AIMC Topic: Dengue Virus
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Machine learning and dengue forecasting: Comparing random forests and artificial neural networks for predicting dengue burden at national and sub-national scales in Colombia.
PLoS neglected tropical diseases
Sep 24, 2020
The robust estimate and forecast capability of random forests (RF) has been widely recognized, however this ensemble machine learning method has not been widely used in mosquito-borne disease forecasting. In this study, two sets of RF models were dev...
Target Identification Using Homopharma and Network-Based Methods for Predicting Compounds Against Dengue Virus-Infected Cells.
Molecules (Basel, Switzerland)
Apr 18, 2020
Drug target prediction is an important method for drug discovery and design, can disclose the potential inhibitory effect of active compounds, and is particularly relevant to many diseases that have the potential to kill, such as dengue, but lack any...
Undersampling: case studies of flaviviral inhibitory activities.
Journal of computer-aided molecular design
Nov 26, 2019
Imbalanced datasets, comprising of more inactive compounds relative to the active ones, are a common challenge in ligand-based model building workflows for drug discovery. This is particularly true for neglected tropical diseases since efforts to ide...
ML-DSP: Machine Learning with Digital Signal Processing for ultrafast, accurate, and scalable genome classification at all taxonomic levels.
BMC genomics
Apr 3, 2019
BACKGROUND: Although software tools abound for the comparison, analysis, identification, and classification of genomic sequences, taxonomic classification remains challenging due to the magnitude of the datasets and the intrinsic problems associated ...
Morbidity Rate Prediction of Dengue Hemorrhagic Fever (DHF) Using the Support Vector Machine and the Aedes aegypti Infection Rate in Similar Climates and Geographical Areas.
PloS one
May 11, 2015
BACKGROUND: In the past few decades, several researchers have proposed highly accurate prediction models that have typically relied on climate parameters. However, climate factors can be unreliable and can lower the effectiveness of prediction when t...
Multivariate forecasting of dengue infection in Bangladesh: evaluating the influence of data downscaling on machine learning predictive accuracy.
BMC infectious diseases
May 27, 2025
The increasing incidence of dengue virus (DENV) infections poses significant public health challenges in Bangladesh, demanding advanced forecasting methodologies to guide timely interventions. This study introduces a rigorous multivariate time series...
Dual level dengue diagnosis using lightweight multilayer perceptron with XAI in fog computing environment and rule based inference.
Scientific reports
May 13, 2025
Over the last fifty years, arboviral infections have made an unparalleled contribution to worldwide disability and morbidity. Globalization, population growth, and unplanned urbanization are the main causes. Dengue is regarded as the most significant...
m6A-Atlas: a comprehensive knowledgebase for unraveling the N6-methyladenosine (m6A) epitranscriptome.
Nucleic acids research
Jan 8, 2021
N 6-Methyladenosine (m6A) is the most prevalent RNA modification on mRNAs and lncRNAs. It plays a pivotal role during various biological processes and disease pathogenesis. We present here a comprehensive knowledgebase, m6A-Atlas, for unraveling the ...
Artificial Neural Network Analysis of Pharmacokinetic and Toxicity Properties of Lead Molecules for Dengue Fever, Tuberculosis and Malaria.
Current computer-aided drug design
Jan 1, 2016
Poor pharmacokinetic and toxicity profiles are major reasons for the low rate of advancing lead drug candidates into efficacy studies. The In-silico prediction of primary pharmacokinetic and toxicity properties in the drug discovery and development p...
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