that can support users in their interpretation of molecular cytogenomic findings. A participatory design approach was taken to make VErdICT human-centered. We describe its development, usability and usefulness, and outline some future research challenges.
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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 | 0.024 | 0.089 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.010 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".