Automated Linking PUBMED Documents with GO Terms Using SVM

Autor:	Su-Shing Chen, Hyun-Ki Kim
Rok vydání:	2021
Předmět:	Support vector machine ComputingMethodologies_PATTERNRECOGNITION Information retrieval Computer science Gene ontology Molecular function Genomic data Graph (abstract data type) Training phase
Zdroj:	Journal of Data Science. 5:259-267
ISSN:	1683-8602 1680-743X
DOI:	10.6339/jds.2007.05(2).331
Popis:	We have developed an automated linking scheme for PUBMED citations with GO terms using SVM (Support Vector Machine), a classification algorithm. The PUBMED database has been essential to life science researchers with over 12 million citations. More recently GO (Gene Ontology) has provided a graph structure for biological process, cellular component, and molecular function of genomic data. By text mining the textual content of PUBMED and associating them with GO terms, we have built up an ontological map for these databases so that users can search PUBMED via GO terms and conversely GO entries via PUBMED classification. Consequently, some interesting and unexpected knowledge may be captured from them for further data analysis and biological experimentation. This paper reports our results on SVM implementation and the need to parallelize for the training phase.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::0be836c6b248bec33d872d0bfe2c05ca https://doi.org/10.6339/jds.2007.05(2).331 Zobrazit plný text záznamu