Prediction of Indel flanking regions in protein sequences using a variable-order Markov model
Why this work is in the frame
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Bibliographic record
Abstract
MOTIVATION: Insertion/deletion (indel) and amino acid substitution are two common events that lead to the evolution of and variations in protein sequences. Further, many of the human diseases and functional divergence between homologous proteins are more related to indel mutations, even though they occur less often than the substitution mutations do. A reliable identification of indels and their flanking regions is a major challenge in research related to protein evolution, structures and functions. RESULTS: In this article, we propose a novel scheme to predict indel flanking regions in a protein sequence for a given protein fold, based on a variable-order Markov model. The proposed indel flanking region (IndelFR) predictors are designed based on prediction by partial match (PPM) and probabilistic suffix tree (PST), which are referred to as the PPM IndelFR and PST IndelFR predictors, respectively. The overall performance evaluation results show that the proposed predictors are able to predict IndelFRs in the protein sequences with a high accuracy and F1 measure. In addition, the results show that if one is interested only in predicting IndelFRs in protein sequences, it would be preferable to use the proposed predictors instead of HMMER 3.0 in view of the substantially superior performance of the former.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it