Biomedical subjects

K Vijay-Shanker

Publications and source records attributed to K Vijay-Shanker.

5 recordsLinked to original sources

PubMed2006

An online literature mining tool for protein phosphorylation.

A web-based version of the RLIMS-P literature mining system was developed for online mining of protein phosphorylation information from MEDLINE abstracts. The online tool presents extracted phosphorylation objects (phosphorylated proteins, phosphorylation sites and protein kinases) in summary tables and full reports with evidence-tagged abstracts. The tool further allows mapping of phosphorylated proteins to protein entries in the UniProt Knowledgebase based on PubMed ID and/or protein name. The literature mining, coupled with database association, allows retrieval of rich biological information for the phosphorylated proteins and facilitates database annotation of phosphorylation features.

Animals↗

PubMed2005

Literature mining and database annotation of protein phosphorylation using a rule-based system.

MOTIVATION: A large volume of experimental data on protein phosphorylation is buried in the fast-growing PubMed literature. While of great value, such information is limited in databases owing to the laborious process of literature-based curation. Computational literature mining holds promise to facilitate database curation. RESULTS: A rule-based system, RLIMS-P (Rule-based LIterature Mining System for Protein Phosphorylation), was used to extract protein phosphorylation information from MEDLINE abstracts. An annotation-tagged literature corpus developed at PIR was used to evaluate the system for finding phosphorylation papers and extracting phosphorylation objects (kinases, substrates and sites) from abstracts. RLIMS-P achieved a precision and recall of 91.4 and 96.4% for paper retrieval, and of 97.9 and 88.0% for extraction of substrates and sites. Coupling the high recall for paper retrieval and high precision for information extraction, RLIMS-P facilitates literature mining and database annotation of protein phosphorylation.

Abstracting and Indexing↗

PubMed2005

Beyond the clause: extraction of phosphorylation information from medline abstracts.

MOTIVATION: Phosphorylation is an important biochemical reaction that plays a critical role in signal transduction pathways and cell-cycle processes. A text mining system to extract the phosphorylation relation from the literature is reported. The focus of this paper is on the new methods developed and implemented to connect and merge pieces of information about phosphorylation mentioned in different sentences in the text. The effectiveness and accuracy of the system as a whole as well as that of the methods for extraction beyond a clause/sentence is evaluated using an independently annotated dataset, the Phospho.ELM database. The new methods developed to merge pieces of information from different sentences are shown to be effective in significantly raising the recall without much difference in precision.

Artificial Intelligence↗

PubMed2004

Using name-internal and contextual features to classify biological terms.

There has been considerable work done recently in recognizing named entities in biomedical text. In this paper, we investigate the named entity classification task, an integral part of the named entity extraction task. We focus on the different sources of information that can be utilized for classification, and note the extent to which they are effective in classification. To classify a name, we consider features that appear within the name as well as nearby phrases. We also develop a new strategy based on the context of occurrence and show that they improve the performance of the classification system. We show how our work relates to previous works on named entity classification in the biological domain as well as to those in generic domains. The experiments were conducted on the GENIA corpus Ver. 3.0 developed at University of Tokyo. We achieve f value of 86 in 10-fold cross validation evaluation on this corpus.

Abstracting and Indexing↗

PubMed2003

A biological named entity recognizer.

In this paper we describe a new named entity extraction system. Our system is based on a manually developed set of rules that rely heavily upon some crucial lexical information, linguistic constraints of English, and contextual information. This system achieves state of art results in the protein name detection task, which is what many of the current name extraction systems do. We discuss the need for detection of chemical names and show that we not only obtain a high degree of success in recognizing chemicals but that this task can help improve the precision of protein name detection as well. We use context and surrounding words for categorization of named entities and find the results obtained are encouraging.

Abstracting and Indexing↗