Similar Datasets
Project description:Please provide a short descriptive title for the study
| PRJEB19333 | ENA
Project description:In several author name disambiguation studies, some ethnic name groups such as East Asian names are reported to be more difficult to disambiguate than others. This implies that disambiguation approaches might be improved if ethnic name groups are distinguished before disambiguation. We explore the potential of ethnic name partitioning by comparing performance of four machine learning algorithms trained and tested on the entire data or specifically on individual name groups. Results show that ethnicity-based name partitioning can substantially improve disambiguation performance because the individual models are better suited for their respective name group. The improvements occur across all ethnic name groups with different magnitudes. Performance gains in predicting matched name pairs outweigh losses in predicting nonmatched pairs. Feature (e.g., coauthor name) similarities of name pairs vary across ethnic name groups. Such differences may enable the development of ethnicity-specific feature weights to improve prediction for specific ethic name categories. These findings are observed for three labeled data with a natural distribution of problem sizes as well as one in which all ethnic name groups are controlled for the same sizes of ambiguous names. This study is expected to motive scholars to group author names based on ethnicity prior to disambiguation.
| S-EPMC8359369 | biostudies-literature
Project description:The pfam04002 annotation describes RadC as a bacterial DNA repair protein. Although the radC gene is expressed specifically during competence for genetic transformation in Streptococcus pneumoniae, we report that radC mutants exhibit normal uptake and processing of transforming DNA. They also display normal sensitivity to DNA-damaging agents, providing no support for the rad epithet.
| S-EPMC2519389 | biostudies-literature