[Evaluation of computational methods for HLA three loci haplotype by compare with family-based data].
Bibliographic record
Abstract
OBJECTIVE: We evaluated the accuracy and efficiency of computational inference methods for haplotype on estimate HLA-A-B-C haplotype frequencies by compared with the haplotypes manually defined in a family-base dataset. METHODS: 558 individuals with pedigree information were selected, and their haplotyps were compared with the data obtained by the following three method: the Expectation-Maximization (EM) and Excoffier-Laval-Balding (ELB)algorithms using the AELEQUIN software, and the SAS/Genetics PROC HAPLOTYPE method. RESULTS: After performing the SAS/Genetics method, and the Expectation-Maximization (EM) and Excoffier-Laval-Balding (ELB) algorithms using the AELEQUIN software, 248, 247, and 238 different haplotypes were obtained respectively. The accuracy rates of these three methods were 88.5%, 89.1%, and 90.3% respectively. There are no significant different in the accuracy and estimated haplotype frequency comparisons among any two of these computational inference methods. CONCLUSION: High accuracy haplotype frequency estimate rates could be obtained by these three computational inference methods, and there are no significant difference in the comparison of haplotypes estimated by SAS/Genetics, the EM and ELB algorithms using the AELEQUIN software. However, ELB algorithm shows better performance than EM algorithm and SAS/Genetics PROC HAPLOTYPE method for haplotype frequencies estimation in general.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 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".