Hepatitis

Generative Adversarial Network-Based Augmentation With Noval 2-Step Authentication for Anti-Coronavirus Peptide Prediction.

The virus poses a longstanding and enduring danger to various forms of life. Despite the ongoing endeavors to combat viral diseases, there exists a necessity to explore and develop novel therapeutic options. Antiviral peptides are bioactive molecules with a favorable toxicity profile, making them promising alternatives for viral infection treatment. Therefore, this article employed a generative ad...

DMAMP: A Deep-Learning Model for Detecting Antimicrobial Peptides and Their Multi-Activities.

Due to the broad-spectrum and high-efficiency antibacterial activity, antimicrobial peptides (AMPs) and their functions have been studied in the field of drug discovery. Using biological experiments to detect the AMPs and corresponding activities require a high cost, whereas computational technologies do so for much less. Currently, most computational methods solve the identification of AMPs and t...

An explainable deep learning platform for molecular discovery.

Deep learning approaches have been increasingly applied to the discovery of novel chemical compounds. These predictive approaches can accurately model...

The isolation, bioactivity, and synthesis of natural products from with anti-HIV activities.

Natural products isolated from have attracted considerable attention from the chemical community due to their unique structures and promising anti-HI...

VaxOptiML: leveraging machine learning for accurate prediction of MHC-I and II epitopes for optimized cancer immunotherapy.

Cancer immunotherapy hinges on accurate epitope prediction for advancing vaccine development. VaxOptiML (available at https://vaxoptiml.streamlit.app/...

Using machine learning for personalized prediction of longitudinal coronavirus disease 2019 vaccine responses in transplant recipients.

The coronavirus disease 2019 pandemic has underscored the importance of vaccines, especially for immunocompromised populations like solid organ transp...

High-throughput Kinetics using capillary Electrophoresis and Robotics (HiKER) platform used to study T7, T3, and Sp6 RNA polymerase misincorporation.

T7 RNA Polymerase (RNAP) is a widely used enzyme with recent applications in the production of RNA vaccines. For over 50 years denaturing sequencing g...

Construction and validation of a machine learning-based prediction model for short-term mortality in critically ill patients with liver cirrhosis.

OBJECTIVE: Critically ill patients with liver cirrhosis generally have a poor prognosis due to complications such as multiple organ failure. This stud...

Integrating Interpretability in Machine Learning and Deep Neural Networks: A Novel Approach to Feature Importance and Outlier Detection in COVID-19 Symptomatology and Vaccine Efficacy.

In this study, we introduce a novel approach that integrates interpretability techniques from both traditional machine learning (ML) and deep neural n...

Machine learning-enhanced immunopeptidomics applied to T-cell epitope discovery for COVID-19 vaccines.

Next-generation T-cell-directed vaccines for COVID-19 focus on establishing lasting T-cell immunity against current and emerging SARS-CoV-2 variants. ...

Analysis of four long non-coding RNAs for hepatocellular carcinoma screening and prognosis by the aid of machine learning techniques.

Hepatocellular carcinoma (HCC) represents a significant health burden in Egypt, largely attributable to the endemic prevalence of hepatitis B and C vi...

-targeted AI-driven vaccines: a paradigm shift in gastric cancer prevention.

, a globally prevalent pathogen Group I carcinogen, presents a formidable challenge in gastric cancer prevention due to its increasing antimicrobial r...

A deep learning approach predicting the activity of COVID-19 therapeutics and vaccines against emerging variants.

Understanding which viral variants evade neutralization is crucial for improving antibody-based treatments, especially with rapidly evolving viruses l...

Current update on the neurological manifestations of long COVID: more questions than answers.

Since the outbreak of the COVID-19 pandemic, there has been a global surge in patients presenting with prolonged or late-onset debilitating sequelae o...

Analysis of the relationships between interferon-stimulated genes and anti-SSA/Ro 60 antibodies in primary Sjögren's syndrome patients via multiomics and machine learning methods.

BACKGROUND: Primary Sjögren's syndrome (pSS) is a chronic systemic autoimmune disease characterized by lymphocyte infiltration of the exocrine glands....

Predictive model of in-hospital mortality in liver cirrhosis patients with hyponatremia: an artificial neural network approach.

Hyponatremia can worsen the outcomes of patients with liver cirrhosis. However, it remains unclear about how to predict the risk of death in cirrhotic...

Discovery of Active Ingredient of Yinchenhao Decoction Targeting TLR4 for Hepatic Inflammatory Diseases Based on Deep Learning Approach.

Yinchenhao Decoction (YCHD), a classic formula in traditional Chinese medicine, is believed to have the potential to treat liver diseases by modulatin...

Accurate non-invasive detection of MASH with fibrosis F2-F3 using a lightweight machine learning model with minimal clinical and metabolomic variables.

BACKGROUND: There are no known non-invasive tests (NITs) designed for accurately detecting metabolic dysfunction-associated steatohepatitis (MASH) wit...

Machine-learning methodologies to predict disease progression in chronic hepatitis B in Africa.

BACKGROUND: Little is known about the determinants of disease progression among African patients with chronic HBV infection.

Machine learning algorithms for prediction of measles one vaccination dropout among 12-23 months children in Ethiopia.

INTRODUCTION: Despite the availability of a safe and effective measles vaccine in Ethiopia, the country has experienced recurrent and significant meas...