Influenza Virus

Influenza viruses pose a significant global health threat, necessitating research focused on accurate prediction of outbreaks and rapid, reliable identification of viral strains. Current research employs machine learning, particularly neural networks (including recurrent and convolutional architectures) and random forests, to analyze diverse data sources such as epidemiological data, web search activity, and viral genetic sequences (e.g., hemagglutinin) from transmission electron microscopy images. These advanced computational methods aim to improve influenza forecasting, enhance diagnostic capabilities, and ultimately contribute to more effective public health interventions.

Papers

September 2, 2024

Forecasting infectious disease prevalence with associated uncertainty using neural networks
Michael Morris
Neural Network High Uncertainty Anticipation Uncertainty Estimation Epidemic Forecasting Forecasting Task Prevalence Threshold Superior Forecasting Influenza Virus

July 26, 2024

FLUE: Federated Learning with Un-Encrypted model weights
Elie Atallah
Private Data Encryption Method Encrypted Model Influenza Virus

May 24, 2024

Heterogeneous virus classification using a functional deep learning model based on transmission electron microscopy images (Preprint)
Niloy Sikder, Md. Al-Masrur Khan, Anupam Kumar Bairagi, Mehedi Masud, Jun Jiat Tiang, Abdullah-Al Nahid
Microscopy Image Transmission Electron Microscopy Functional Deep Neural Network Influenza Virus

September 28, 2023

AE-GPT: Using Large Language Models to Extract Adverse Events from Surveillance Reports-A Use Case with Influenza Vaccine Adverse Events
Yiming Li, Jianfu Li, Jianping He, Cui Tao
Vaccine Related Adverse Event Surveillance Application Influenza Virus

November 5, 2022

Efficient Cavity Searching for Gene Network of Influenza A Virus
Junjie Li, Jietong Zhao, Yanqing Su, Jiahao Shen, Yaohua Liu, Xinyue Fan, Zheng Kou
Complex Network Cavity Expansion Influenza Virus Genetic Network Gene Network

July 28, 2022

Dive into Machine Learning Algorithms for Influenza Virus Host Prediction with Hemagglutinin Sequences
Yanhua Xu, Dominik Wojtczak
Machine Learning Algorithm DIVeR Identification Scoring Model Influenza Virus Viral Host

June 17, 2022

Random Forest of Epidemiological Models for Influenza Forecasting
Majd Al Aawar, Ajitesh Srivastava
Random Forest Forecasting Model Influenza Virus Tree Ensemble Model Influenza Forecasting

June 8, 2022

Multi-channel neural networks for predicting influenza A virus hosts and antigenic types
Yanhua Xu, Dominik Wojtczak
Neural Network Protein Sequence Multi Channel Influenza Virus Viral Host

January 4, 2022

Predicting Influenza A Viral Host Using PSSM and Word Embeddings
Yanhua Xu, Dominik Wojtczak
Word Embeddings Rapid Detection Scoring Model Influenza Virus Viral Host Influenza Forecasting

December 14, 2021

COVID-19 Pneumonia and Influenza Pneumonia Detection Using Convolutional Neural Networks
Julianna Antonchuk, Benjamin Prescott, Philip Melanchthon, Robin Singh
Convolutional Neural Network Covid 19 Radiology Imaging Ventilator Associated Pneumonia Pneumonia Detection Influenza Virus

Influenza Virus

Papers

Forecasting infectious disease prevalence with associated uncertainty using neural networks

FLUE: Federated Learning with Un-Encrypted model weights

Heterogeneous virus classification using a functional deep learning model based on transmission electron microscopy images (Preprint)

AE-GPT: Using Large Language Models to Extract Adverse Events from Surveillance Reports-A Use Case with Influenza Vaccine Adverse Events

Efficient Cavity Searching for Gene Network of Influenza A Virus

Dive into Machine Learning Algorithms for Influenza Virus Host Prediction with Hemagglutinin Sequences

Random Forest of Epidemiological Models for Influenza Forecasting

Multi-channel neural networks for predicting influenza A virus hosts and antigenic types

Predicting Influenza A Viral Host Using PSSM and Word Embeddings

COVID-19 Pneumonia and Influenza Pneumonia Detection Using Convolutional Neural Networks