Predicting liver cancer on epigenomics data using machine learning
Bibliographic record
Abstract
Epigenomics is the branch of biology concerned with the phenotype modifications that do not induce any change in the cell DNA sequence. Epigenetic modifications apply changes to the properties of DNA, which ultimately prevents such DNA actions from being executed. These alterations arise in the cancer cells, which is the only cause of cancer. The liver is the metabolic cleansing center of the human body and the only organ, which can regenerate itself, but liver cancer can stop the cleansing of the body. Machine learning techniques are used in this research to predict the gene expression of the liver cells for the liver hepatocellular carcinoma (LIHC), which is the third biggest reason of death by cancer and affects five hundred thousand people per year. The data for LIHC include four different types, namely, methylation, histone, the human genome, and RNA sequences. The data were accessed through open-source technologies in R programming languages for The Cancer Genome Atlas (TCGA). The proposed method considers 1,000 features across the four types of data. Nine different feature selection methods were used and eight different classification methods were compared to select the best model over 5-fold cross-validation and different training-to-test ratios. The best model was obtained for 140 features for ReliefF feature selection and XGBoost classification method with an AUC of 1.0 and an accuracy of 99.67% to predict the liver cancer.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".