The Need for Centralization of Computational Biology Resources
Bibliographic record
Abstract
Biomedical research is benefiting from the wealth of new data generated in the laboratory through new instrumentation, greater computational resources, and massive repositories of public domain data. Using these data to make scientific discoveries is sometimes straightforward, but can be complicated by the number and breadth of public sources available to the researcher as well as by the plethora of tools from which to choose. Complex searches, analyses, or even storage needs require more computational expertise than that available within an individual laboratory. As biomedical researchers develop more computational skills, this may change over time. Having a centralized group of experts in computational biology can be of great value to the experimental biologist, and, recognizing this, many organizations have invested in building a team of computational biologists, bioinformaticists, and research IT services to address the needs of the investigators. This Editorial presents our views on the benefits and challenges of centralizing these activities. In order to benefit from expertise among existing teams of experts around the world, the “Bioinfo-Core” group was formed during the ISMB 2002 meeting in Edmonton, Canada, with approximately 25 initial members. Since then, the group has expanded in both organization and interest. Our worldwide membership now includes more than 150 people who administer centralized bioinformatics and research computing facilities within diverse organizations, including academia, independent research institutes, academic medical centers, and industry. Additionally, the group holds quarterly meetings via teleconference, continues an annual face-to-face meeting at ISMB (averaging 40–60 people), and hosts a mailing list and Wiki (http://www.bioinfo-core.org) to further communication.
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 | 0.064 | 0.102 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.022 | 0.032 |
| Open science | 0.010 | 0.015 |
| Research integrity | 0.007 | 0.018 |
| Insufficient payload (model declined to judge) | 0.013 | 0.006 |
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".