Implementing a web‐based introductory bioinformatics course for non‐bioinformaticians that incorporates practical exercises
Bibliographic record
Abstract
A recent scientific discipline, bioinformatics, defined as using informatics for the study of biological problems, is now a requirement for the study of biological sciences. Bioinformatics has become such a powerful and popular discipline that several academic institutions have created programs in this field, allowing students to become specialized. However, biology students who are not involved in a bioinformatics program also need a solid toolbox of bioinformatics software and skills. Therefore, we have developed a completely online bioinformatics course for non-bioinformaticians, entitled "BIF-1901 Introduction à la bio-informatique et à ses outils (Introduction to bioinformatics and bioinformatics tools)," given by the Department of Biochemistry, Microbiology, and Bioinformatics of Université Laval (Quebec City, Canada). This course requires neither a bioinformatics background nor specific skills in informatics. The underlying main goal was to produce a completely online up-to-date bioinformatics course, including practical exercises, with an intuitive pedagogical framework. The course, BIF-1901, was conceived to cover the three fundamental aspects of bioinformatics: (1) informatics, (2) biological sequence analysis, and (3) structural bioinformatics. This article discusses the content of the modules, the evaluations, the pedagogical framework, and the challenges inherent to a multidisciplinary, fully online course. © 2017 by The International Union of Biochemistry and Molecular Biology, 46(1):31-38, 2018.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.036 | 0.019 |
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".