AlzGenPred - CatBoost-based gene classifier for predicting Alzheimer’s disease using high-throughput sequencing data
Rohit Shukla, Tiratha Raj Singh · 2024
Read AlzGenPred - CatBoost-based gene classifier for predicting Alzheimer’s disease using high-throughput sequencing data by Rohit Shukla, Tiratha Raj Singh (2024) in the Tower of Babel, a free online library of public-domain works preserved in clean, searchable text. No sign-up. The full text is served as a plain-text file at a predictable URL, where text is present: https://antarctic-labs.com/tower-of-babel/pmc-11621786.txt.
Description
Thousands of genes have been tied to Alzheimer's disease, but sorting the truly involved from the merely nearby takes a sieve. Rohit Shukla and Tiratha Raj Singh build one in 2024 from gene network data, after finding that raw sequence features alone classify poorly. Their classifier, AlzGenPred, reads how genes connect rather than how they spell.
The tool sorted known disease genes with about ninety-seven percent accuracy and held up on a separate gene set and on expression data. The authors have put it on the web for any researcher to use. They present it as a way to shortlist biomarker candidates fast. Better shortlists, they argue, speed the search for the disease's drivers.
Catalog record
| CREATOR | Rohit Shukla, Tiratha Raj Singh |
|---|---|
| YEAR | 2024 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 60 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |