Integrative Data Analysis of Multi-Platform Cancer Data with a Multimodal Deep Learning Approach

By Muxuan Liang, Zhizhong Li, Ting Chen, Jianyang Zeng. 2015

220 citations recorded by the source corpus.

Subfields: Cancer Research, Molecular Biology

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Citations in the corpus

Opportunities and obstacles for deep learning in biology and medicine (2017, methodology): “Another application used DBNs for joint unsupervised feature learning from cancer datasets containing gene expression, DNA methylation, and miRNA expression data [148].”
Deep Learning-based Image Segmentation on Multimodal Medical Imaging (2019, unknown): “The unsupervised multimodal deep belief network [27] encoded relationships across data from different modalities with data fusion through a joint latent model.”
A review on machine learning principles for multi-view biological data integration (2016, background): “in bioinformatics to integrate gene expression, DNA methylation and drug response for tumor subtyping [145], showing superior performance to k-mean clustering in terms of clinical discrepancy (survival time).”
Privacy-preserving deep learning (2015, background): “Deep learning has shown promise for analyzing complex biomedical data related to cancer [13, 22, 32] and genetics [15, 56].”
Ensemble deep learning in bioinformatics (2020, methodology): “Ensemble deep learning methods such as multi-model approaches reviewed previously have been demonstrated to be highly effective in combining different omics data for joint inference 89 and classification 90 .”
MOLI: multi-omics late integration with deep neural networks for drug response prediction (2019, background): “…(Dimitrakopoulos et al. , 2018; Mo et al. , 2013; Shrestha et al. , 2017; Singh et al. , 2019), patient stratiﬁcation (Khakabimamaghani and Ester, 2016), survival prediction (Chaudhary et al. , 2018), subgroup discovery (Liang et al. , 2015), and drug response prediction (Ding et al. , 2018).”
Privacy-Preserving Collaborative Deep Learning with Irregular Participants. (2018, background): “…tremendously revolutionized the ﬁelds of machine learning and artiﬁ-cial intelligence, achieving a much better performance than traditional machine learning methods in various applications, e.g., image processing [1], speech recognition [2], [3], cancer analysis [4], [5], and the game of Go [6].”
Deep Learning in Pharmacogenomics: From Gene Regulation to Patient Stratification (2018, methodology): “exibility of architectures, neural networks are capable of multimodal learning, i.e. jointly learning from a number of different datasets and data types without explicit definition of common features [35]. For example, Chaudhary et al. [36] trained a deep autoencoder model jointly on RNA-seq, miRNA-”
Multimodal deep learning for biomedical data fusion: a review (2022, methodology): “DBNs have been extensively used to fuse biomedical modalities for drug repurposing [52], clustering of cancer patients [53] and predicting disease-gene pairs [54].”
Deep Multimodal Learning: A Survey on Recent Advances and Trends (2017, background): “[32] 2015 Gene expression, DNA methylation, and drug response Cancer subtype clustering Intermediate Generative DBM”
Machine Learning and Integrative Analysis of Biomedical Big Data (2019, methodology): “Specifically, in [100], omics data from multiple sources (gene expression, miRNA expression, and DNA methylation) were combined with clinical data to perform integrated clustering based on multimodal deep belief networks (DBN) [101].”