Partitioning and correlating subgroup characteristics from Aligned Pattern Clusters
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Bibliographic record
Abstract
MOTIVATION: Evolutionarily conserved amino acids within proteins characterize functional or structural regions. Conversely, less conserved amino acids within these regions are generally areas of evolutionary divergence. A priori knowledge of biological function and species can help interpret the amino acid differences between sequences. However, this information is often erroneous or unavailable, hampering discovery with supervised algorithms. Also, most of the current unsupervised methods depend on full sequence similarity, which become inaccurate when proteins diverge (e.g. inversions, deletions, insertions). Due to these and other shortcomings, we developed a novel unsupervised algorithm which discovers highly conserved regions and uses two types of information measures: (i) data measures computed from input sequences; and (ii) class measures computed using a priori class groupings in order to reveal subgroups (i.e. classes) or functional characteristics. RESULTS: Using known and putative sequences of two proteins belonging to a relatively uncharacterized protein family we were able to group evolutionarily related sequences and identify conserved regions, which are strong homologous association patterns called Aligned Pattern Clusters, within individual proteins and across the members of this family. An initial synthetic demonstration and in silico results reveal that (i) the data measures are unbiased and (ii) our class measures can accurately rank the quality of the evolutionarily relevant groupings. Furthermore, combining our data and class measures allowed us to interpret the results by inferring regions of biological importance within the binding domain of these proteins. Compared to popular supervised methods, our algorithm has a superior runtime and comparable accuracy. AVAILABILITY AND IMPLEMENTATION: The dataset and results are available at www.pami.uwaterloo.ca/∼ealee/files/classification2015 CONTACT: akcwong@uwaterloo.ca SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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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 | 0.000 | 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