The paper introduces DeepBind, a deep learning-based method for predicting sequence specificities of DNA- and RNA-binding proteins. DeepBind is designed to handle diverse experimental data, including microarrays, sequencing, and high-throughput assays, and it outperforms other state-of-the-art methods in terms of accuracy and scalability. The method uses deep convolutional neural networks to discover new patterns and combine them into predictive binding scores. DeepBind can be trained on large datasets and automatically calibrates parameters to avoid overfitting. The trained models can be used to identify binding sites in new sequences and to analyze the effects of mutations on binding affinity. The authors demonstrate the effectiveness of DeepBind by evaluating it on various datasets, including transcription factor binding data from the DREAM5 challenge and RNA binding data from the RNAcompete system. They also show that DeepBind can accurately predict in vivo binding data and identify damaging genetic variants that affect transcription factor binding. The results highlight the potential of deep learning in understanding regulatory processes in biological systems and in identifying disease-causing variants.The paper introduces DeepBind, a deep learning-based method for predicting sequence specificities of DNA- and RNA-binding proteins. DeepBind is designed to handle diverse experimental data, including microarrays, sequencing, and high-throughput assays, and it outperforms other state-of-the-art methods in terms of accuracy and scalability. The method uses deep convolutional neural networks to discover new patterns and combine them into predictive binding scores. DeepBind can be trained on large datasets and automatically calibrates parameters to avoid overfitting. The trained models can be used to identify binding sites in new sequences and to analyze the effects of mutations on binding affinity. The authors demonstrate the effectiveness of DeepBind by evaluating it on various datasets, including transcription factor binding data from the DREAM5 challenge and RNA binding data from the RNAcompete system. They also show that DeepBind can accurately predict in vivo binding data and identify damaging genetic variants that affect transcription factor binding. The results highlight the potential of deep learning in understanding regulatory processes in biological systems and in identifying disease-causing variants.