PS8-Net: A Deep Convolutional Neural Network to Predict the Eight-State\n Protein Secondary Structure
Bibliographic record
Abstract
Protein secondary structure is crucial to creating an information bridge\nbetween the primary and tertiary (3D) structures. Precise prediction of\neight-state protein secondary structure (PSS) has significantly utilized in the\nstructural and functional analysis of proteins in bioinformatics. Deep learning\ntechniques have been recently applied in this research area and raised the\neight-state (Q8) protein secondary structure prediction accuracy remarkably.\nNevertheless, from a theoretical standpoint, there are still lots of rooms for\nimprovement, specifically in the eight-state PSS prediction. In this study, we\nhave presented a new deep convolutional neural network (DCNN), namely PS8-Net,\nto enhance the accuracy of eight-class PSS prediction. The input of this\narchitecture is a carefully constructed feature matrix from the proteins\nsequence features and profile features. We introduce a new PS8 module in the\nnetwork, which is applied with skip connection to extracting the long-term\ninter-dependencies from higher layers, obtaining local contexts in earlier\nlayers, and achieving global information during secondary structure prediction.\nOur proposed PS8-Net achieves 76.89%, 71.94%, 76.86%, and 75.26% Q8 accuracy\nrespectively on benchmark CullPdb6133, CB513, CASP10, and CASP11 datasets. This\narchitecture enables the efficient processing of local and global\ninterdependencies between amino acids to make an accurate prediction of each\nclass. To the best of our knowledge, PS8-Net experiment results demonstrate\nthat it outperforms all the state-of-the-art methods on the aforementioned\nbenchmark datasets.\n
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".