cAPTured: Neural Reflex Arc-Inspired Fuzzy Continual Learning for Capturing in Silico Aptamer-Target Protein Interactions
Bibliographic record
Abstract
Aptamers are oligonucleotides or peptides with unique binding properties for specific target molecules, and they have shown great potential in diagnostics, therapeutics, and bio-sensing. However, the current in vitro SELEX-based method for discovering new target-selective aptamers is challenging, time-consuming, and often unsuccessful in finding high-affinity aptamers. Recently, in silico methods have gained immense attention. However, since labeled interaction-pair data collection is expensive and needs highly trained specialists, available data is sparse. Further, since acquiring positive-class samples is even more challenging, available datasets showcase high-class imbalance. This makes designing deep learning models incredibly challenging, as they require a sufficiently large training set and are biased towards the dominant class. Additionally, current models cannot be updated in real-time, and end-to-end re-training is necessary for each new aptamer-target interaction pair discovery. The present work is the first to address both these challenges. We present cAPTured, a novel fuzzy continual learning method for predicting aptamer-target protein interaction pairs in a continual learning environment. cAPTured continually updates its learned feature space on a non-stationary interaction-pair data stream. We performed extensive evaluation studies and experiments to establish the effectiveness of the proposed approach. cAPTured outperforms existing methods on the benchmark dataset by a significant margin.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".