mRNA Biomarkers for Invasive Breast Cancer based on a Deep Feature Selection Approach
Bibliographic record
Abstract
Early detection of breast cancer and effective identification of its correct stage remain major challenges for healthcare professionals. Testing the tumour for Oestrogen Receptor and Progesterone Receptor is a standard part of the initial evaluation of breast cancer diagnosis and treatment planning. Several expression profiling studies have illustrated that the expression of these hormone receptors is linked with diverse genetic variations, which means that several mutated genes can a affect the development and progression of breast cancer and contribute to its heterogeneity. Unfortunately, due to the high dimensionality and low sample size nature of microarray data, traditional statistical feature selection techniques fail to identify genes that could act as risk factors for breast cancer. Inspired by this, we developed a deep learning-based feature extraction module with a weight interpretation method to select a subset of robust biomarkers across three different mRNA expression data sets from The Cancer Genome Atlas program (TCGA). For a discovered feature (a gene) to be accepted for further investigation, it must have been independently selected by the weight interpretation method from each of the deep feature extraction modules (each having been trained on a different data set). The small panel of discovered biomarkers was then subsequently evaluated using a range of classifiers to ascertain their predictive ability with respect to the above hormone receptor status. We observed strong evidence that the upregulation in the expression levels of highly positively weighted genes within the deep feature selection modules and the down regulation in the expression levels of the highly negatively weighted genes both indicated the strong likelihood of a patient experiencing ER+/PR+ invasive breast cancer. In addition, we discovered a number of potentially novel biomarkers worthy of further consideration.
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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 | 0.001 |
| 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 | 0.000 |
| 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".