Driving Evolution Towards Discovery of Patterns in Sets of Weakly-Conserved DNA Sequences
Bibliographic record
Abstract
An evolutionary algorithm is used to evolve a population of self-driving automata (SDAs), modified state machines, that are used to produce output in the form of a DNA sequence. The SDAs are evaluated based on their ability to create sequences that closely match all DNA sequences within a given set. This is evaluated in a pairwise fashion, but attempts to match all sequences concurrently, using two fitness functions that are differentiated by whether they allow for gaps. Additionally, a secondary fitness metric, Sequence Diversity Fitness, encourages diversity among the output of the SDAs within the population throughout evolution. The target sequences are Φ-segments of dehydrin proteins, which are weakly-conserved, can vary considerably in length, and for which traditional methods fail when used to find patterns within them. The ultimate goal is to use SDAs to assist in identifying patterns within Φ-segments. Locating such a pattern could prove fruitful for understanding the functions of dehydrins and how they contribute to the protection of plants from abiotic stresses. Several sets of target sequences are used for analysis, with some sets being more closely-related than others. The evolutionary algorithm was found to produce sequences that matched (according to one of the fitness functions) up to 100% of a given set of target sequences under certain conditions, with closely-related sequences being more accurately matched.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".