Since the appearance of coronavirus for COVID-19 in last November, the spurt in the number of academic articles on the disease has led to touch more than 50,000. The massive volume of new information is not necessarily a good thing though!
Meanwhile, all the recent coronavirus literature is not reviewed. This makes it challenging for accurate and promising research to stand out and be pursued for further research.
To address this, a team of researchers is creating an artificial intelligence platform – Semantic Visualization of Scientific Data – the Brandeis team led by a computer science and linguistics professor. The platform is designed to sort the growing volume of published work on coronavirus and help biologists gain insights. Besides, this will help researchers notice trends and patterns across research that could result in treatment or cure.
The expert of the Brandeis team is partnering with colleagues at Harvard University, Tufts University, Vassar College, and the University of Illinois.
Research Lead shares view of Leveraging Linguistic Background
The lead researcher who is a computational linguist shared how he is leveraging his background for coronavirus research. The researcher focuses on language and to extract information from large amounts of text, such as COVID-19 dataset, which now includes above 50, 000 academic articles. Meanwhile, biologists at the forefronof coronavirus are trying to find relations between genes, proteins, and drugs and how their interaction with the virus in the human body.
The artificial intelligence platform sifts through existing manuscripts and papers and enables scientists to connect and make generalizations that cannot be related reading one paper at a time.
For biologists studying coronavirus, the AI platform – SemViz – gives a rapid way to observe a global overview of regulators, inhibitors, and activators of proteins and genes involved in the disease.