Teaching expression proteomics: From the wet‐lab to the laptop
Bibliographic record
Abstract
Expression proteomics has become, in recent years, a key genome-wide expression approach in fundamental and applied life sciences. This postgenomic technology aims the quantitative analysis of all the proteins or protein forms (the so-called proteome) of a given organism in a given environmental and genetic context. It is a challenge to provide effective training in this area due to its demanding laboratory procedures and laborious computational data analysis. However, the effective training of undergraduates and postgraduates in this field is highly recommended to prepare them for the challenges of postgenomic research and of medical, industrial and other economical activities. Since 2004, the area of Biological Sciences at the Department of Chemical and Biological Engineering of Instituto Superior Técnico (IST) has been teaching Expression Proteomics to undergraduate and postgraduate students in three formats: 1) as modules of curricular units (CU), in particular of Functional Genomics and Bioinformatics (FGB), offered as a mandatory CU to IST Biological Engineering or Biotechnology Master courses students, or as an elective CU to other MSc courses with a biological component and to the MSc in Information Systems and Computer Engineering; the topic is also part of the PhD program in Biotechnology; 2) as mentored coaching, in which IST students integrate ongoing research programs at the Biological Sciences Research Group of IBB at IST; and 3) as intensive thematic courses open to the external community. In this article, educational programs and teaching methodologies and tools that we have been using are outlined, from the wet-lab to the laptop. The current role of quantitative proteomics in biological research, with emphasis on microbial stress response and on biomedical and biotechnological applications, is addressed, as a case-study, anchored on our group research activities.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 0.015 |
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".