A Picture is Worth 1000 Words: Using Pictorial Expression Data in Bioinformatics Assignments
Bibliographic record
Abstract
When learning bioinformatics, students are often given an unknown sequence and are required to perform a BLAST search to determine gene identity and % identity shared with genes in other species.The goal is usually for students to speculate the role of this unknown gene within their organism.We have found that access to visual information about expression patterns is very useful especially for non-experts like our students.Researchers from the University of Toronto developed the ePlant browsers that summarize expression data from thousands of experiments first in Arabidopsis (Winter et al. 2007) and now from a diverse array of plant species (as well as mice and humans).In this workshop we will use this online tool to explore expression of several genes in terms of tissue and subcellular specificity, developmental regulation, different physiological conditions and natural variation in different sub-species.It is also possible to look at a specific plant tissue or condition and find genes expressed within this tissue or condition.Expression data for any specific gene is linked with many other useful genomic tools.This tool could be used as a part of a genetics, developmental biology, cell biology, physiology or ecology lab.
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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 | 0.005 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.016 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.131 | 0.068 |
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".