godseye

Monitor biomedical trends visually across ALL of PubMed + bioRxiv

Bioinformatics is one of the fastest growing interdisciplinary fields. As new technologies emerge, new types of data come into the spotlight, thereby creating the need for novel computational approaches and methodologies that can successfully deal with those new data. As a result, the interest of the community for specific areas often shifts dramatically over a short amount of time. Here we present a text mining approach to systematically identify trending topics in Bioinformatics over time and space, as embodied in journal articles' abstracts and titles.

Using keyword prominence and an efficient temporal segmentation algorithm, our method highlights trending topics in the bioinformatics literature, and can be helpful in predicting the ever-changing demands of the bioinformatics job market.

We quilted together the NCBI MEDLINE®/PubMed® database and bioRxiv® database to extract all titles, abstracts, and author affiliations of all published papers and preprints, respectively.

By including bioRxiv, godseye can monitor the pulse of the biological research community with a delay of several days at most, which is the average time it takes for a preprint to be publicly displayed on bioRxiv upon submission. In contrast, PubMed's delay is at least 3-18 months, which is often the time range of the lengthy peer-review cycle. Once a preprint is published in a peer-reviewed journal and thereby available on PubMed, godseye delegates to the PubMed resource for extracting information from the abstract/title (since these are often updated relative to the bioRxiv version of the paper).

We define the prominence of a keyword w as the fraction of journal abstracts in a given time range that contain the keyword w. An arbitrary parameter α is then chosen to filter out keywords whose prominence is < α.

The main idea is to optimally segment the yearly data into smaller contiguous time ranges, in a way that maximizes the overlap of prominent keywords within the resulting temporal segments. The algorithm uses dynamic programming to efficiently compute an optimal segmentation, and takes as input the keyword frequency per year and the desired number of temporal segments n. For more information on the original implementation of the algorithm, see Siy et al. The algorithm returns the optimal segments and a list of prominent keywords in each segment.

• Modularize Python code with OOP methods
• Analyze PDF/HTML contents of a PubMed or bioRxiv paper, not just its abstract, title, and author affiliations. For this task, consider integrating existing tools like fulltext, pdftools, and pubcrawl
• Implement dynamic programming algorithm to achieve optimal temporal segmentation. But also re-consider other algorithm choices (besides for temporal segmentation) because perhaps there are other more suitable (optimal) alternatives
• Expand godseye into ML territory with snorkel
• Implement graph database to understand the relation between any set of keywords over a time period or geographical region (e.g., the spatial and temporal evolution of keyword co-occurrences)

You are welcome to:

• submit suggestions and bug-reports at: https://github.com/Quiltomics/godseye/issues
• send a pull request on: https://github.com/Quiltomics/godseye
• compose an e-mail to: [email protected]

Please note that this project is released with a Contributor Code of Conduct. By participating in this project you agree to abide by its terms.

• Nanda Kishore Adapa
• Parvathi Chundi, Ph.D.
• Dario Ghersi, M.D. Ph.D.
• Bohdan Khomtchouk, Ph.D.
• Kasra A. Vand

This work is a hard fork of biotrends. It is an academic partnership between Drs. Ghersi and Khomtchouk at UNO and Stanford, respectively.

Coming soon!