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Identifying Outliers using Influence Function of Multiple Kernel Canonical Correlation Analysis
[article]
2016
arXiv
pre-print
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Identifying significant outliers is an essential and challenging issue for imaging genetics and multiple sources data analysis. ...
First, we address the influence function (IF) of kernel mean element, kernel covariance operator, kernel cross-covariance operator, kernel canonical correlation analysis (kernel CCA) and multiple kernel ...
Acknowledgments The authors wish to thank the NIH (R01 GM109068, R01 MH104680) and NSF (1539067) for support. ...
arXiv:1606.00113v1
fatcat:ek4wtrkbrjc2zlrupyh4uc2jye
Finding the needle in a high-dimensional haystack: Canonical correlation analysis for neuroscientists
2020
NeuroImage
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Canonical correlation analysis (CCA) is a prototypical family of methods that is useful in identifying the links between variable sets from different modalities. ...
Importantly, CCA is well suited to describing relationships across multiple sets of data and so is well suited to the analysis of big neuroscience datasets. ...
In this context sparse multiple CCA can help describe canonical correlations across three or more domains of variables, making it a good tool for exploring genetics and imaging data. ...
doi:10.1016/j.neuroimage.2020.116745
pmid:32278095
fatcat:7samuqfqwvdvdnhafps3zslmjy
Influence function and robust variant of kernel canonical correlation analysis
2018
Neurocomputing
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Our experiments on both synthesized and imaging genetics data demonstrate that the proposed IF of standard kernel CCA can identify outliers. ...
In addition, while the influence function (IF) of an estimator can characterize its robustness, asymptotic properties and standard error, the IF of a standard kernel canonical correlation analysis (standard ...
Application to imaging genetics data from MCIC and TCGA To demonstrate the application of the proposed methods, we used three data sets: the Mind Clinical Imaging Consortium (MCIC) and two data sets from ...
doi:10.1016/j.neucom.2018.04.008
pmid:30416263
pmcid:PMC6223640
fatcat:744xg6d3afch7dgir4mhlzb47e
An experimental study of employing visual appearance as a phenotype
2008
2008 IEEE Conference on Computer Vision and Pattern Recognition
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Despite the difficulties, we show convincing evidence that the application of correlations between genotype and visual phenotype for identification is feasible with current technologies. ...
To this end, we employ sensitive forcedmatching tests, that can accurately detect correlations between data sets. ...
Figure 4 .Figure 5 . 45 Matching accuracy (% and standard deviation) on all data sets using all algorithms: Regularized Canonical Correlation Analysis (CCA), Kernel Canonical Correlation Analysis (KCCA ...
doi:10.1109/cvpr.2008.4587624
dblp:conf/cvpr/WolfD08
fatcat:qub6jkvhzne65fdbw5y32vpawe
Finding the needle in high-dimensional haystack: A tutorial on canonical correlation analysis
[article]
2018
arXiv
pre-print
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Canonical correlation analysis (CCA) is a prototypical family of methods for wrestling with and harvesting insight from such rich datasets. ...
In the example of neuroscience, studies with thousands of subjects are becoming more common, which provide extensive phenotyping on the behavioral, neural, and genomic level with hundreds of variables. ...
Similar to the advent of microarrays in genetics, brain-imaging and extensive behavioral phenotyping yield datasets with tens of thousands of variables (1). ...
arXiv:1812.02598v1
fatcat:wa2urvsku5fqbdi7bkzjg3mzxi
More Is Better: Recent Progress in Multi-Omics Data Integration Methods
2017
Frontiers in Genetics
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Considerable work has been done with the advent of high-throughput studies, which have enabled the data access for downstream analyses. ...
To improve the clinical outcome prediction, a gamut of software tools has been developed. ...
Correlation
Analysis
L1 penalty
Chen et al., 2013
CCA sparse
group
Unsupervised
Two types of data
Group of features with
weights
Canonical Correlation
Analysis
L1 penalty
Lin et al., ...
doi:10.3389/fgene.2017.00084
pmid:28670325
pmcid:PMC5472696
fatcat:ccdbkwpqufbqjitaxs2kkxcs4y
Influence Function and Robust Variant of Kernel Canonical Correlation Analysis
[article]
2017
arXiv
pre-print
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Our experiments on synthesized data and imaging genetics analysis demonstrate that the proposed IF of standard kernel CCA can identify outliers. ...
In addition, while the influence function (IF) of an estimator can characterize its robustness, asymptotic properties and standard error, the IF of a standard kernel canonical correlation analysis (standard ...
Application to imaging genetics data from MCIC and TCGA To demonstrate the application of the proposed methods, we used three data sets: the Mind Clinical Imaging Consortium (MCIC) and two data sets from ...
arXiv:1705.04194v1
fatcat:sm4kthr5rbhyjnb2twsf4lipfq
A robust kernel machine regression towards biomarker selection in multi-omics datasets of osteoporosis for drug discovery
[article]
2022
arXiv
pre-print
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However, they are sensitive to some deviations in distribution when the observed samples are potentially contaminated with adversarial corrupted outliers (e.g., a fictional data distribution). ...
We address a robust kernel-centered Gram matrix to estimate the model parameters accurately. ...
To that end, we apply different methods (the ttest, canonical correlation analysis based gene shaving (CCAOut), kernel canonical correlation analysis based gene shaving (KCCAOut), and Linear Models for ...
arXiv:2201.05060v1
fatcat:traehcflabe7rjnijb625qhgsa
DROIDS 3.0 - detecting genetic and drug class variant impact on conserved protein binding dynamics
2019
Biophysical Journal
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Local canonical correlations in learning patterns generated from independent, yet identically prepared, MD validation runs are used to identify regions of functionally conserved protein dynamics. ...
We identify regions of conserved dynamics in Hsp90 that connect the ATP binding pocket to other functional regions. ...
Local canonical correlations in the positional performance plots are then used in detecting sequence-encoded functionally conserved dynamics regions, as well as genetic and drug class variant impacts to ...
doi:10.1016/j.bpj.2019.12.008
pmid:31928763
pmcid:PMC7002913
fatcat:4dkx665hubfepl5knv7wgwvhui
Dimensionality reduction-based fusion approaches for imaging and non-imaging biomedical data: concepts, workflow, and use-cases
2017
BMC Medical Imaging
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Results and conclusions: Our preliminary results in these specific use cases indicated that the use of kernel representations in conjunction with DR-based fusion may be most effective, as a weighted multi-kernel-based ...
Our objective in this paper was to help identify metholodological choices that need to be made in order to build a data fusion technique, as it is not always clear which strategy is optimal for a particular ...
as considered within canonical correlation analysis (CCA) [6, 28] or principal component analysis (PCA) [29] . ...
doi:10.1186/s12880-016-0172-6
pmid:28056889
pmcid:PMC5217665
fatcat:zluxbwhzdnerfiadcjfo4vria4
Multiview learning for understanding functional multiomics
2020
PLoS Computational Biology
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Secondly, we explore possible applications to different biological systems, including human diseases (e.g., brain disorders and cancers), plants, and single-cell analysis, and discuss both the benefits ...
and caveats of using multiview learning to discover the molecular mechanisms and functions of these systems. ...
correlation analysis/sparse generalized canonical correlation analysis; rMKL-LPP,
regularized multiple kernel learning-locality preserving projections; rMV-spc, regularized multiview subspace clustering ...
doi:10.1371/journal.pcbi.1007677
pmid:32240163
pmcid:PMC7117667
fatcat:jqpizdutnrgtnlymfmjhh4qo4q
Machine learning based detection of genetic and drug class variant impact on functionally conserved protein binding dynamics
[article]
2019
bioRxiv
pre-print
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Up to seven different machine learners can be deployed on the dynamics of each amino acid, and local canonical correlations in learning patterns generated from self-similar MD runs are used to identify ...
Still and moving images of comparative dynamics allow users to see both when and where a protein dynamic simulation displays a specific functional state defined by the functional protein comparison. ...
and local 38 canonical correlations in learning patterns generated from self-similar MD runs are used to identify 39 regions of functionally conserved protein dynamics. ...
doi:10.1101/724211
fatcat:q2czd62nynh4radct7aut2dzzy
A Review of Statistical Methods in Imaging Genetics
[article]
2018
arXiv
pre-print
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Other Versions
With the rapid growth of modern technology, many large-scale biomedical studies have been/are being/will be conducted to collect massive datasets with large volumes of multi-modality imaging, genetic, ...
However, the development of analytical methods for the joint analysis of both high-dimensional imaging phenotypes and high-dimensional genetic data, called big data squared (BD^2), presents major computational ...
These estimated rates of change are then related to genetic markers using sparse canonical correlation analysis. ...
arXiv:1707.07332v2
fatcat:ykdjy3dxsrdaffcvv2hwfgan4u
A Survey on Multi-View Clustering
[article]
2018
arXiv
pre-print
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Several representative real-world applications are elaborated. To promote future development of MVC, we envision several open problems that may require further investigation and thorough examination. ...
Therefore, this paper reviews the common strategies for combining multiple views of data and based on this summary we propose a novel taxonomy of the MVC approaches. ...
clustering), (5) view combination after projection (mainly canonical correlation analysis (CCA)). ...
arXiv:1712.06246v2
fatcat:w3b2hfnqyzbbbfcz6t3gl5mlny
COMPASS: A computational model to predict changes in MMSE scores 24-months after initial assessment of Alzheimer's disease
2016
Scientific Reports
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For (3), "genetic only" model has Pearson's correlation of 0.15 to predict progression in the MCI group. ...
using standardized data. ...
ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: AbbVie, Alzheimer's Association ...
doi:10.1038/srep34567
pmid:27703197
pmcid:PMC5050516
fatcat:nguewevtkbbsfn4gqxjplgkgaq
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