Similarity network fusion for aggregating data types on a genomic scale

By Bo Wang, Aziz M. Mezlini, Feyyaz Demir, Marc Fiume, Zhuowen Tu, Michael Brudno, Benjamin Haibe-Kains, Anna Goldenberg. Nature Methods, 2014

1,802 citations recorded by the source corpus.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/bc5071d839c7f06d4cb5af9df9f116595b38755a

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Citations in the corpus

Deep Learning based multi-omics integration robustly predicts survival in liver cancer (2017, methodology): “In dealing with the missing values (preprocessing of data), three steps were performed as elsewhere (29).”
Graph Learning for Multiview Clustering (2018, neutral): “SC, CRSC, RMSC, SNF, MVSC, and MKKM need to perform k-means after they obtain the new representation of data points, so the metrics are different in each experiment because of the k-means clustering processing.”
More Is Better: Recent Progress in Multi-Omics Data Integration Methods (2017, methodology): “Since this approach requires high computational complexity, it is necessary to preselect the features critical for clustering results (Wang et al., 2014; Speicher and Pfeifer, 2015).”
Drug Similarity Integration Through Attentive Multi-view Graph Auto-Encoders (2018, methodology): “The unsupervised methods For example, in [Wang et al., 2014] and [Angione et al.”
Machine Learning and Integrative Analysis of Biomedical Big Data (2019, background): “Similarity network fusion (SNF) framework aggregated mRNA expression, DNA methylation and miRNA expression data for cancer patients, and used networks as a basis for integration [29].”
Content-Based High-Resolution Remote Sensing Image Retrieval via Unsupervised Feature Learning and Collaborative Affinity Metric Fusion (2016, methodology): “Afterwards, collaborative affinity metric fusion is utilized in genome-wide data aggregation [28] and multi-cue fusion for salient object detection [29].”
DIABLO: an integrative approach for identifying key molecular drivers from multi-omics assays (2019, neutral): “Colon 92 mRNA 17 814 Wang et al. (2014) High: 33 miRNA 312 Low: 59 CpGs 23 088”
Precision Oncology: The Road Ahead (2017, methodology): “Some methods search for a “consensus” after clustering patients by each platform separately [37], or cluster with protein-protein interactions [29], or patient similarity networks [80, 81].”
Chromatin Potential Identified by Shared Single-Cell Profiling of RNA and Chromatin (2020, methodology): “To jointly cluster on ATAC and RNA signals, we used Similarity NEtwork FUSION (Wang et al., 2014) to combine the distance matrix in chromatin space and RNA space.”
Deep learning for healthcare: review, opportunities and challenges (2018, neutral): “To this aim, previous works tried to link multiple data sources to build joint knowledge bases that could be used for predictive analysis and discovery [4–6].”
A review on machine learning principles for multi-view biological data integration (2016, methodology): “As a typical example of this principle, SNF [76] integrates mRNA expression, DNA methylation and miRNA of cohort cancer patients for tumor subtyping and survival prediction.”