相关实验视频

Updated: May 13, 2025

High-throughput Detection Method for Influenza Virus
10:05

High-throughput Detection Method for Influenza Virus

Published on: February 4, 2012

26.1K

MIFlu:基于大型语言模型的多模式流感预测计划

Jaeuk Moon, Jonghwa Shim, Eunbeen Kim

    IEEE journal of biomedical and health informatics
    |April 15, 2025
    PubMed
    概括

    准确的流感预测对公共卫生至关重要. 一种新的多式联络方法,MIFlu,使用大型语言模型 (LLM) 来融合文本和时间序列数据,提高流感预测高达26.2%.

    更多相关视频

    Using Zebrafish Models of Human Influenza A Virus Infections to Screen Antiviral Drugs and Characterize Host Immune Cell Responses09:07

    Using Zebrafish Models of Human Influenza A Virus Infections to Screen Antiviral Drugs and Characterize Host Immune Cell Responses

    Published on: January 20, 2017

    9.8K
    Use of an Influenza Antigen Microarray to Measure the Breadth of Serum Antibodies Across Virus Subtypes08:52

    Use of an Influenza Antigen Microarray to Measure the Breadth of Serum Antibodies Across Virus Subtypes

    Published on: July 26, 2019

    8.1K

    相关概念视频

    Steps in Outbreak Investigation01:18

    Steps in Outbreak Investigation

    In the ever-evolving field of public health, statistical analysis serves as a cornerstone for understanding and managing disease outbreaks. By leveraging various statistical tools, health professionals can predict potential outbreaks, analyze ongoing situations, and devise effective responses to mitigate impact. For that to happen, there are a few possible stages of the analysis:
    95
    Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

    Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

    Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
    The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
    47
    Prediction Intervals01:03

    Prediction Intervals

    The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
    However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y. 
    2.2K
    Statistical Methods for Analyzing Epidemiological Data01:25

    Statistical Methods for Analyzing Epidemiological Data

    Epidemiological data primarily involves information on specific populations' occurrence, distribution, and determinants of health and diseases. This data is crucial for understanding disease patterns and impacts, aiding public health decision-making and disease prevention strategies. The analysis of epidemiological data employs various statistical methods to interpret health-related data effectively. Here are some commonly used methods:
    239