Q3 Accuracy and SOV Measure Analysis of Application of GA in Protein Secondary Structure Prediction
Bibliographic record
Abstract
The protein secondary structure prediction (PSP) of the large biological molecule protein is an important task of bioinformatics and in the last decades many machines learning and soft computing methodologies play vital roles in achieving satisfactory results. The protein structural class determination is an important topic in protein science because an idea about protein structural class is quite useful to know about the changes and reaction of a living body in order to design new drugs and medicines. Though several hard computing techniques may be helpful in these areas but focusing upon the steady development and big data size in protein sequences that are entering into databanks, it is a challenge to do experiments with the hard computing techniques. Soft computing techniques like Artificial Neural Network, Fuzzy logic, Genetic Algorithm play a vital role for these types of genomic researches. To face these complex challenges, this article presents a novel method to predict the protein structure by using Genetic Algorithm. The Q3 accuracy and SOV measure analysis with SOVH, SOVE, SOVC value of respective α-helix (H), β-sheet (E) and coil/loop(C) structures are also discussed. The application of Genetic algorithm i.e. the proposed technique GApred provides better result than that of SPIDER2, JPred4, FSVM and SSpro5 for all the three datasets in the experiment. This method is helpful for distinct protein secondary structure prediction and a significant success rate was observed, which indicates that it can be used as a powerful tool in drug design and medicine research.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".