A preliminary approach to creating an overview of lactoferrin multi-functionality utilizing a text mining method

Abstract

Lactoferrin is a multi-functional metal-binding glycoprotein that exhibits many biological functions of interest to many researchers from the fields of clinical medicine, dentistry, pharmacology, veterinary medicine, nutrition and milk science. To date, a number of academic reports concerning the biological activities of lactoferrin have been published and are easily accessible through public data repositories. However, as the literature is expanding daily, this presents challenges in understanding the larger picture of lactoferrin function and mechanisms. In order to overcome the "analysis paralysis" associated with lactoferrin information, we attempted to apply a text mining method to the accumulated lactoferrin literature. To this end, we used the information extraction system GENPAC (provided by Nalapro Technologies Inc., Tokyo). This information extraction system uses natural language processing and text mining technology. This system analyzes the sentences and titles from abstracts stored in the PubMed database, and can automatically extract binary relations that consist of interactions between genes/proteins, chemicals and diseases/functions. We expect that such information visualization analysis will be useful in determining novel relationships among a multitude of lactoferrin functions and mechanisms. We have demonstrated the utilization of this method to find pathways of lactoferrin participation in neovascularization, Helicobacter pylori attack on gastric mucosa, atopic dermatitis and lipid metabolism.

Optimization of the control of atopic dermatitis using a ratio of immunoregulatory proteins

“…Ранее нами было установлено [6], что при АтД происходит накопление данных трех белков, что не является благоприятным фактором: подавляющее большинство молекул лактоферрина в крови при АтД не является продуктом эпителиальных клеток, а высвобождается из гранул активированных нейтрофилов и способствует поддержанию воспалительных процессов, значительная часть α2-макроглобулина окислена и неспособна адекватно взаимодействовать с цитокинами и рецепторами, α1-антитрипсин накапливается в циркуляции только в комплексе с протеиназами, активно высвобождающимися при воспалении. Другие авторы [13,14] подтверждают участие данных белков в патогенезе АтД. Для того, чтобы взаимно усилить прогностическую значимость содержания данных иммунорегуляторных белков в крови, они были объединены в коэффициент.…”

Aircraft interior failure pattern recognition utilizing text mining and neural networks

“…There are many interesting fields of research such as detection of similarities between patent documents and scientific publications (Magerman et al 2010); examining mobile learning trends (Hung and Zhang 2011); discovering a multi-functional metal-binding glycoprotein that exhibits many biological functions of interest to many researchers from the fields of clinical medicine, dentistry, pharmacology, veterinary medicine, nutrition and milk science (Shimazaki and Kushida 2010); identifying fall-related injuries in electronic medical record (Tremblay et al 2009); mining business policy texts for discovering process models (Li et al 2010); discovering knowledge by opinion mining from noisy text data (Dey and Haque 2009); tracking what people are saying, finding influencers, and using many social network analytic tools to analyze the underlying social networks embedded within the blogosphere (Macskassy 2011) and (Huang et al 2011) and with emails via clustering and pattern discovery (Manco et al 2008); identifying the anomaly cases for knowledge discovery from the warranty and service data in the automotive domain (Rajpathak et al 2011); discovering frequent musical patterns (motifs) that is a relevant problem in musicology (Jiménez et al 2011). In Biology, text mining has new challenges as can be seen in Dai et al (2010); a good example of text mining on language recognition can be seen in Al-Jumaily et al (2011), where Arabic, the most widely spoken language in the Arab World is identified on the web.…”