Computational modeling of genetic processes in stichotrichous ciliates
Bibliographic record
Abstract
This thesis concerns itself primarily with the study of the process of gene unscrambling in stichotrichous ciliates as a computational model. We begin by presenting an in vitro model of DNA computing, based on circular insertions and deletions, that serves as part of the basis for one of the investigated models of gene descrambling in ciliates. This work has also been published as Circular contextual insertion/deletion with applications to biomolecular computation [17]. We next proceed to analyze the bio-operations proposed by two models of this computational biological process from the point of view of formal language theory. We consider the closure properties of various families of languages under these operations, the solvability of language equations involving these operations and some additional abstract properties of the operations. The results given here have also appeared as Some properties of ciliate bio-operations [16], Closure and decidability properties of some language classes with respect to ciliate bio-operations [14] and The ld and dlad bio-operations on formal languages [15]. We then present an algorithm to determine the relative complexity of scrambled genes by finding minimal descrambling paths. We include not only a theoretical description of this technique, but also the results of applying it to real ciliate genes. Finally, we consider the relative time-complexities of the proposed models of gene descrambling.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".