Meta-analysis and other approaches for synthesizing structured and unstructured data in plant pathology

Area of Science:

• Plant Pathology
• Bioinformatics
• Computational Biology

Background:

• The "data deluge" refers to the exponential growth of scientific information from diverse sources.
• Traditional data analysis methods struggle with the volume and complexity of this data.
• This presents challenges but also opportunities for scientific discovery.

Purpose of the Study:

• To review analytical approaches for synthesizing the vast information generated by the data deluge.
• To accelerate the pace of discovery in plant pathology using advanced data synthesis techniques.

Main Methods:

• Review of meta-analysis for summarizing structured data across literature.
• Exploration of data-mining approaches for complex, unstructured datasets, including 'omics data.
• Discussion of web crawling, text mining, and social media analytics for novel datasets.

Main Results:

• Established methods like meta-analysis can summarize structured data.
• Data-mining and 'omics integration offer new ways to analyze complex epidemiological data.
• Web crawling, text mining, and social media analytics enable digital disease surveillance.

Conclusions:

• Advanced analytical approaches are crucial for navigating the data deluge in plant pathology.
• Integrating diverse data types and employing novel computational methods accelerates scientific discovery.
• Future research will leverage new data scales and types to address significant questions in plant pathology.