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Recent developments in deep learning have allowed for a leap in computational analysis of epigenomic data, but a fair comparison of different architectures is challenging. Toneyan et al. use GOPHER, their new framework for model evaluation and comparison, to perform a comprehensive analysis, exploring modelling choices of deep learning for epigenomic profiles.
Deep learning methods have in recent years shown promising results in characterizing proteins and extracting complex sequence–structure–function relationships. This Analysis describes a benchmarking study to compare the performances and advantages of recent deep learning approaches in a range of protein prediction tasks.
