AIMC Topic: SARS-CoV-2

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Deep learning for discriminating non-trivial conformational changes in molecular dynamics simulations of SARS-CoV-2 spike-ACE2.

Scientific reports
Molecular dynamics (MD) simulations produce a substantial volume of high-dimensional data, and traditional methods for analyzing these data pose significant computational demands. Advances in MD simulation analysis combined with deep learning-based a...

Managing spatio-temporal heterogeneity of susceptibles by embedding it into an homogeneous model: A mechanistic and deep learning study.

PLoS computational biology
Accurate prediction of epidemics is pivotal for making well-informed decisions for the control of infectious diseases, but addressing heterogeneity in the system poses a challenge. In this study, we propose a novel modelling framework integrating the...

Development of a COVID-19 early risk assessment system based on multiple machine learning algorithms and routine blood tests: a real-world study.

Frontiers in immunology
BACKGROUNDS: During the Coronavirus Disease 2019 (COVID-19) epidemic, the massive spread of the disease has placed an enormous burden on the world's healthcare and economy. The early risk assessment system based on a variety of machine learning (ML) ...

COVID-19 from symptoms to prediction: A statistical and machine learning approach.

Computers in biology and medicine
During the COVID-19 pandemic, the analysis of patient data has become a cornerstone for developing effective public health strategies. This study leverages a dataset comprising over 10,000 anonymized patient records from various leading medical insti...

A novel hybrid deep learning IChOA-CNN-LSTM model for modality-enriched and multilingual emotion recognition in social media.

Scientific reports
In the rapidly evolving field of artificial intelligence, the importance of multimodal sentiment analysis has never been more evident, especially amid the ongoing COVID-19 pandemic. Our research addresses the critical need to understand public sentim...

Relationship matters: Using machine learning methods to predict the mental health severity of Chinese college freshmen during the pandemic period.

Journal of affective disorders
BACKGROUND: Pandemics act as stressors and may lead to frequent mental health disorders. College student, especially freshmen, are particularly susceptible to experiencing intense mental stress reactions during a pandemic. We aimed to identify stable...

Large-scale deep learning identifies the antiviral potential of PKI-179 and MTI-31 against coronaviruses.

Antiviral research
Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) has led to the global pandemic of Coronavirus Disease (2019) (COVID-19), underscoring the urgency for effective antiviral drugs. Despite the development of different vaccination strategies,...

Does COVID-19 lockdown matter for air pollution in the short and long run in China? A machine learning approach to policy evaluation.

Journal of environmental management
This paper leverages a data-driven two-step approach to effectively evaluate the effects of COVID-19 lockdown on air pollution in both the short and long-term in China. Using air pollution, meteorological conditions, and air mass clusters from 34 air...

Crowd-sourced machine learning prediction of long COVID using data from the National COVID Cohort Collaborative.

EBioMedicine
BACKGROUND: While many patients seem to recover from SARS-CoV-2 infections, many patients report experiencing SARS-CoV-2 symptoms for weeks or months after their acute COVID-19 ends, even developing new symptoms weeks after infection. These long-term...

Machine learning-enabled mental health risk prediction for youths with stressful life events: A modelling study.

Journal of affective disorders
BACKGROUND: Youths face significant mental health challenges exacerbated by stressful life events, particularly in the context of the COVID-19 pandemic. Immature coping strategies can worsen mental health outcomes.