Networks - learning salient gene and protein features from network topologies.
Bibliographic record
Abstract
Duncan Forster is PhD student in Molecular Genetics co-supervised by Prof Gary Bader and Charlie Boone at the University of Toronto. https://baderlab.org/Members His work has addressed the following questions. Firstly, we wanted to determine whether recent deep learning architectures (namely graph neural networks/graph convolutional networks) could be used to learn salient gene and protein features from network topologies. If so, these features could be integrated in a trainable, end-to-end fashion allowing for effective integration of biological networks. These recent deep learning architectures have shown substantial improvements over previous network feature learning approaches on a range of tasks, which motivates their use in biological domains. Secondly, we wanted to determine more effective evaluation strategies in order to compare integration approaches. This is a challenging task due to differences in input network sizes and standard coverage, biases and quality of the standards, differences in method outputs (networks vs. features), and biases in the current evaluation strategies themselves. Code is available at https://github.com/bowang-lab/BIONIC
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".