Consensus Prediction of Protein Secondary Structures
Bibliographic record
Abstract
I dedicate this thesis to my parents. They raised me up, provide me happiness, cultivate my personality, and provide me the best education. I dedicate this thesis to my grandmother, a diligent and kind Chinese woman, who made great contributions to the whole family, and left us in 2004 when I was pursuing this degree in Canada. I wish her peace forever. ii Protein structure prediction is one of the most significant problems in bioinformat-ics. Currently, there are some tools which can predict protein secondary structure, or find protein structural motifs and some specific structure segments. However, their results are sometimes different or contradictory. CISPred is a consensus protein structure prediction system which integrates results in order to provide overall consensus predictions of protein secondary struc-tures. The average accuracy of CISPred predictions is 82.6 % on a dataset con-taining 109 CASP sequences, and 89.3 % on a dataset containing 1758 sequences. iii Acknowledgements I sincerely appreciate my supervisors, Dr. Patricia Evans and Dr. Virendra Bhavsar. They impart their knowledge, direct my research, and provide finan-cial support. Dr. Virendra Bhavsar and Dr. Patricia Evans are professors with profound knowledge and experience, and have been respectful mentors. The two years of study and research with them have been one of the best periods in my life.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.005 |
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".