Sequential Pattern Mining for Discovering Gene Interactions and their Contextual Information from Biomedical Texts

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F15%3A00229981" target="_blank" >RIV/68407700:21230/15:00229981 - isvavai.cz</a>

Výsledek na webu

<a href="http://www.biomedcentral.com/content/pdf/s13326-015-0023-3.pdf" target="_blank" >http://www.biomedcentral.com/content/pdf/s13326-015-0023-3.pdf</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1186/s13326-015-0023-3" target="_blank" >10.1186/s13326-015-0023-3</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Sequential Pattern Mining for Discovering Gene Interactions and their Contextual Information from Biomedical Texts

Popis výsledku v původním jazyce

BACKGROUND: Discovering gene interactions and their characterizations from biological text collections is a crucial issue in bioinformatics. Indeed, text collections are large and it is very difficult for biologists to fully take benefit from this amountof knowledge. Natural Language Processing (NLP) methods have been applied to extract background knowledge from biomedical texts. Some of existing NLP approaches are based on handcrafted rules and thus are time consuming and often devoted to a specific corpus. Machine learning based NLP methods, give good results but generate outcomes that are not really understandable by a user. RESULTS: We take advantage of an hybridization of data mining and natural language processing to propose an original symbolicmethod to automatically produce patterns conveying gene interactions and their characterizations. Therefore, our method not only allows gene interactions but also semantics information on the extracted interactions (e.g., modalities, bio

Název v anglickém jazyce

Sequential Pattern Mining for Discovering Gene Interactions and their Contextual Information from Biomedical Texts

Popis výsledku anglicky

BACKGROUND: Discovering gene interactions and their characterizations from biological text collections is a crucial issue in bioinformatics. Indeed, text collections are large and it is very difficult for biologists to fully take benefit from this amountof knowledge. Natural Language Processing (NLP) methods have been applied to extract background knowledge from biomedical texts. Some of existing NLP approaches are based on handcrafted rules and thus are time consuming and often devoted to a specific corpus. Machine learning based NLP methods, give good results but generate outcomes that are not really understandable by a user. RESULTS: We take advantage of an hybridization of data mining and natural language processing to propose an original symbolicmethod to automatically produce patterns conveying gene interactions and their characterizations. Therefore, our method not only allows gene interactions but also semantics information on the extracted interactions (e.g., modalities, bio

Druh

J_x - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)

CEP obor

JC - Počítačový hardware a software

OECD FORD obor

—

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2015

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Journal of Biomedical Semantics

ISSN

2041-1480

e-ISSN

—

Svazek periodika

6:27

Číslo periodika v rámci svazku

6

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

12

Strana od-do

1-12

Kód UT WoS článku

000354922800001

EID výsledku v databázi Scopus

2-s2.0-84938814624