Thursday, January 26, 2023
News on Pathogens and Preparedness
Global Biodefense
  • Featured
  • COVID-19
  • Funding
  • Directory
  • Jobs
  • Events
  • Subscribe
No Result
View All Result
  • Featured
  • COVID-19
  • Funding
  • Directory
  • Jobs
  • Events
  • Subscribe
No Result
View All Result
Global Biodefense
No Result
View All Result
Home Research

COVIDScholar: AI Tool Sifts Through Thousands of Papers to Guide Researchers

by Amalie Trewartha and John Dagdelen, University of California, Berkeley
May 12, 2020
COVIDScholar: AI Tool Sifts Through Thousands of Papers to Guide Researchers

Artificial intelligence can do what humans can’t – connect the dots across the majority of coronavirus research. baranozdemir/E+ via Getty Images

The scientific community worldwide has mobilized with unprecedented speed to tackle the COVID-19 pandemic, and the emerging research output is staggering.

Every day, hundreds of scientific papers about COVID-19 come out, in both traditional journals and non-peer-reviewed preprints. There’s already far more than any human could possibly keep up with, and more research is constantly emerging.

And it’s not just new research. We estimate that there are as many as 500,000 papers relevant to COVID-19 that were published before the outbreak, including papers related to the outbreaks of SARS in 2002 and MERS in 2012. Any one of these might contain the key information that leads to effective treatment or a vaccine for COVID-19.

Traditional methods of searching through the research literature just don’t cut it anymore. This is why we and our colleagues at Lawrence Berkeley National Lab are using the latest artificial intelligence techniques to build COVIDScholar, a search engine dedicated to COVID-19. COVIDScholar includes tools that pick up subtle clues like similar drugs or research methodologies to recommend relevant research to scientists. AI can’t replace scientists, but it can help them gain new insights from more papers than they could read in a lifetime.

COVIDScholar is a search engine with machine learning algorithms under the hood. Screen capture by The Conversation

Why it matters

When it comes to finding effective treatments for COVID-19, time is of the essence. Scientists spend 23% of their time searching for and reading papers. Every second our search tools can save them is more time to spend making discoveries in the lab and analyzing data.

AI can do more than just save scientists time. Our group’s previous work showed that AI can capture latent scientific knowledge from text, making connections that humans missed. There, we showed that AI was able to suggest new, cutting-edge functional materials years before their discovery by humans. The information was there all along, but it took combining information from hundreds of thousands of papers to find it.

We are now applying the same techniques to COVID-19, to find existing drugs that could be repurposed, genetic links that might help develop a vaccine or effective treatment regimens. We’re also starting to build in new innovations, like using molecular structures to help find which drugs are similar to each other, including those that are similar in unexpected ways.

How we do this work

The most important part of our work is the data. We’ve built web scrapers that collect new papers as they’re published from a wide variety of sources, making them available on our website within 15 minutes of their appearance online. We also clean the data, fixing mistakes in formatting and comparing the same paper from multiple sources to find the best version. Our machine learning algorithms then go to work on the paper, tagging it with subject categories and marking work important to COVID-19.

COVIDScholar labels and categorizes about 250 journal papers a day to help researchers make connections they might otherwise miss. Kevin Cruse and Haoyan Huo, CC BY-ND

We’re also continuously seeking out experts in new areas. Their input and annotation of data is what allows us to train new AI models.

What’s next

So far, we have assembled a collection of over 60,000 papers on COVID-19, and we’re expanding the collection daily. We’ve also built search tools that group research into categories, suggest related research and allow users to find papers that connect different concepts, such as papers that connect a specific drug to the diseases it’s been used to treat in the past. We’re now building AI algorithms that allow researchers to plug search results into quantitative models for studying topics like protein interactions. We’re also starting to dig through the past literature to find hidden gems.

We hope that very soon, researchers using COVIDScholar will start to identify relationships that they might never have imagined, bringing us closer to treatments and a remedy for COVID-19.

ABOUT THE AUTHORS

Amalie Trewartha, Post Doctoral Fellow, University of California, Berkeley and John Dagdelen, Graduate Student Researcher, Persson Group, University of California, Berkeley

This article is courtesy of The Conversation. Read the original article.

Tags: Artificial IntelligenceBioinformaticsCOVID-19Editor PickInnovationSARS-CoV-2

Related Posts

Influenza Proteins Tilt and Wave in ‘Breath-like’ Motions
Pathogens

Influenza Proteins Tilt and Wave in ‘Breath-like’ Motions

January 25, 2023
DARPA Selects Teams to Develop Vaccine Durability Prediction Model
Medical Countermeasures

DARPA Selects Teams to Develop Vaccine Durability Prediction Model

January 13, 2023
The device appears smaller than a playing card, transparent, with visible channels branching off.
Medical Countermeasures

How Organ-on-a-chip Models Could Grease the Drug Development Pipeline

January 10, 2023
New Virus Discovered in Swiss Ticks
Biosurveillance

New Virus Discovered in Swiss Ticks

December 7, 2022
Load More

Latest News

Partner Therapeutics’ Novel Approach to Stratify Sepsis Patients Gains Backing From BARDA

Biopreparedness Research Virtual Environment (BRaVE) Initiative Backed by $105M DOE Funding

January 25, 2023
Influenza Proteins Tilt and Wave in ‘Breath-like’ Motions

Influenza Proteins Tilt and Wave in ‘Breath-like’ Motions

January 25, 2023
Biodefense Headlines – 24 January 2023

Biodefense Headlines – 24 January 2023

January 24, 2023
Biodefense Headlines – 17 January 2023

Biodefense Headlines – 17 January 2023

January 17, 2023

Subscribe

  • About
  • Contact
  • Privacy
  • Subscribe

© 2022 Stemar Media Group LLC

No Result
View All Result
  • Featured
  • COVID-19
  • Funding
  • Directory
  • Jobs
  • Events
  • Subscribe

© 2022 Stemar Media Group LLC

We use cookies to ensure that we give you the best experience on our website. If you continue to use this site we will assume that you are okay with it.OkPrivacy policy