Infectious Disease

Machine Learning-Driven Discovery and Evaluation of Antimicrobial Peptides from Mucus Proteome.

Marine antimicrobial peptides (AMPs) represent a promising source for combating infections, especially against antibiotic-resistant pathogens and traditionally challenging infections. However, traditional drug discovery methods face challenges such as time-consuming processes and high costs. Therefore, leveraging machine learning techniques to expedite the discovery of marine AMPs holds significan...

Artificial Intelligence and Cardiology Practice in Nigeria: Are We Ready?

Cardiovascular diseases are the leading cause of death globally. As cardiovascular risk factors continuously rise to pandemic levels, there is intense pressure worldwide to improve cardiac care in preventive cardiology, cardio-diagnostics, therapeutics, and interventional cardiology. Artificial intelligence (AI), an advanced branch of computer science has ushered in the fourth industrial revolutio...

Identification of gene and protein signatures associated with long-term effects of COVID-19 on the immune system after patient recovery by analyzing single-cell multi-omics data using a machine learning approach.

Viral infections significantly impact the immune system, and impact will persist until recovery. However, the influence of severe acute respiratory sy...

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

An essential post-translational modification, phosphorylation is intimately related with a wide range of biological activities. The advancement of eff...

Machine learning and matrix-assisted laser desorption/ionization time-of-flight mass spectra for antimicrobial resistance prediction: A systematic review of recent advancements and future development.

BACKGROUND: The use of matrix-assisted laser desorption/ionization time-of-flight mass spectra (MALDI-TOF MS) combined with machine learning technique...

Applying Machine Learning for Antibiotic Development and Prediction of Microbial Resistance.

Antimicrobial resistance (AMR) poses a serious threat to human health worldwide. It is now more challenging than ever to introduce a potent antibiotic...

SmartCADD: AI-QM Empowered Drug Discovery Platform with Explainability.

Artificial intelligence (AI) has emerged as a pivotal force in enhancing productivity across various sectors, with its impact being profoundly felt wi...

COVID-19 Vaccine: Predicting Vaccine Types and Assessing Mortality Risk Through Ensemble Learning Algorithms.

BACKGROUND: There is no doubt that vaccination is crucial for preventing the spread of diseases; however, not every vaccine is perfect or will work fo...

Assessing Axillary Lymph Node Burden and Prognosis in cT1-T2 Stage Breast Cancer Using Machine Learning Methods: A Retrospective Dual-Institutional MRI Study.

BACKGROUND: Pathological axillary lymph node (pALN) burden is an important factor for treatment decision-making in clinical T1-T2 (cT1-T2) stage breas...

Using machine learning to dissect host kinases required for Leishmania internalization and development.

The Leishmania life cycle alternates between promastigotes, found in the sandfly, and amastigotes, found in mammals. When an infected sandfly bites a ...

LC-SRM Combined With Machine Learning Enables Fast Identification and Quantification of Bacterial Pathogens in Urinary Tract Infections.

Urinary tract infections (UTIs) are a worldwide health problem. Fast and accurate detection of bacterial infection is essential to provide appropriate...

StackDPPred: Multiclass prediction of defensin peptides using stacked ensemble learning with optimized features.

Host defense or antimicrobial peptides (AMPs) are promising candidates for protecting host against microbial pathogens for example bacteria, virus, fu...

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

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a single-stranded RNA virus, which mainly causes respiratory and enteric diseases and ...

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

CRISPR/Cas-based molecular diagnosis demonstrates potent potential for sensitive and rapid pathogen detection, notably in SARS-CoV-2 diagnosis and mut...

Accurate diagnosis of acute appendicitis in the emergency department: an artificial intelligence-based approach.

The diagnosis of abdominal pain in emergency departments is challenging, and appendicitis is a common concern. Atypical symptoms often delay diagnosis...

Machine learning surveillance of foodborne infectious diseases using wastewater microbiome, crowdsourced, and environmental data.

Clostridium perfringens (CP) is a common cause of foodborne infection, leading to significant human health risks and a high economic burden. Thus, eff...

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

The COVID-19 pandemic has necessitated the development of innovative and efficient methods for early detection and diagnosis. Integrating Internet of ...

Adapting nanopore sequencing basecalling models for modification detection via incremental learning and anomaly detection.

We leverage machine learning approaches to adapt nanopore sequencing basecallers for nucleotide modification detection. We first apply the incremental...

Generative artificial intelligence performs rudimentary structural biology modeling.

Natural language-based generative artificial intelligence (AI) has become increasingly prevalent in scientific research. Intriguingly, capabilities of...

Machine learning-causal inference based on multi-omics data reveals the association of altered gut bacteria and bile acid metabolism with neonatal jaundice.

Early identification of neonatal jaundice (NJ) appears to be essential to avoid bilirubin encephalopathy and neurological sequelae. The interaction be...