Pattern discovery and cancer gene identification in integrated cancer genomic data

By Q. Mo, Sijian Wang, Venkatraman E. Seshan, Adam B. Olshen, Nikolaus Schultz, Christopher Sander, R. Powers, Marc Ladanyi, Ronglai Shen, P. Bickel. Proceedings of the National Academy of Sciences of the United States of America, 2013

424 citations recorded by the source corpus.

Subfields: Cancer Research, Molecular Biology, Pathology and Forensic Medicine

View Paper: https://www.semanticscholar.org/paper/c0206de6f103232a98eb57203bd3821f43b1f190

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Citations in the corpus

Whole-Genome and Epigenomic Landscapes of Etiologically Distinct Subtypes of Cholangiocarcinoma (2017, methodology): “The “iClusterPlus” R package (76) was used to perform integrative unsupervised clustering of 94 CCAs based on 4 genomic data types: (i) somatic point mutations in 404 targeted genes (gene by sample matrix of binary values), (ii) sCNAs defined as copy-number segments identified by ASCAT v2.”
Cell-of-Origin Patterns Dominate the Molecular Classification of 10,000 Tumors from 33 Types of Cancer (2018, methodology): “Integrative clustering with iCluster—The iCluster clustering algorithm formulates the problem of subgroup discovery as a joint multivariate regression of multiple data types with reference to a set of common latent variables, which represent the underlying 28 tumor subtypes (Mo et al., 2013; Shen et”
MOLI: multi-omics late integration with deep neural networks for drug response prediction (2019, methodology): “Multi-omics data have been exploited for different problems such as driver gene identification (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.”
Multi-omic and multi-view clustering algorithms: review and cancer benchmark (2018, background): “An extension called iCluster+ (64) includes different models for numeric, categorical and count data, but maintains the idea that data originate from a low dimension matrix Z.”
Machine Learning and Integrative Analysis of Biomedical Big Data (2019, methodology): “Recently, iCluster was upgraded to iCluster+ to incorporate diverse data modalities including, binary, categorical and continuous values such that somatic mutation, CNA and gene expression were integrated and distinct tumor sub-groups were identified [75].”
Multi-omics Data Integration, Interpretation, and Its Application (2020, methodology): “39 Further improvement of iClusterPlus method with fully Bayesian model and improved computation time, iCluster-Bayes, 61 has advanced the feature selection criterion and can help in the identification of prominent features from multi-omics data integration.”
Comprehensive molecular characterization of gastric adenocarcinoma (2014, neutral): “Additionally, frequent amplifications of cell cycle mediators (CCNE1, CCND1 and CDK6) suggest the potential for therapeutic inhibition of cyclin-dependent kinases (Supplementary Fig.”
More Is Better: Recent Progress in Multi-Omics Data Integration Methods (2017, background): “H is the loading factor (coeﬃcient), the imposed sparsity with diﬀerent types of penalty functions for various data types. iCluster uses E to represent the error/noise term, and the underlying decomposition equation is: (Mo et al., 2013).”
Comprehensive and Integrative Genomic Characterization of Hepatocellular Carcinoma (2017, positive): “…molecular platforms of HCC, we utilized iCluster, which formulates the problem of subgroup discovery as a joint multivariate regression of multiple data types with reference to a set of common latent variables that represent the underlying tumor subtypes (Mo et al., 2013; Shen et al., 2009).”
Multi‐Omics Factor Analysis—a framework for unsupervised integration of multi‐omics data sets (2018, methodology): “We also compared MOFA to two previously reported latent variable models for multi-omics integration: GFA (Leppa¨aho & Kaski, 2017) and iCluster (Mo et al , 2013).”