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Identifying Outliers using Influence Function of Multiple Kernel Canonical Correlation Analysis [article]

Md Ashad Alam, Yu-Ping Wang
2016 arXiv   pre-print
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

Hao-Ting Wang, Jonathan Smallwood, Janaina Mourao-Miranda, Cedric Huchuan Xia, Theodore D. Satterthwaite, Danielle S. Bassett, Danilo Bzdok
2020 NeuroImage  
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

Md. Ashad Alam, Kenji Fukumizu, Yu-Ping Wang
2018 Neurocomputing  
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

Lior Wolf, Yoni Donner
2008 2008 IEEE Conference on Computer Vision and Pattern Recognition  
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]

Hao-Ting Wang, Jonathan Smallwood, Janaina Mourao-Miranda, Cedric Huchuan Xia, Theodore D. Satterthwaite, Danielle S. Bassett, Danilo Bzdok
2018 arXiv   pre-print
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

Sijia Huang, Kumardeep Chaudhary, Lana X. Garmire
2017 Frontiers in Genetics  
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]

Md. Ashad Alam, Kenji Fukumizu, Yu-Ping Wang
2017 arXiv   pre-print
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]

Md Ashad Alam and Hui Shen and Hong-Wen Deng
2022 arXiv   pre-print
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

Gregory A. Babbitt, Ernest P. Fokoue, Joshua R. Evans, Kyle I. Diller, Lily E. Adams
2019 Biophysical Journal  
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

Satish E. Viswanath, Pallavi Tiwari, George Lee, Anant Madabhushi
2017 BMC Medical Imaging  
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

Nam D Nguyen, Daifeng Wang
2020 PLoS Computational Biology  
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]

Gregory A Babbitt, Ernest P Fokoue, Joshua R Evans, Kyle I Diller, Lily E Adams
2019 bioRxiv   pre-print
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]

Farouk S. Nathoo, Linglong Kong, Hongtu Zhu
2018 arXiv   pre-print
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]

Guoqing Chao, Shiliang Sun, Jinbo Bi
2018 arXiv   pre-print
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

Fan Zhu, Bharat Panwar, Hiroko H. Dodge, Hongdong Li, Benjamin M. Hampstead, Roger L. Albin, Henry L. Paulson, Yuanfang Guan
2016 Scientific Reports  
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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