An introduction to deep learning on biological sequence data: examples and solutions

By Vanessa Isabell Jurtz, alexander rosenberg johansen, Morten Nielsen, José Juan Almagro Armenteros, Henrik Nielsen, Casper Kaae Sønderby, Ole Winther, Søren Kaae Sønderby. 2017

141 citations recorded by the source corpus.

Subfields: Molecular Biology

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In Molecular Biology

Citations in the corpus

NetSurfP-2.0: improved prediction of protein structural features by integrated deep learning (2018, background): “Since then, deep learning techniques have affected the application of machine learning in biology expanding the ability of prediction tools to produce more accurate results on complex datasets (11-18).”
Development and evaluation of a deep learning model for protein–ligand binding affinity prediction (2017, methodology): “Deep learning has been relatively widely used by the bioinformatics (Leung et al. , 2014; Alipanahi et al. , 2015; Park and Kellis, 2015; Jurtz et al. , 2017; Jim´enez et al. , 2017) and computational biology community (Angermueller et al. , 2016).”
A Review of Deep Learning Methods for Antibodies (2020, background): “Additionally, recurrent neural networks—often used in language processing tasks—can be utilized by framing sequence data as a language with a unique vocabulary [25].”
Machine learning in protein engineering (2018, methodology): “…learning networks are also particularly useful in metabolic pathway optimization (Oyetunde et al., 2018) and genome annotation (Yip et al., 2013; Jurtz et al., 2017; Yue & Wang, 2018), which have been reviewed elsewhere. k -nearest-neighbor (Shakhnarovich et al., 2006) methods make predictions…”
Deep learning in bioinformatics: introduction, application, and perspective in big data era (2019, background): “Notice that there are a number of other review papers available, introducing the applications of deep learning in bioinformatics, biomedicine and healthcare [82, 5, 104, 98, 65, 18, 164, 36, 155], which are summarized in Table 3.”