Implementing a web‐based introductory bioinformatics course for non‐bioinformaticians that incorporates practical exercises
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| 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 it