Combining a genetic algorithm and ensemble method to improve the classification of viruses
Bibliographic record
Abstract
Genomic features identification is an important step toward machine learning (ML) derived classifiers. Designing robust feature identification methods can increase the performance of ML algorithms and reduce high dimensional data. The Classification of genomes from nucleotides sequences often requires an exponential feature space according to the size of the genomes. These feature spaces should also capture the abundant mutations affecting the genomic sequences over time of several complex viruses such as the human immunodeficiency virus (HIV). One way of overcoming such challenges could be to design an accurate and efficient algorithm to capture a bag of minimal subsets of genomic features based on k-mers that could represent the most likely alternative and evolutionary space. In this article, we introduce KEVOLVE, a new method based on a Genetic Algorithm (GA) including a ML kernel to extract a bag of minimal subsets of genomic features maximizing a given classification score threshold. K EVOLVE is coupled with an ensemble prediction model based on support vector machines (SVMs) and is applied on the classification of HIV genomic sequences. Subsets of genomic features identified reduce the size of initial feature matrices by 99% and models based on them outperform state-of-the-art HIV predictors. The results also show that KEVOLVE is less sensitive to high mutation rates of the virus. The source code is available at https://github.com/bioinfoUQAM/Kevolve
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".