Probabilistic Graphical Models and Deep Belief Networks for Prognosis of Breast Cancer
Bibliographic record
Abstract
We propose a probabilistic graphical model (PGM) for prognosis and diagnosis of breast cancer. PGMs are suitable for building predictive models in medical applications, as they are powerful tools for making decisions under uncertainty from big data with missing attributes and noisy evidence. Previous work relied mostly on clinical data to create a predictive model. Moreover, practical knowledge of an expert was needed to build the structure of a model, which may not be accurate. In our opinion, since cancer is basically a genetic disease, the integration of microarray and clinical data can improve the accuracy of a predictive model. However, since microarray data is high-dimensional, including genomic variables may lead to poor results for structure and parameter learning due to the curse of dimensionality and small sample size problems. We address these problems by applying manifold learning and a deep belief network (DBN) to microarray data. First, we construct a PGM and a DBN using clinical and microarray data, and extract the structure of the clinical model automatically by applying a structure learning algorithm to the clinical data. Then, we integrate these two models using softmax nodes. Extensive experiments using real-world databases, such as METABRIC and NKI, show promising results in comparison to Support Vector Machines (SVMs) and k-Nearest Neighbors (k-NN) classifiers, for classifying tumors and predicting events like recurrence and metastasis.
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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".