Use of Figures in Literature Mining for Biomedical Digital Libraries
Bibliographic record
Abstract
The maintenance of biomedical digital libraries (including organism databases and protein databases) involves analysis of a large number of documents. Much work is done manually: curators study large numbers of biomedical documents while updating and annotating organism databases such as MGI (mouse genome informatics) and Flybase (a database of the fruit-fly genome). We summarize the annotation process in organism databases, and describe some of the roles played by the gene ontology and by document databases such as PubMed. Efforts are ongoing to automate parts of the annotation process. Biomedical text mining contests, such as the TREC Genomics Track (Hersh et al., 2004, 2005), define annotation subtasks, and provide training and test data. So far, these efforts have focused on the analysis of the text content of documents. We are investigating the analysis of figures in biomedical documents; the information derived from figure analysis may later be combined with the information derived from text analysis. We present an algorithm for using figures in document triage; triage involves determining which documents are relevant to a given annotation task. In our triage algorithm, we segment figures into subfigures and classify the subfigures as graphical, gel, fluorescence microscopy, and other microscopy. A secondary classification into subcategories is performed by clustering, using clusters created from the subfigures in the labeled training data. The classifications of all subfigures in a document are combined to form a document descriptor. The document descriptor is then classified using a naive Bayes classifier, as either relevant or irrelevant to the given annotation task.
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.
How this classification was reachedexpand
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 | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".