A bioinformatics system for searching Co-Occurrence based on Co-Operational Formation with Advanced Method (COCOFAM)

TL;DR

This paper introduces COCOFAM, a novel bioinformatics search system that efficiently analyzes large-scale biomedical literature by leveraging co-occurrence data and advanced computational methods.

Contribution

The paper presents COCOFAM, a new system integrating Spark and global scheduling to improve literature search efficiency in biomedical research.

Findings

Enables large-scale literature analysis with high efficiency.

Utilizes co-occurrence data for better knowledge extraction.

Supports systematic and rapid literature searches.

Abstract

Literature analysis is a key step in obtaining background information in biomedical research. However, it is difficult for researchers to obtain knowledge of their interests in an efficient manner because of the massive amount of the published biomedical literature. Therefore, efficient and systematic search strategies are required, which allow ready access to the substantial amount of literature. In this paper, we propose a novel search system, named Co-Occurrence based on Co-Operational Formation with Advanced Method(COCOFAM) which is suitable for the large-scale literature analysis. COCOFAM is based on integrating both Spark for local clusters and a global job scheduler to gather crowdsourced co-occurrence data on global clusters. It will allow users to obtain information of their interests from the substantial amount of literature.