Balanced Functional Module Detection in Genomic Data
Bibliographic record
Abstract
Abstract High dimensional genomic data can be analyzed to understand the effects of multiple variables on a target variable such as a clinical outcome, risk factor or diagnosis. Of special interest are functional modules, cooperating sets of variables affecting the target. Graphical models of various types are often useful for characterizing such networks of variables. In other applications such as social networks, the concept of balance in undirected signed graphs characterizes the consistency of associations within the network. To extend this concept to applications where a set of predictor variables influences an outcome variable, we define balance for functional modules. This property specifies that the module variables have a joint effect on the target outcome with no internal conflict, an efficiency that evolution may use for selection in biological networks. We show that for this class of graphs, observed correlations directly reflect paths in the underlying graph. Consequences of the balance property are exploited to implement a new module discovery algorithm, bFMD, which selects a subset of variables from highdimensional data that compose a balanced functional module. Our bFMD algorithm performed favorably in simulations as compared to other module detection methods that do not consider balance properties. Additionally, bFMD detected interpretable results in a real application for RNA-seq data obtained from The Cancer Genome Atlas (TCGA) for Uterine Corpus Endometrial Carcinoma using the percentage of tumor invasion as the target outcome of interest. bFMD detects sparse sets of variables within highdimensional datasets such that interpretability may be favorable as compared to other similar methods by leveraging balance properties used in other graphical applications.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".