AIMC Topic: SARS-CoV-2

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Res-GCN: Identification of protein phosphorylation sites using graph convolutional network and residual network.

Computational biology and chemistry
An essential post-translational modification, phosphorylation is intimately related with a wide range of biological activities. The advancement of effective computational methods for correctly recognizing phosphorylation sites is important for in-dep...

PhosBERT: A self-supervised learning model for identifying phosphorylation sites in SARS-CoV-2-infected human cells.

Methods (San Diego, Calif.)
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a single-stranded RNA virus, which mainly causes respiratory and enteric diseases and is responsible for the outbreak of coronavirus disease 19 (COVID-19). Numerous studies have demonstr...

High-Throughput and Integrated CRISPR/Cas12a-Based Molecular Diagnosis Using a Deep Learning Enabled Microfluidic System.

ACS nano
CRISPR/Cas-based molecular diagnosis demonstrates potent potential for sensitive and rapid pathogen detection, notably in SARS-CoV-2 diagnosis and mutation tracking. Yet, a major hurdle hindering widespread practical use is its restricted throughput,...

Hybrid optimal feature selection-based iterative deep convolution learning for COVID-19 classification system.

Computers in biology and medicine
The COVID-19 pandemic has necessitated the development of innovative and efficient methods for early detection and diagnosis. Integrating Internet of Things (IoT) devices and applications in healthcare has facilitated various functions. This work aim...

Generative artificial intelligence performs rudimentary structural biology modeling.

Scientific reports
Natural language-based generative artificial intelligence (AI) has become increasingly prevalent in scientific research. Intriguingly, capabilities of generative pre-trained transformer (GPT) language models beyond the scope of natural language tasks...

Confidence-Aware Severity Assessment of Lung Disease from Chest X-Rays Using Deep Neural Network on a Multi-Reader Dataset.

Journal of imaging informatics in medicine
In this study, we present a method based on Monte Carlo Dropout (MCD) as Bayesian neural network (BNN) approximation for confidence-aware severity classification of lung diseases in COVID-19 patients using chest X-rays (CXRs). Trained and tested on 1...

Enhancing COVID-19 forecasting precision through the integration of compartmental models, machine learning and variants.

Scientific reports
Predicting epidemic evolution is essential for making informed decisions and guiding the implementation of necessary countermeasures. Computational models are vital tools that provide insights into illness progression and enable early detection, proa...

COVID-19 IgG antibodies detection based on CNN-BiLSTM algorithm combined with fiber-optic dataset.

Journal of virological methods
The urgent need for efficient and accurate automated screening tools for COVID-19 detection has led to research efforts exploring various approaches. In this study, we present pioneering research on COVID-19 detection using a hybrid model that combin...

Identifying COVID-19 survivors living with post-traumatic stress disorder through machine learning on Twitter.

Scientific reports
The COVID-19 pandemic has disrupted people's lives and caused significant economic damage around the world, but its impact on people's mental health has not been paid due attention by the research community. According to anecdotal data, the pandemic ...

Revolutionizing adjuvant development: harnessing AI for next-generation cancer vaccines.

Frontiers in immunology
With the COVID-19 pandemic, the importance of vaccines has been widely recognized and has led to increased research and development efforts. Vaccines also play a crucial role in cancer treatment by activating the immune system to target and destroy c...