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How to stop worrying and love multiple citation experimental data Научная публикация

Журнал Mendeleev Communications
ISSN: 1364-551X , E-ISSN: 0959-9436
Вых. Данные Год: 2025, Том: 35, Номер: 2, Страницы: 224-227 Страниц : 4 DOI: 10.71267/mencom.7710
Авторы Timofeev Yaroslav Vladislavovich 1,2 , Mrasov Amir M. 1,2 , Panova Maria V 2 , Novikov Fedor Nikolaevich 2 , Svitanko Igor 2
Организации
1 Department of Chemistry, M. V. Lomonosov Moscow State University, 119991 Moscow, Russian Federation
2 N. D. Zelinsky Institute of Organic Chemistry, Russian Academy of Sciences, 119991 Moscow, Russian Federation

Реферат: Numerous public databases now collect and disseminate biological activity data from literature and patents, forming the basis for chemogenomics and novel scoring functions. However, data quality is often compromised due to multiple citations of values across different studies with varying protocols. To address this issue, we used the XGBoost model in combination with a BERT-based NLP approach and a distance-based out-of-distribution (OOD) data detection method to enhance classification accuracy and exclude review articles.
Библиографическая ссылка: Timofeev Y.V. , Mrasov A.M. , Panova M.V. , Novikov F.N. , Svitanko I.
How to stop worrying and love multiple citation experimental data
Mendeleev Communications. 2025. V.35. N2. P.224-227. DOI: 10.71267/mencom.7710 WOS Scopus OpenAlex
Идентификаторы БД:
Web of science: WOS:001506165900015
Scopus: 2-s2.0-105007460397
OpenAlex: W4407709724
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БД Цитирований
OpenAlex Нет цитирований
Scopus Нет цитирований
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