Deep Feature Selection: Theory and Application to Identify Enhancers and Promoters

By Yifeng Li, Chih-Yu Chen, Wyeth W. Wasserman. 2015

263 citations recorded by the source corpus.

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In Molecular Biology

Citations in the corpus

Deep Neural Networks for High Dimension, Low Sample Size Data (2017, background): “However, according to our experiments, DFS fails to achieve sparse connections when facing the HDLSS data.”
A review on machine learning principles for multi-view biological data integration (2016, background): “The sub-networks provide the flexibility of choosing appropriate deep learning models respectively for individual data views, such as deep belief net (DBN) [139] or deep Boltzmann machine (DBM) [140] for binary, Gaussian or count data, convolutional neural network [141] for image data, recurrent neu”
Feature Selection (2016, methodology): “To deal with large-scale unlabeled data in social media, Li et al. (2015) propose the USFS algorithm to tackle unsupervised streaming feature selection.”
Genome-wide prediction of cis-regulatory regions using supervised deep learning methods (2016, methodology): “Feature selection Based on our newly devised deep feature selection (DFS) model [26], we designed the randomized DFS (RDFS), which is a deep extension of randomized LASSO [74], for stably selecting subsets of discriminative features.”
Predicting enhancer-promoter interaction from genomic sequence with deep neural networks (2016, methodology): “One approach may be to apply very recently developed methods such as DeepLIFT [39] and deep feature selection [40] for measuring importance of and selecting among different convolutional features, to identify those that might mediate EPI within or across cell lines.”
Feature selection in machine learning: A new perspective (2018, background): “Features should be accurately selected to save the training time for deep learning methods [168-171].”