scholarly journals Clustering high-dimensional mixed data to uncover sub-phenotypes: joint analysis of phenotypic and genotypic data

2017 ◽  
Vol 36 (28) ◽  
pp. 4548-4569 ◽  
Author(s):  
D. McParland ◽  
C. M. Phillips ◽  
L. Brennan ◽  
H. M. Roche ◽  
I. C. Gormley
Keyword(s):  
Mixed Data ◽  
Joint Analysis ◽  
High Dimensional

scholarly journals Regularized Latent Class Model for Joint Analysis of High‐Dimensional Longitudinal Biomarkers and a Time‐to‐Event Outcome

Biometrics ◽  
2018 ◽  
Vol 75 (1) ◽  
pp. 69-77
Author(s):  
Jiehuan Sun ◽  
Jose D. Herazo‐Maya ◽  
Philip L. Molyneaux ◽  
Toby M. Maher ◽  
Naftali Kaminski ◽  
...  
Keyword(s):  
Latent Class ◽  
Latent Class Model ◽  
Joint Analysis ◽  
High Dimensional ◽  
Time To Event ◽  
Class Model

scholarly journals Unsupervised Learning for Large Scale Data: The ATHLOS Project

2021 ◽  
Author(s):  
Petros Barmpas ◽  
Sotiris Tasoulis ◽  
Aristidis G. Vrahatis ◽  
Panagiotis Anagnostou ◽  
Spiros Georgakopoulos ◽  
...  
Keyword(s):  
Unsupervised Learning ◽  
Real World ◽  
Large Scale ◽  
High Dimensional Data ◽  
Experimental Studies ◽  
Mixed Data ◽  
Categorical Variables ◽  
High Dimensional ◽  
Data Types ◽  
Unified Framework

1AbstractRecent technological advancements in various domains, such as the biomedical and health, offer a plethora of big data for analysis. Part of this data pool is the experimental studies that record various and several features for each instance. It creates datasets having very high dimensionality with mixed data types, with both numerical and categorical variables. On the other hand, unsupervised learning has shown to be able to assist in high-dimensional data, allowing the discovery of unknown patterns through clustering, visualization, dimensionality reduction, and in some cases, their combination. This work highlights unsupervised learning methodologies for large-scale, high-dimensional data, providing the potential of a unified framework that combines the knowledge retrieved from clustering and visualization. The main purpose is to uncover hidden patterns in a high-dimensional mixed dataset, which we achieve through our application in a complex, real-world dataset. The experimental analysis indicates the existence of notable information exposing the usefulness of the utilized methodological framework for similar high-dimensional and mixed, real-world applications.

scholarly journals Joint analysis of gene expression levels and histological images identifies genes associated with tissue morphology

2018 ◽  
Author(s):  
Jordan T. Ash ◽  
Gregory Darnell ◽  
Daniel Munro ◽  
Barbara E. Engelhardt
Keyword(s):  
Gene Expression ◽  
Image Features ◽  
Population Variation ◽  
Joint Analysis ◽  
High Dimensional ◽  
Specific Cell ◽  
Histological Image ◽  
Expression Levels ◽  
Histological Images ◽  
Gene Expression Levels

Histological images are used to identify and to characterize complex phenotypes such as tumor stage. Our goal is to associate histological image phenotypes with high-dimensional genomic markers; the limitations to incorporating histological image phenotypes in genomic studies are that the relevant image features are difficult to identify and extract in an automated way, and confounders are difficult to control in this high-dimensional setting. In this paper, we use convolutional autoencoders and sparse canonical correlation analysis (CCA) on histological images and gene expression levels from paired samples to find subsets of genes whose expression values in a tissue sample correlate with subsets of morphological features from the corresponding sample image. We apply our approach, ImageCCA, to three data sets, two from TCGA and one from GTEx v6, and we find three types of biological associations. In TCGA, we find gene sets associated with the structure of the extracellular matrix and cell wall infrastructure, implicating uncharacterized genes in extracellular processes. Across studies, we find sets of genes associated with specific cell types, including muscle tissue and neuronal cells, and with cell type proportions in heterogeneous tissues. In the GTEx v6 data, we find image features that capture population variation in thyroid and in colon tissues associated with genetic variants, suggesting that genetic variation regulates population variation in tissue morphological traits. The software is publicly available at: https://github.com/daniel-munro/imageCCA.

scholarly journals Learning Moral Graphs in Construction of High-Dimensional Bayesian Networks for Mixed Data

2019 ◽  
Vol 31 (6) ◽  
pp. 1183-1214 ◽  
Author(s):  
Suwa Xu ◽  
Bochao Jia ◽  
Faming Liang
Keyword(s):  
Computational Complexity ◽  
Bayesian Networks ◽  
Bayesian Network ◽  
Learning Algorithm ◽  
Hill Climbing ◽  
Mixed Data ◽  
High Dimensional ◽  
Large Set ◽  
Worst Case ◽  
Markov Network

Bayesian networks have been widely used in many scientific fields for describing the conditional independence relationships for a large set of random variables. This letter proposes a novel algorithm, the so-called p-learning algorithm, for learning moral graphs for high-dimensional Bayesian networks. The moral graph is a Markov network representation of the Bayesian network and also the key to construction of the Bayesian network for constraint-based algorithms. The consistency of the p-learning algorithm is justified under the small- n, large- p scenario. The numerical results indicate that the p-learning algorithm significantly outperforms the existing ones, such as the PC, grow-shrink, incremental association, semi-interleaved hiton, hill-climbing, and max-min hill-climbing. Under the sparsity assumption, the p-learning algorithm has a computational complexity of O(p2) even in the worst case, while the existing algorithms have a computational complexity of O(p3) in the worst case.

scholarly journals Raking and Relabeling for Imbalanced Data

2022 ◽  
Author(s):  
Seunghwan Park ◽  
Hae-Wwan Lee ◽  
Jongho Im
Keyword(s):  
High Dimensional Data ◽  
Imbalanced Data ◽  
Sampling Strategy ◽  
Classification Performance ◽  
Mixed Data ◽  
Categorical Variables ◽  
High Dimensional ◽  
Data Generation ◽  
Minority Class ◽  
Imbalanced Data Classification

<div>We consider the binary classification of imbalanced data. A dataset is imbalanced if the proportion of classes are heavily skewed. Imbalanced data classification is often challengeable, especially for high-dimensional data, because unequal classes deteriorate classifier performance. Under sampling the majority class or oversampling the minority class are popular methods to construct balanced samples, facilitating classification performance improvement. However, many existing sampling methods cannot be easily extended to high-dimensional data and mixed data, including categorical variables, because they often require approximating the attribute distributions, which becomes another critical issue. In this paper, we propose a new sampling strategy employing raking and relabeling procedures, such that the attribute values of the majority class are imputed for the values of the minority class in the construction of balanced samples. The proposed algorithms produce comparable performance as existing popular methods but are more flexible regarding the data shape and attribute size. The sampling algorithm is attractive in practice, considering that it does not require density estimation for synthetic data generation in oversampling and is not bothered by mixed-type variables. In addition, the proposed sampling strategy is robust to classifiers in the sense that classification performance is not sensitive to choosing the classifiers.</div>

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