Improving Prediction Accuracy of Microarray Cancer Data with Non-negative Matrix Factorization and Its Variant
Bibliographic record
Abstract
Abnormal growth in cells with the potential to diffuse to other parts of the human body could occur due to multiple reasons such as changes in DNA segments activity. Altering DNA methylation is known as an important factor in cancer development and altering DNA activity by avoiding some of the normal activities of DNA. Feature extraction is used to reduce the dimensionality in high dimensional datasets as well as to filter the most useful features in predicting gene expression for a cancer. A number of feature extraction methods have been used in literature for selecting the most useful features. In this study Semi-orthogonal Non-Negative Matrix Factorization (SONMF) and Non-negative Matrix Factorization (NMF) were studied and tested on four microarray cancer datasets for feature extraction and compared with FFT features, Symmetry of Methylation Density Features, and Principal Component Analysis (PCA). Five different classifiers, namely Naive Bayes, Support Vector Machine (SVM), K-nearest Neighbor (KNN), Random Forest and Neural Network were used to predict the gene expression of the four cancer microarray datasets. The experiments show that for colon cancer dataset, Semi-orthogonal NMF (SONMF) and Non-negative Matrix Factorization (NMF) performed the best compared with other feature extraction methods with Naive Bayes classifier. For Oral cancer dataset, the highest accuracy was observed with SONMF and Neural Network classifier. In Leukemia cancer, the highest accuracy of 100% was observed with NMF, SONMF and PCA with Neural Network and SVM classifiers. For prostate cancer dataset, SONMF with Naive Bayes classifier gave the highest accuracy. Overall, the results show that SONMF and NMF were more consistent compared with other features extraction methods and gave the best features for prediction accuracy of microarray cancer datasets.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".