R(<scp>NA</scp>)‐tistic expression: The art of matching unknown <scp>mRNA</scp> and proteins to environmental response in ecological genomics
Bibliographic record
Abstract
A challenge of modern ecological genomics is reducing uncertainty surrounding the biological inferences from gene expression. For example, approximately 40% of proteins in eukaryotic model organisms do not contain characterized domains (Gollery et al., 2006). Even proteins of "known function" are typically only characterized in the sense that they have a domain function, but provide no information on their biological role within the cell (e.g., activation, pathways or targets). Yet, as molecular ecologists, a common objective is to elucidate how organisms respond to environmental variation through changes in gene expression, including homoeostatic, acclimatory, and adaptive responses to environmental stressors, a challenge increased by poor protein ecological annotation. Now, in this issue of Molecular Ecology, Orsini et al. (2017) use the quintessential Daphnia system to characterize the differences in stress response in three genotypic backgrounds to common biotic and abiotic stressors found in nature. Using an optimized weighted gene co-expression network analysis, they link genes of unknown function to genes that they co-activate with and enrich for gene ontology. Determining the functional networks of genes that behave in genotype- and treatment-specific responses gives insight into possible pathways and respective ecological roles, helping pave the way for the next generation of transcriptomic studies in molecular ecology.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.005 |
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".