Deep autoencoder neural networks for gene ontology annotation predictions

By Davide Chicco, Peter Sadowski, Pierre Baldi. 2014

196 citations recorded by the source corpus.

Subfields: Molecular Biology, Computational Biology & Bioinformatics

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Citations in the corpus

Differential Privacy Preservation for Deep Auto-Encoders: an Application of Human Behavior Prediction (2016, background): “…success in recent years, the applications of deep learning are being expanded into other fields such as social media (Yuan et al. 2013), social network analysis (Perozzi, Al-Rfou, and Skiena 2014), bioinformatics (Chicco, Sadowski, and Baldi 2014), medicine and healthcare (Song et al. 2014).”
DeepFool: A Simple and Accurate Method to Fool Deep Neural Networks (2015, background): “However, these same architectures have been shown to be unstable to small, well sought, perturbations of the images.”
A practical tutorial on autoencoders for nonlinear feature fusion: Taxonomy, models, software and guidelines (2018, positive): “In [41] this classical approach is compared with the use of deep AEs, reaching as conclusion that the latter is able to improve the results.”
Ten quick tips for machine learning in computational biology (2017, background): “2) [26], the number k of clusters in k -means clustering [22], the number of topics (classes) in topic modeling [24], and the dimensions of an artificial neural network (number of hidden layers and number of hidden units) [34].”
Deep Learning with Convolutional Neural Networks Applied to Electromyography Data: A Resource for the Classification of Movements for Prosthetic Hands (2016, methodology): “Deep learning methods are also successfully applied to applications requiring to process big amounts of data, such as drug discovery (Ramsundar et al., 2015), compound activity prediction (Dahl et al., 2014) and genomic information annotation (Chicco et al., 2014).”
The History Began from AlexNet: A Comprehensive Survey on Deep Learning Approaches (2018, methodology): “AE is applied in Bio-informatics [102,171] and cyber security [172].”
Principles and Practice of Explainable Machine Learning (2020, background): “• Multi-layer Neural Networks (NNs) are a class of models that have been used extensively in a number of applications, ranging from bioinformatics [15] to recommendation systems [85], due to their state-of-the-art performance.”
Convolutional Neural Networks (2017, unknown): “…2015; Simonyan and Zisserman, 2015), automatic speech recognition (Hinton et al., 2012; Hannun et al., 2014), natural language processing (Collobert et al., 2011; Mikolov et al., 2013; Cho et al., 2014), bioinformatics (Chicco et al., 2014), even high energy particle physics (Baldi et al., 2014).”