HyMM: Hybrid method for disease-gene prediction by integrating multiscale module structures
Bibliographic record
Abstract
Abstract Motivation Identifying disease-related genes is important for the study of human complex diseases. Module structures or community structures are ubiquitous in biological networks. Although the modular nature of human diseases can provide useful insights, the mining of information hidden in multiscale module structures has received less attention in disease-gene prediction. Results We propose a hybrid method, HyMM, to predict disease-related genes more effectively by integrating the information from multiscale module structures. HyMM consists of three key steps: extraction of multiscale modules, gene rankings based on multiscale modules and integration of multiple gene rankings. The statistical analysis of multiscale modules extracted by three multiscale-module-decomposition algorithms (MO, AS and HC) shows that the functional consistency of the modules gradually improves as the resolution increases. This suggests the existence of different levels of functional relationships in the multiscale modules, which may help reveal disease-gene associations. We display the effectiveness of multiscale module information in the disease-gene prediction and confirm the excellent performance of HyMM by 5-fold cross-validation and independent test. Specifically, HyMM with MO can more effectively enhance the ability of disease-gene prediction; HyMM (MO, RWR) and HyMM (MO, RWRH) are especially preferred due to their excellent comprehensive performance, and HyMM (AS, RWRH) is also good choice due to its local performance. We anticipate that this work could provide useful insights for disease-module analysis and disease-gene prediction based on multi-scale module structures. Availability https://github.com/xiangiu0208/HvMM Contact limin@mail.csu.edu.cn Supplementary information Supplementary data are available at Bioinformatics online.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".