Visual analytics for supporting evidence-based interpretation of molecular cytogenomic findings
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it