A multi-stage protein secondary structure prediction system using machine learning and information theory
Bibliographic record
Abstract
In this paper, we evaluated the performance of a multi-stage protein secondary structure (PSS) prediction model. The proposed classifier uses statistical information and protein profiles. The statistical information is derived from protein sequences and structures by using a k-means clustering technique and Information theory. In the first stage, a feed-forward artificial neural network maps a sequence fragment to a region in the Ramachandran plot (2D-plot). A score vector is constructed with the mapped region using clustering and statistical information. The score vector represents the tendency of pairing an identified region in the 2D-plot and secondary structures for a residue. The score vectors which are used in the second stage have fewer dimensions compared to input vectors that are commonly derived from protein sequences or profile information. In the second stage, a two-tier classifier is employed based on an artificial neural network and a genetic programming (GP) method. The GP method uses IF rules for a three-state classification. The two-tier classifier's performance is compared to those of two-tier artificial neural networks (ANNs) and support vector machines (SVMs). The prediction method is examined with a common protein dataset, RS126. The performance of the proposed classification model is measured based on Q3and segment overlap (SOV) scores. The proposed PSS prediction model improves over 3% the Q3score and 2% the SOV score in comparison to those of two-tier ANN and SVMs architectures.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".