Evolving research trends in bioinformatics
Bibliographic record
Abstract
The cross-disciplinary nature of bioinformatics entails co-evolution with other biomedical disciplines, whereby some bioinformatics applications become popular in certain disciplines and, in turn, these disciplines influence the focus of future bioinformatics development efforts. We observe here that the growth of computational approaches within various biomedical disciplines is not merely a reflection of a general extended usage of computers and the Internet, but due to the production of useful bioinformatics databases and methods for the rest of the biomedical scientific community. We have used the abstracts stored both in the MEDLINE database of biomedical literature and in NIH-funded project grants, to quantify two effects. First, we examine the biomedical literature as a whole and find that the use of computational methods has become increasingly prevalent across biomedical disciplines over the past three decades, while use of databases and the Internet have been rapidly increasing over the past decade. Second, we study the recent trends in the use of bioinformatics topics. We observe that molecular sequence databases are a widely adopted contribution in biomedicine from the field of bioinformatics, and that microarray analysis is one of the major new topics engaged by the bioinformatics community. Via this analysis, we were able to identify areas of rapid growth in the use of informatics to aid in curriculum planning, development of computational infrastructure and strategies for workforce education and funding.
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 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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.012 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".