Network Assessor: An automated method for quantitative assessment of a network’s potential for gene function prediction
Bibliographic record
Abstract
Networks of gene-gene interactions (or, “functional interactions” or more generally, “associations”) have proven very useful for predicting gene function. Association networks have proven useful in other biological domains to predict properties of nodes representing, e.g., patients, based on their connectivity with other nodes with pre-established properties. The quality of these predictions depends on the quality and relevance of the association data. For predicting gene function, there are hundreds of different networks that can be used and a plethora of different algorithms to use them—validating prediction performance can be time consuming and error prone. Here we describe methodology and software to automatically evaluate the contribution of an individual association network to predicting gene function (and more generally, predicting node function). This software is implemented in Network Assessor, which is part of the GeneMANIA command line tools. We also describe its use in the GeneMANIA quality control system.Availability: The software is available in Java JAR format at http://pages.genemania.org/tools/.
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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 | 0.001 | 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".