SparkText: Biomedical Text Mining on Big Data Framework.

ABSTRACT:

Background

Many new biomedical research articles are published every day, accumulating rich information, such as genetic variants, genes, diseases, and treatments. Rapid yet accurate text mining on large-scale scientific literature can discover novel knowledge to better understand human diseases and to improve the quality of disease diagnosis, prevention, and treatment.

Results

In this study, we designed and developed an efficient text mining framework called SparkText on a Big Data infrastructure, which is composed of Apache Spark data streaming and machine learning methods, combined with a Cassandra NoSQL database. To demonstrate its performance for classifying cancer types, we extracted information (e.g., breast, prostate, and lung cancers) from tens of thousands of artic

SUBMITTER: Ye Z 

PROVIDER: S-EPMC5042555 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature