Discovering cancer biomarkers: from DNA to communities of genes
Bibliographic record
Abstract
In this paper, we consider genes as actors of a social network, a research area that has not yet received attention in the literature of social network mining and analysis. Even though our research project covers both genes and proteins, we concentrate in this paper on gene; we first try to describe the gene expression data and how gene interactions can be realised as a social network. Then we describe how data mining techniques could reveal important information by identifying disease biomarkers from the social communities of genes. This is possible because of the way genes interact and form communities that are anticipated to have certain effects on the different processes that take place within an organism. Gene communities both contribute to the development of an organism by coding proteins and cause serious diseases. In this paper, we concentrate on genes that act as cancer biomarkers. We apply a multiobjective clustering approach to produce alternative clustering solutions and then derive a matrix that reflects the link between genes based on their common occurrence on the same cluster within different alternative solutions. The latter matrix leads to the social network of genes, which is then analysed to discover the communities and the central genes within each community. The latter genes are studied further as cancer biomarkers. The test results are promising in demonstrating the applicability and effectiveness of the developed mining-based methodology.
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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".