Enhancements to hidden markov models for gene finding and other biological applications
Bibliographic record
Abstract
In this thesis, we present enhancements of hidden Markov models for the problem of finding genes in DNA sequences. Genes are the parts of DNA that serve as a template for synthesis of proteins. Thus, gene finding is a crucial step in the analysis of DNA sequencing data. Hidden Markov models are a key tool used in gene finding. Yhis thesis presents three methods for extending the capabilities of hidden Markov models to better capture the statistical properties of DNA sequences. In all three, we encounter limiting factors that lead to trade-offs between the model accuracy and those limiting factors. First, we build better models for recognizing biological signals in DNA sequences. Our new models capture non-adjacent dependencies within these signals. In this case, the main limiting factor is the amount of training data: more training data allows more complex models. Second, we design methods for better representation of length distributions in hidden Markov models, where we balance the accuracy of the representation against the running time needed to find genes in novel sequences. Finally, we show that creating hidden Markov models with complex topologies may be detrimental to the prediction accuracy, unless we use more complex prediction algorithms. However, such algorithms require longer running time, and in many cases the prediction problem is NP-hard. For gene finding this means that incorporating some of the prior biological knowledge into the model would require impractical running times. However, we also demonstrate that our methods can be used for solving other biological problems, where input sequences are short. As a model example to evaluate our methods, we built a gene finder ExonHunter that outperforms programs commonly used in genome projects.
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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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".