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- W3100796008 abstract "Abstract Bio-motif detection is one of essential computational tasks for bioinformatics and genomics. Based on a theoretical framework for quantitatively modeling the relationship of convolution kernel shape and the motif detection effectiveness, we design and propose a novel convolution-based model, VCNN (Variable CNN), for effective bio-motif detection via the adaptive kernel length at runtime. Empirical evaluations based on both simulated and real-world genomics data demonstrate VCNN’s superior performance to classical CNN in both detection power and hyper-parameter robustness. All source code and data are available at https://github.com/gao-lab/VCNN/ freely for academic usage." @default.
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- W3100796008 date "2018-12-31" @default.
- W3100796008 modified "2023-09-23" @default.
- W3100796008 title "A new convolution model for effective bio-motif detection via rationally design the black box" @default.
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