Credibility Analysis of Putative Disease-Causing Genes Using Bioinformatics
Bibliographic record
Abstract
BACKGROUND: Genetic studies are challenging in many complex diseases, particularly those with limited diagnostic certainty, low prevalence or of old age. The result is that genes may be reported as disease-causing with varying levels of evidence, and in some cases, the data may be so limited as to be indistinguishable from chance findings. When there are large numbers of such genes, an objective method for ranking the evidence is useful. Using the neurodegenerative and complex disease amyotrophic lateral sclerosis (ALS) as a model, and the disease-specific database ALSoD, the objective is to develop a method using publicly available data to generate a credibility score for putative disease-causing genes. METHODS: Genes with at least one publication suggesting involvement in adult onset familial ALS were collated following an exhaustive literature search. SQL was used to generate a score by extracting information from the publications and combined with a pathogenicity analysis using bioinformatics tools. The resulting score allowed us to rank genes in order of credibility. To validate the method, we compared the objective ranking with a rank generated by ALS genetics experts. Spearman's Rho was used to compare rankings generated by the different methods. RESULTS: The automated method ranked ALS genes in the following order: TARDBP, FUS, ANG, SPG11, NEFH, OPTN, ALS2, SETX, FIG4, VAPB, DCTN1, TAF15, VCP, DAO. This compared very well to the ranking of ALS genetics experts, with Spearman's Rho of 0.69 (P = 0.009). CONCLUSION: We have presented an automated method for scoring the level of evidence for a gene being disease-causing. In developing the method we have used the model disease ALS, but it could equally be applied to any disease in which there is genotypic uncertainty.
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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 | 0.011 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.022 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".