A two-step logistic regression algorithm for identifying individual-cancer-related genes
Bibliographic record
Abstract
The identification of cancer-related genes is important towards the understanding of complex genetic diseases. Although many machine learning algorithms are proposed to identify disease-related genes, they often either have poor performance to identify locus heterogeneity cancer-related genes or are not applicable to predict individual-disease-related genes due to the lack of positive instances (imbalanced classification). To overcome these two issues, a two-step logistic regression (LR) based algorithm is proposed in this study for identifying individual-cancer-related genes. A set of high potential cancer-class-related genes is first generated in step 1, followed by a second round of LR-based algorithm conducted on this smaller dataset for identifying individual-cancer-related genes. Numerical experiments show that the proposed two-step LR-based algorithm not only works well for locus heterogeneity data, but also has good performance to handle the imbalanced classification problem. The individual-cancer-related gene identification experiments achieve AUC values of around 0.85 when the threshold of posterior probability is chosen between 0.3 and 0.6. All evaluations are conducted by using the leave-one-out cross validation method.
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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".