Developing a protocol for bioinformatics analysis: An integrated information behavior and task analysis approach: Research Articles
Bibliographic record
Abstract
The purpose of this research is to capture, understand, and model the process used by bioinformatics analysts when facing a specific scientific problem. Integrating information behavior with task analysis, we interviewed 20 bioinformatics experts about the process they follow to conduct a typical bioinformatics analysis—a functional analysis of a gene, and then used a task analysis approach to model that process. We found that each expert followed a unique process in using bioinformatics resources, but had significant similarities with their peers. We synthesized these unique processes into a standard research protocol, from which we developed a procedural model that describes the process of conducting a functional analysis of a gene. The model protocol consists of a series of 16 individual steps, each of which specifies detail for the type of analysis, how and why it is conducted, the tools used, the data input and output, and the interpretation of the results. The linking of information behavior and task analysis research is a novel approach, as it provides a rich high-level view of information behavior while providing a detailed analysis at the task level. In this article we concentrate on the latter. © 2005 Wiley Periodicals, Inc.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
| gpt | no category Domain: not available · Genre: Protocol About the Canadian research system: no · About a Canadian topic: no | Not applicable | high |
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 | 0.144 | 0.277 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.007 | 0.011 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.013 | 0.011 |
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, unvalidatedLabeled directly by 2 models reading the full record.
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".