We use cookies to understand how you use our site and to improve your experience. This includes personalizing content and advertising. To learn more, click here. By continuing to use our site, you accept our use of cookies. Cookie Policy.

Features Partner Sites Information LinkXpress
Sign In
Advertise with Us

Download Mobile App





Researchers Use Natural-Language Processing (NLP) Algorithms to Predict SARS-CoV-2 Virus Mutations

By HospiMedica International staff writers
Posted on 18 Jan 2021
Print article
Image: Researchers Use NLP Algorithms to Predict SARS-CoV-2 Virus Mutations (Photo courtesy of Baidu)
Image: Researchers Use NLP Algorithms to Predict SARS-CoV-2 Virus Mutations (Photo courtesy of Baidu)
Natural-language processing (NLP) algorithms are now able to generate protein sequences and predict virus mutations, including key changes that help the SARS-CoV-2 virus evade the immune system.

The key insight making this possible is that many properties of biological systems can be interpreted in terms of words and sentences. In the last few years, a handful of researchers have shown that protein sequences and genetic codes can be modeled using NLP techniques. Now, computational biologists at the Massachusetts Institute of Technology (MIT; Cambridge, MA, USA) have pulled several of these strands together and use NLP to predict mutations that allow viruses to avoid being detected by antibodies in the human immune system, a process known as viral immune escape. The basic idea is that the interpretation of a virus by an immune system is analogous to the interpretation of a sentence by a human.

The team uses two different linguistic concepts: grammar and semantics (or meaning). The genetic or evolutionary fitness of a virus - characteristics such as how good it is at infecting a host - can be interpreted in terms of grammatical correctness. A successful, infectious virus is grammatically correct; an unsuccessful one is not. Similarly, mutations of a virus can be interpreted in terms of semantics. Mutations that make a virus appear different to things in its environment - such as changes in its surface proteins that make it invisible to certain antibodies - have altered its meaning. Viruses with different mutations can have different meanings, and a virus with a different meaning may need different antibodies to read it.

To model these properties, the researchers used an LSTM, a type of neural network that predates the transformer-based ones used by large language models like GPT-3. These older networks can be trained on far less data than transformers and still perform well for many applications. Instead of millions of sentences, they trained the NLP model on thousands of genetic sequences taken from three different viruses: 45,000 unique sequences for a strain of influenza, 60,000 for a strain of HIV, and between 3,000 and 4,000 for a strain of the SARS-CoV-2 virus.

NLP models work by encoding words in a mathematical space in such a way that words with similar meanings are closer together than words with different meanings. This is known as an embedding. For viruses, the embedding of the genetic sequences grouped viruses according to how similar their mutations were. The overall aim of the approach is to identify mutations that might let a virus escape an immune system without making it less infectious - that is, mutations that change a virus’s meaning without making it grammatically incorrect.

To test their approach, the team used a common metric for assessing predictions made by machine-learning models that scores accuracy on a scale between 0.5 (no better than chance) and 1 (perfect). In this case, they took the top mutations identified by the tool and, using real viruses in a lab, checked how many of them were actual escape mutations. Their results ranged from 0.69 for HIV to 0.85 for one coronavirus strain. This is better than results from other state-of-the-art models, according to the researchers.

The team has been running models on new variants of the coronavirus, including the so-called UK mutation, the mink mutation from Denmark, and variants taken from South Africa, Singapore and Malaysia. Using NLP accelerates a slow process. Previously, the genome of the virus taken from a COVID-19 patient in hospital could be sequenced and its mutations re-created and studied in a lab. However, that can take weeks, whereas the NLP model predicts potential mutations straight away, which focuses the lab work and speeds it up.

“We’re learning the language of evolution,” said Bonnie Berger, a computational biologist at the Massachusetts Institute of Technology. “Biology has its own language.”

Related Links:
Massachusetts Institute of Technology (MIT)

Gold Member
SARS‑CoV‑2/Flu A/Flu B/RSV Sample-To-Answer Test
SARS‑CoV‑2/Flu A/Flu B/RSV Cartridge (CE-IVD)
Gold Member
Disposable Protective Suit For Medical Use
Disposable Protective Suit For Medical Use
Silver Member
Compact 14-Day Uninterrupted Holter ECG
NR-314P
New
Infant Blood Draw Station
Infant Blood Draw Station

Print article

Channels

Critical Care

view channel
Image: The permeable wearable electronics developed for long-term biosignal monitoring (Photo courtesy of CityUHK)

Super Permeable Wearable Electronics Enable Long-Term Biosignal Monitoring

Wearable electronics have become integral to enhancing health and fitness by offering continuous tracking of physiological signals over extended periods. This monitoring is crucial for understanding an... Read more

Surgical Techniques

view channel
Image: NTT and Olympus have begun the world\'s first joint demonstration experiment of a cloud endoscopy system (Photo courtesy of Olympus)

Cloud Endoscopy System Enables Real-Time Image Processing on the Cloud

Endoscopes, which are flexible tubes inserted into the body's natural openings for internal examination and biopsy collection, are becoming increasingly vital in medical diagnostics. Their minimal invasiveness... Read more

Patient Care

view channel
Image: The newly-launched solution can transform operating room scheduling and boost utilization rates (Photo courtesy of Fujitsu)

Surgical Capacity Optimization Solution Helps Hospitals Boost OR Utilization

An innovative solution has the capability to transform surgical capacity utilization by targeting the root cause of surgical block time inefficiencies. Fujitsu Limited’s (Tokyo, Japan) Surgical Capacity... Read more

Health IT

view channel
Image: First ever institution-specific model provides significant performance advantage over current population-derived models (Photo courtesy of Mount Sinai)

Machine Learning Model Improves Mortality Risk Prediction for Cardiac Surgery Patients

Machine learning algorithms have been deployed to create predictive models in various medical fields, with some demonstrating improved outcomes compared to their standard-of-care counterparts.... Read more

Point of Care

view channel
Image: The PATHFAST hs-cTnI-II high-sensitivity troponin assay has been developed for the PATHFAST Biomarker Analyzer (Photo courtesy of Polymedco)

POC Myocardial Infarction Test Delivers Results in 17 Minutes

Chest pain is the second leading cause of emergency department (ED) visits by adults in the United States, generating over 7 million visits annually. In the event of a suspected heart attack, physicians... Read more
Copyright © 2000-2024 Globetech Media. All rights reserved.