Profile of representative research of George F Gao. Try it.
Profile of representative research of Bing Zhu. Try it.
Profile of representative research of Shan Wang. Try it.
Profile of PAX6 function. Try it.
Profile of dme-bantam miRNA function. Try it.
BEND3 in differentiation. Try it.
Profile of Autophagy. Try it.
Profile of Sleep. Try it.
Profile of ATP9A function. Try it.
Profile of Kbhb. Try it.
Profile of JGG database issue in 2017. Try it.
Profile of ScienceBulletin in 2024. Try it.
Profile of ScienceBulletin in 2023. Try it.
Profile of ScienceBulletin in 2022. Try it.
Profile of ScienceBulletin in 2021. Try it.
Profile of ScienceBulletin in 2020. Try it.
Profile of Genomics, Proteomics & Bioinformatics in 2024. Try it.
Profile of Nature in 2024. Try it.
Profile of Cell in 2024. Try it.
Profile of Science in 2024. Try it.
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 summarization of biological findings, highlighting scientific hotspots, and the research profile of a journal's publications. 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 | WordCloud |
|---|---|---|---|
| Ranking methods | Ranking words by calculating correlation or frequency from inputted texts | 25 | 1 |
| Correlation similarity | Algorithms for calculating word correlation | 19 | |
| Layout | Diverse set of biological layouts including organs, organisms, cells and others | 29 | 1 |
| LLMs | APIs of pre-trained LLMs used in wViP | GPT-3.5-turbo, LLaMA-2-70b |
|
| Input formats | Text formats supported by wViP | TXT, Docx, PDF | TXT |
