Browsing by Author "Ding, Ruoyao"
Now showing 1 - 3 of 3
Results Per Page
Sort Options
Item pGenN, a Gene Normalization Tool for Plant Genes and Proteins in Scientific Literature(PLOS (Public Library of Science), 2015-08-10) Ding, Ruoyao; Arighi, Cecilia N.; Lee, Jung-Youn; Wu, Cathy H.; Vijay-Shanker, K.; Ruoyao Ding, Cecilia N. Arighi, Jung-Youn Lee, Cathy H. Wu, K. Vijay-Shanker; Ding, Ruoyao; Arighi, Cecilia N.; Lee, Jung-Youn; Wu, Cathy H.; Vijay-Shanker, K.BACKGROUND Automatically detecting gene/protein names in the literature and connecting them to databases records, also known as gene normalization, provides a means to structure the information buried in free-text literature. Gene normalization is critical for improving the coverage of annotation in the databases, and is an essential component of many text mining systems and database curation pipelines. METHODS In this manuscript, we describe a gene normalization system specifically tailored for plant species, called pGenN (pivot-based Gene Normalization). The system consists of three steps: dictionary-based gene mention detection, species assignment, and intra species normalization. We have developed new heuristics to improve each of these phases. RESULTS We evaluated the performance of pGenN on an in-house expertly annotated corpus consisting of 104 plant relevant abstracts. Our system achieved an F-value of 88.9%(Precision 90.9% and Recall 87.2%) on this corpus, outperforming state-of-art systems presented in BioCreative III. We have processed over 440,000 plant-related Medline abstracts using pGenN. The gene normalization results are stored in a local database for direct query from the pGenN web interface (proteininformationresource.org/pgenn/). The annotated literature corpus is also publiclyItem pGenN, a Gene Normalization Tool for Plant Genes and Proteins in Scientific Literature(Public Library of Science, 2015-08-10) Ding, Ruoyao; Arighi, Cecilia N.; Lee, Jung-Youn; Wu, Cathy H.; Vijay-Shanker, K.; Ruoyao Ding, Cecilia N. Arighi, Jung-Youn Lee, Cathy H. Wu, K. Vijay-Shanker; Dina, Ruoyao; Arighi, Cecilia N.; Lee, Jung-Youn; Wu, Cathy H.; Vijay-Shanker, K.BACKGROUND Automatically detecting gene/protein names in the literature and connecting them to databases records, also known as gene normalization, provides a means to structure the information buried in free-text literature. Gene normalization is critical for improving the coverage of annotation in the databases, and is an essential component of many text mining systems and database curation pipelines. METHODS In this manuscript, we describe a gene normalization system specifically tailored for plant species, called pGenN (pivot-based Gene Normalization). The system consists of three steps: dictionary-based gene mention detection, species assignment, and intra species normalization. We have developed new heuristics to improve each of these phases. RESULTS We evaluated the performance of pGenN on an in-house expertly annotated corpus consisting of 104 plant relevant abstracts. Our system achieved an F-value of 88.9%(Precision 90.9% and Recall 87.2%) on this corpus, outperforming state-of-art systems presented in BioCreative III. We have processed over 440,000 plant-related Medline abstracts using pGenN. The gene normalization results are stored in a local database for direct query from the pGenN web interface (proteininformationresource.org/pgenn/). The annotated literature corpus is also publicly available through the PIR text mining portal (proteininformationresource. org/iprolink/).Item Using text mining techniques to assist gene related annotation(University of Delaware, 2017) Ding, RuoyaoBiomedical researchers usually describe their experimental results in research publications. With the rapid growth of biomedical publications, the information of interest needs to be extracted automatically to avoid the time consuming and labor intensive process. ☐ In this dissertation, we seek to help in the process of gene related annotations, by extracting the unstructured information buried in the literature and providing means to structure the extracted information. We start by recognizing gene names in the literature and linking them to database records. Based on this work, we develop a system which automatically selects articles that are about a specific UniProt protein entry. Next, we describe our approach for mining information related to another important bio-named entity, protein complex. Finally, we present our work on assisting the curation of gene annotation. ☐ In each of these tasks, we conduct experiments to evaluate the efficacy of our approaches. The results show that our systems achieve good performances, and can be used to assist the annotation process related to genes.