The properties of high-dimensional data spaces: implications for exploring gene and protein expression data

By R. Clarke, H. Ressom, Antai Wang, J. Xuan, Minetta C. Liu, E. Gehan, Yue Joseph Wang. Nature Reviews. Cancer, 2008

605 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/374c8ef7124c6d60b313574ceb5562c65e0b79be

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Citations in the corpus

Visualizing High-Dimensional Data: Advances in the Past Decade (2017, neutral): “For example, genomic microarrays in biology [1], [2], spectrometry data in air quality research [3], simulation parameters in nuclear safety engineering [4], and chemical compositions in combustion simulations [5] can all be mapped to high-dimensional spaces (with a few dozen to several hundreds of ”
Identifying protein interaction subnetworks by a bagging Markov random field-based method (2012, background): “Finally, because of the heterogeneity in tissue samples and the inherent noise in microarray data (17), reproducibility of currently identified subnetworks is often low when tested on independent data sets (18,19).”
Methods of integrating data to uncover genotype–phenotype interactions (2015, background): “Furthermore, this form of data integration can inflate high-dimensionality for the data, with the number of samples being smaller than the number of measurements for each sample 58 .”
Promises and challenges for the implementation of computational medical imaging (radiomics) in oncology (2017, background): “:3) n , or the ‘curse-of-dimensionality’ [34]; resulting in a high probability of false positive results [35].”
From big data analysis to personalized medicine for all: challenges and opportunities (2015, background): “Biological complexity presents a challenge in extracting useful information within high-dimensional data [69].”
Metagenomic biomarker discovery and explanation (2011, background): “Related challenges are associated with high-dimensional data regardless of the data type or experimental platform; the number of potential biomarkers, for example, is typically much higher than the number of samples [24-26].”
A survey on unsupervised outlier detection in high‐dimensional numerical data (2012, methodology): “That the relevance of attributes may be related to certain subgroups of the data has been recognized in applications like gene expression analysis [59], a problem setting that also motivated research on specialized methods for subspace clustering [31–33,60].”
Metabolic Reconstruction for Metagenomic Data and Its Application to the Human Microbiome (2012, background): “As has been shown for genotype [58] and gene expression [59] data, phenotype can be difficult to reproducibly associate with high-dimensional genomic or metagenomic fea-Metabolic tures, particularly in large cohorts with complex population structure or when using multivariate models [60].”
Statistical Mechanics of Optimal Convex Inference in High Dimensions (2016, background): “spanning the sciences, engineering, and humanities, including neuroscience [1,2], systems biology [3], health care [4], economics [5], social science [6], and history [7].”
Automatic Posterior Transformation for Likelihood-Free Inference (2019, background): “This scenario is common in neuroscience (Paninski & Cunningham, 2017), physics (Brehmer et al., 2018), systems biology (Clarke et al., 2008) and inverse graphics (Romaszko et al., 2017).”
Searchlight analysis: Promise, pitfalls, and potential (2013, background): “…associated with whole-brain MVPA (the “curse” refers to computational difficulties which can occur when there are more voxels than examples, see (Clarke et al., 2008; Jain et al., 2000); it is minimized in searchlight analysis since relatively few voxels are typically included in each…”