DPGR framework infers clade-level relative transmission fitness from GISAID influenza data and CNN models predict DPGR from full viral genomes with R² above 0.95.
, author Torgo, L
2 Pith papers cite this work. Polarity classification is still indexing.
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2026 2verdicts
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A hybrid framework uses adaptive bin partitioning, CVAE, multistage oversampling, LDWL loss, and gated fusion to improve performance on imbalanced regression benchmarks.
citing papers explorer
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Inferring and Predicting Clade-Level Relative Transmission Fitness in Seasonal Influenza A Using Differential Population Growth Rate and Deep Learning
DPGR framework infers clade-level relative transmission fitness from GISAID influenza data and CNN models predict DPGR from full viral genomes with R² above 0.95.
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Hybrid Imbalanced Regression Through Unified Data-Level and Algorithm-Level Balancing
A hybrid framework uses adaptive bin partitioning, CVAE, multistage oversampling, LDWL loss, and gated fusion to improve performance on imbalanced regression benchmarks.