Conceptual and Empirical Challenges of Ascribing Functions to Transposable Elements
Bibliographic record
Abstract
Media attention and the subsequent scientific backlash engendered by the claim by spokespeople for the Encyclopedia of DNA Elements (ENCODE) project that 80% of the human genome has a biochemical function highlight the need for a clearer understanding of function concepts in biology. This article provides an overview of two major function concepts that have been developed in the philosophy of science--the causal role concept and the selected effects concept--and their relevance to ENCODE. Unlike in some previous critiques, the ENCODE project is not considered problematic here because it employed a causal role definition of function (which is relatively common in genetics) but because of how this concept was misused. In addition, several unique challenges that arise when dealing with transposable elements (TEs) but that were ignored by ENCODE are highlighted. These include issues surrounding TE-level versus organism-level selection, the origins versus the persistence of elements, and accidental versus functional organism-level benefits. Finally, some key questions are presented that should be addressed in any study aiming to ascribe functions to major portions of large eukaryotic genomes, the majorities of which are made up of transposable elements.
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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 | 0.066 | 0.141 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.006 | 0.090 |
| Scholarly communication | 0.011 | 0.031 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.006 | 0.015 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".