With the rapid progression in biological researches, an expansive array of biological profiles in diverse topics with complex implications has emerged. It is challenging for researchers to easily and rapidly capture the hotspots from such massive literature. Here, we provide an easy-to-use web server, word cloud visualization of biological profiles (wViP), which aims to transfer the scientific text into a concise and fantastic word cloud based on the correlation among in-context words. Facilitated with Large Language Models (LLMs), we could further refine and visualize the association between the topic word(s) with other words in inputted texts for various purposes, including but not limited to profiling scientists, summarizing biological findings, capturing scientific hotspots, and modeling topics from literature. We anticipate that wViP will be a powerful online service to visualize various biological profiles with the word cloud from the scientific literature.
For the help of wViP and the tutorial, please refer to the Documentation page.
For the source code of wViP, please visit the GitHub page.
| Aspect | Description | wViP | WordItOut | WordClouds.com |
|---|---|---|---|---|
| Ranking methods | Ranking words by calculating correlation or frequency from inputted texts | 25 | 1 | 1 |
| Correlation similarity | Algorithms for calculating word correlation | 19 | ||
| Layout | Diverse set of biological layouts including organs, organisms, cells and others | 57 | 1 | 5 |
| LLMs | APIs of pre-trained LLMs used in wViP | DeepSeek-V3-0324, DeepSeek-R1-0528, GPT-o4-mini, GPT-o3-mini, GPT-4.1-mini, GPT-4o-mini, GPT-3.5-turbo, Mistral Large, Mistral Medium 3 |
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| Input formats | Text formats supported by wViP | TXT, Docx, PDF, XML | TXT | TXT, Docx, PDF, Web page |
