AIMC Topic: COVID-19

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iAFPs-Mv-BiTCN: Predicting antifungal peptides using self-attention transformer embedding and transform evolutionary based multi-view features with bidirectional temporal convolutional networks.

Artificial intelligence in medicine
Globally, fungal infections have become a major health concern in humans. Fungal diseases generally occur due to the invading fungus appearing on a specific portion of the body and becoming hard for the human immune system to resist. The recent emerg...

Discovery of Covalent Lead Compounds Targeting 3CL Protease with a Lateral Interactions Spiking Neural Network.

Journal of chemical information and modeling
Covalent drugs exhibit advantages in that noncovalent drugs cannot match, and covalent docking is an important method for screening covalent lead compounds. However, it is difficult for covalent docking to screen covalent compounds on a large scale b...

Automated Detection of COVID-19 from Multimodal Imaging Data Using Optimized Convolutional Neural Network Model.

Journal of imaging informatics in medicine
The incidence of COVID-19, a virus that is responsible for infections in the upper respiratory tract and lungs, witnessed a daily rise in fatalities throughout the pandemic. The timely identification of COVID-19 can contribute to the formulation of s...

Application of MALDI-TOF MS and machine learning for the detection of SARS-CoV-2 and non-SARS-CoV-2 respiratory infections.

Microbiology spectrum
Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) could aid the diagnosis of acute respiratory infections (ARIs) owing to its affordability and high-throughput capacity. MALDI-TOF MS has been proposed for use...

Understanding COVID-19 infection among people with intellectual and developmental disabilities using machine learning.

Disability and health journal
BACKGROUND: People with intellectual and developmental disabilities (IDD) were disproportionately affected by the COVID-19 pandemic. Predicting COVID-19 infection has been difficult.

Deep learning in public health: Comparative predictive models for COVID-19 case forecasting.

PloS one
The COVID-19 pandemic has had a significant impact on both the United Arab Emirates (UAE) and Malaysia, emphasizing the importance of developing accurate and reliable forecasting mechanisms to guide public health responses and policies. In this study...

MIS-Net: A deep learning-based multi-class segmentation model for CT images.

PloS one
The accuracy of traditional CT image segmentation algorithms is hindered by issues such as low contrast and high noise in the images. While numerous scholars have introduced deep learning-based CT image segmentation algorithms, they still face challe...

Multi-modal deep learning methods for classification of chest diseases using different medical imaging and cough sounds.

PloS one
Chest disease refers to a wide range of conditions affecting the lungs, such as COVID-19, lung cancer (LC), consolidation lung (COL), and many more. When diagnosing chest disorders medical professionals may be thrown off by the overlapping symptoms (...

Binding Activity Classification of Anti-SARS-CoV-2 Molecules using Deep Learning Across Multiple Assays.

Balkan medical journal
BACKGROUND: The coronavirus disease-2019 (COVID-19) pandemic, caused by severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2), has urgently necessitated effective therapeutic solutions, with a focus on rapidly identifying and classifying poten...

Evaluation of Effectiveness of Self-Supervised Learning in Chest X-Ray Imaging to Reduce Annotated Images.

Journal of imaging informatics in medicine
A significant challenge in machine learning-based medical image analysis is the scarcity of medical images. Obtaining a large number of labeled medical images is difficult because annotating medical images is a time-consuming process that requires sp...