Unsupervised Feature Construction and Knowledge Extraction from Genome-Wide Assays of Breast Cancer with Denoising Autoencoders

By Jie Tan, Matthew Ung, Chao Cheng, Casey S. Greene. 2014

143 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Molecular Biology, Computational Biology & Bioinformatics, Medical & Health AI

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Citations in the corpus

Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI (2019, neutral): “Informativeness [5, 44, 21, 25, 26, 45, 30, 32, 34, 35, 37, 38, 41, 46, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 63, 64, 65, 66, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 86, 87, 88, 89, 59, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 1”
Semi-Supervised Learning of the Electronic Health Record for Phenotype Stratification (2016, methodology): “Denoising autoencoders (DAs) are a powerful tool to perform unsupervised learning [2].”
Deep Learning based multi-omics integration robustly predicts survival in liver cancer (2017, background): “0 S1 (5) S2 (22) Hawaiian cohort (log-rank P value 0.”
ADAGE-Based Integration of Publicly Available Pseudomonas aeruginosa Gene Expression Data with Denoising Autoencoders Illuminates Microbe-Host Interactions (2016, methodology): “We used the parameters identiﬁed as suitable for a gene expression compendium by Tan et al. (60): a batch size of 10 over 500 training epochs with a corruption level of 0.1 and a learning rate of 0.01.”
"Hello AI": Uncovering the Onboarding Needs of Medical Practitioners for Human-AI Collaborative Decision-Making (2019, background): “Deep neural networks (DNNs) are increasingly being developed for use in medical applications, such as cancer detection and grading [66, 70]).”
Unsupervised extraction of functional gene expression signatures in the bacterial pathogen Pseudomonas aeruginosa with eADAGE (2016, methodology): “This approach has properties that make it suitable for gene expression data (Tan et al , 2015).”
Learning the Structural Vocabulary of a Network (2017, unknown): “…al., 2012), speech (Hinton et al., 2012), genomic (Alipanahi, Delong, Weirauch, & Frey, 2015; Zhou & Troyanskaya, 2015), and gene expression data (Tan, Hammond, Hogan, & Greene, 2016; Tan, Ung, Cheng, & Greene, 2015) that have improved on manually designed features (LeCun, Bengio, & Hinton, 2015).”
A DEEP LEARNING APPROACH FOR CANCER DETECTION AND RELEVANT GENE IDENTIFICATION (2017, background): “16 In similar studies of computer vision, unsupervised deep learning methods have been successfully applied to extract information from high dimensional image data.”
Artificial Intelligence for Clinical Trial Design. (2019, background): “The field of biomedicine has also benefited from this surge in AI methods at many levels, from sophisticated natural language processing (NLP) searches of the biomedical literature [55], to cancer sub-phenotyping using DL [56], to predictions of gene targets of microRNAs [57], drug–target interactio”
Deep learning in biomedicine (2018, methodology): “Autoencoders have also been used in genomics to compress the rich, high-dimensional information contained in gene expression datasets 23 , and in drug discovery to obtain compact representations of drug-like molecules 24 .”
Learning structure in gene expression data using deep architectures, with an application to gene clustering (2015, background): “In a recent work [20], denoising autoencoder is applied to effectively summarize key features in breast cancer data.”