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Related Experiment Video

Updated: Mar 14, 2026

Cloud-Based Phrase Mining and Analysis of User-Defined Phrase-Category Association in Biomedical Publications

Cloud-Based Phrase Mining and Analysis of User-Defined Phrase-Category Association in Biomedical Publications

Published on: February 23, 2019

SparkText: Biomedical Text Mining on Big Data Framework.

Zhan Ye¹, Ahmad P Tafti^2,3, Karen Y He⁴

¹Biomedical Informatics Research Center, Marshfield Clinic Research Foundation, Marshfield, WI, 54449, United States of America.

|September 30, 2016

Summary

This summary is machine-generated.

SparkText, a Big Data text mining framework, efficiently classifies cancer types from scientific articles. It achieved 93.81% accuracy in just 6 minutes, outperforming traditional methods.

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Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases

Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases

Published on: May 17, 2019

Area of Science:

Biomedical Informatics
Computational Biology
Data Science

Background:

Daily publication of biomedical research generates vast data on genes, diseases, and treatments.
Accurate text mining of scientific literature is crucial for advancing disease understanding and improving healthcare.

Purpose of the Study:

To develop an efficient text mining framework for large-scale biomedical literature.
To demonstrate the framework's capability in classifying cancer types.

Main Methods:

Developed SparkText on a Big Data infrastructure using Apache Spark, machine learning, and a Cassandra NoSQL database.
Extracted cancer-related information from tens of thousands of PubMed articles.
Utilized Naïve Bayes, Support Vector Machine (SVM), and Logistic Regression for classification models.

Main Results:

SparkText achieved 93.81% accuracy in predicting cancer types using SVM on 29,437 full-text articles.
The framework processed the dataset in approximately 6 minutes, significantly faster than competing tools (over 11 hours).

Conclusions:

Big Data infrastructure enables efficient mining of large-scale scientific articles with real-time updates.
SparkText demonstrates potential for real-time biomedical knowledge discovery and can be applied to other research areas.