Bio-inspired Approaches for G-protein coupled receptors identification using Chou’s PseAAC
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Background: G–protein coupled receptors (GPCRs) are key factors in cell-to-cell communication. GPCR activation is necessary for normal physiology of all organisms while dysfunction of GPCR signalling is responsible for many of the diseases. Consequently, GPCRs have a fundamental role in pharmacological research and are targets for many drugs. Objective: The problem is that many GPCRs remain orphans (have unknown function), they are not classified correctly, and new bioinformatics approaches are needed to address this issue. In our work, we focus on bio-inspired approaches, which are increasingly used in recent years because of their interesting inspirations from biological systems mechanisms and their good performances in many research areas. Methods: In this article, we use categories of bio-inspired well-known methods to identify GPCR function, which are swarm-based approaches and immunological computing. The proposed classifiers based on three popular swarm intelligence approaches are Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO) and PSO/ACO hybridization. The classification results are compared with these of the proposed immunological classifier based on the Artificial Immune Recognition System (AIRS), in order to identify the best bio-inspired method for the given problem. Results: The immune classifier (AIRS2) provided better results than swarm-based classifiers, specifically at the first levels (superfamily and families) Conclusion: It is interesting to adapt the bio-inspired algorithms in order to increase predictive accuracy at all GPCR hierarchical levels
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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