{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/comprehensive-evaluation-of-deep-learning","title":"Comprehensive Evaluation of Deep Learning Architectures for Prediction of DNA/RNA Sequence Binding Specificities","arxiv_id":"1901.10526","date":"2019-01-29","proceeding":null,"authors":["Ameni Trabelsi","Mohamed Chaabane","Asa Ben Hur"],"abstract":"Motivation: Deep learning architectures have recently demonstrated their\npower in predicting DNA- and RNA-binding specificities. Existing methods fall\ninto three classes: Some are based on Convolutional Neural Networks (CNNs),\nothers use Recurrent Neural Networks (RNNs), and others rely on hybrid\narchitectures combining CNNs and RNNs. However, based on existing studies it is\nstill unclear which deep learning architecture is achieving the best\nperformance. Thus an in-depth analysis and evaluation of the different methods\nis needed to fully evaluate their relative. Results: In this study, We present\na systematic exploration of various deep learning architectures for predicting\nDNA- and RNA-binding specificities. For this purpose, we present deepRAM, an\nend-to-end deep learning tool that provides an implementation of novel and\npreviously proposed architectures; its fully automatic model selection\nprocedure allows us to perform a fair and unbiased comparison of deep learning\narchitectures. We find that an architecture that uses k-mer embedding to\nrepresent the sequence, a convolutional layer and a recurrent layer,\noutperforms all other methods in terms of model accuracy. Our work provides\nguidelines that will assist the practitioner in choosing the best architecture\nfor the task at hand, and provides some insights on the differences between the\nmodels learned by convolutional and recurrent networks. In particular, we find\nthat although recurrent networks improve model accuracy, this comes at the\nexpense of a loss in the interpretability of the features learned by the model.\nAvailability and implementation: The source code for deepRAM is available at\nhttps://github.com/MedChaabane/deepRAM","url_abs":"http://arxiv.org/abs/1901.10526v1","url_pdf":"http://arxiv.org/pdf/1901.10526v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"comprehensive-evaluation-of-deep-learning","repo_url":"https://github.com/MedChaabane/deepRAM","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null},{"paper_slug":"comprehensive-evaluation-of-deep-learning","repo_url":"https://github.com/iwasakishuto/DeepScreening","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"automatic-machine-learning-model-selection","task_name":"Automatic Machine Learning Model Selection"},{"task_slug":"deep-learning","task_name":"Deep Learning"},{"task_slug":"model-selection","task_name":"Model Selection"},{"task_slug":"multi-label-text-classification","task_name":"Multi-Label Text Classification"},{"task_slug":"text-classification","task_name":"Text Classification"}],"methods":[{"method_slug":"interpretability","method_name":"Interpretability"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1901.10526","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1901.10526"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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