A review on machine learning principles for multi-view biological data integration

By Yifeng Li, Fang-Xiang Wu, Alioune Ngom. Briefings Bioinform., 2016

411 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Genome-wide prediction of cis-regulatory regions using supervised deep learning methods (2016, methodology): “We expect that further development of integrative machine learning methods [63, 64] is crucial to reconstruct such a gene regulatory system.”
MOGONET integrates multi-omics data using graph convolutional networks allowing patient classification and biomarker identification (2021, methodology): “Existing methods utilizing multi-view data for biomedical classi ﬁ cation tasks either directly concatenate features from different views or learn to fuse data by learning the weight of each view or fusing features from different views in a low-level feature space 4,63 – 65 .”
Machine Learning and Integrative Analysis of Biomedical Big Data (2019, background): “However, multi-omics data analysis and hypothesis generation may involve more than two classes [66], with a varying degree of imbalance among them [172].”
Multimodal deep learning for biomedical data fusion: a review (2022, background): “In recent years, multimodal ML methods have been increasingly studied and applied in a variety of fields [6, 11].”
Multi-omic and multi-view clustering algorithms: review and cancer benchmark (2018, background): “[10], [11] and [12] review methods for multiomics integration, and [13] review multi-omics clustering for cancer application.”