INSONLARDA GENETIK KASALLIKLARNING MOLEKULYAR ASOSLARI
Bibliographic record
Abstract
UO‘K: 616.98:577 INSONLARDA GENETIK KASALLIKLARNING MOLEKULYAR ASOSLARI 1Daminov Muslimbek Asadullayevich p.f.f.d. (PhD), dotsent v.b. 2Jumanov Muratbay Arepbayevich b.f.d., professor 1ZARMED universiteti 2Qoraqalpoq davlat universiteti https://doi.org/10.5281/zenodo.17324173 Annotatsiya: Ushbu maqolada so‘nggi o‘n yilliklarda molekulyar biologiya va genetik tadqiqotlar inson organizmidagi ko‘plab irsiy kasalliklarning kelib chiqish mexanizmlarini ochib berganligi haqida adabiyotlar ma’lumotlari keltirilgan. Shuningdek inson genomining to‘liq o‘rganilishi va yangi avlod sekvenirlash texnologiyalarining joriy etilishi tufayli kasalliklarning genetik determinatsiyasi, mutatsiyalar spektri va molekulyar patogenez mexanizmlari chuqur tahlil qilinmoqda. Genetik kasalliklar orasida qon kasalliklari (talassemiya, gemofiliya), metabolik buzilishlar (fenilketonuriya, galaktozemiya), onkologik sindromlar, shuningdek nevrologik kasalliklar eng ko‘p uchraydiganlari hisoblanadi. Ushbu maqolada insonlarda genetik kasalliklarning molekulyar asoslari bo‘yicha ilmiy adabiyotlar tahlil qilinadi. Kalit so‘zlar: genetik yuk, mutatsiyalar spektri, mahalliy populyatsiya, irsiy kasalliklar, konsanguinitet, neonatal skrining, genetik epidemiologiya, autosomal-retsessiv kasalliklar, molekulyar genetika.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.034 | 0.023 |
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".