Interaction screening for high‐dimensional heterogeneous data via robust hybrid metrics

2021 ◽  
Author(s):  
Wei Xiong ◽  
Han Pan
Keyword(s):  
Heterogeneous Data ◽  
High Dimensional ◽  
Interaction Screening

scholarly journals Bayesian inference for finite mixtures of univariate and multivariate skew-normal and skew-t distributions

Biostatistics ◽  
2010 ◽  
Vol 11 (2) ◽  
pp. 317-336 ◽  
Author(s):  
Sylvia Frühwirth-Schnatter ◽  
Saumyadipta Pyne
Keyword(s):  
Bayesian Inference ◽  
Mixture Models ◽  
Random Effects ◽  
Data Augmentation ◽  
Heterogeneous Data ◽  
High Dimensional ◽  
Finite Mixtures ◽  
Truncated Normal ◽  
T Distribution ◽  
Skew Normal

Abstract Skew-normal and skew-t distributions have proved to be useful for capturing skewness and kurtosis in data directly without transformation. Recently, finite mixtures of such distributions have been considered as a more general tool for handling heterogeneous data involving asymmetric behaviors across subpopulations. We consider such mixture models for both univariate as well as multivariate data. This allows robust modeling of high-dimensional multimodal and asymmetric data generated by popular biotechnological platforms such as flow cytometry. We develop Bayesian inference based on data augmentation and Markov chain Monte Carlo (MCMC) sampling. In addition to the latent allocations, data augmentation is based on a stochastic representation of the skew-normal distribution in terms of a random-effects model with truncated normal random effects. For finite mixtures of skew normals, this leads to a Gibbs sampling scheme that draws from standard densities only. This MCMC scheme is extended to mixtures of skew-t distributions based on representing the skew-t distribution as a scale mixture of skew normals. As an important application of our new method, we demonstrate how it provides a new computational framework for automated analysis of high-dimensional flow cytometric data. Using multivariate skew-normal and skew-t mixture models, we could model non-Gaussian cell populations rigorously and directly without transformation or projection to lower dimensions.

scholarly journals Deep learning in systems medicine

2020 ◽  
Author(s):  
Haiying Wang ◽  
Estelle Pujos-Guillot ◽  
Blandine Comte ◽  
Joao Luis de Miranda ◽  
Vojtech Spiwok ◽  
...  
Keyword(s):  
Deep Learning ◽  
Precision Medicine ◽  
Human Body ◽  
Review Paper ◽  
Heterogeneous Data ◽  
Clinical Impact ◽  
High Dimensional ◽  
Great Promise ◽  
Systems Medicine ◽  
Prevention And Treatment

Abstract Systems medicine (SM) has emerged as a powerful tool for studying the human body at the systems level with the aim of improving our understanding, prevention and treatment of complex diseases. Being able to automatically extract relevant features needed for a given task from high-dimensional, heterogeneous data, deep learning (DL) holds great promise in this endeavour. This review paper addresses the main developments of DL algorithms and a set of general topics where DL is decisive, namely, within the SM landscape. It discusses how DL can be applied to SM with an emphasis on the applications to predictive, preventive and precision medicine. Several key challenges have been highlighted including delivering clinical impact and improving interpretability. We used some prototypical examples to highlight the relevance and significance of the adoption of DL in SM, one of them is involving the creation of a model for personalized Parkinson’s disease. The review offers valuable insights and informs the research in DL and SM.

scholarly journals Prediction of Cocaine Inpatient Treatment Success Using Machine Learning on High-Dimensional Heterogeneous Data

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 218936-218953
Author(s):  
Jose Tapia-Galisteo ◽  
Jose M. Iniesta ◽  
Carmen Perez-Gandia ◽  
Gema Garcia-Saez ◽  
Diego Urgeles Puertolas ◽  
...  
Keyword(s):  
Machine Learning ◽  
Inpatient Treatment ◽  
Treatment Success ◽  
Heterogeneous Data ◽  
High Dimensional

scholarly journals Innovated interaction screening for high-dimensional nonlinear classification

2015 ◽  
Vol 43 (3) ◽  
pp. 1243-1272 ◽  
Author(s):  
Yingying Fan ◽  
Yinfei Kong ◽  
Daoji Li ◽  
Zemin Zheng
Keyword(s):  
High Dimensional ◽  
Nonlinear Classification ◽  
Interaction Screening

scholarly journals A two-stage approach of gene network analysis for high-dimensional heterogeneous data

Biostatistics ◽  
2017 ◽  
Vol 19 (2) ◽  
pp. 216-232 ◽  
Author(s):  
Sangin Lee ◽  
Faming Liang ◽  
Ling Cai ◽  
Guanghua Xiao
Keyword(s):  
Network Analysis ◽  
Gene Network ◽  
Heterogeneous Data ◽  
High Dimensional ◽  
Two Stage ◽  
Gene Network Analysis

scholarly journals Opening the Black Box: Interpretable Machine Learning for Geneticists

2020 ◽  
Author(s):  
Christina B. Azodi ◽  
Jiliang Tang ◽  
Shin-Han Shiu
Keyword(s):  
Machine Learning ◽  
Genomic Data ◽  
Heterogeneous Data ◽  
Black Box ◽  
High Dimensional ◽  
Future Directions ◽  
Making Sense ◽  
Interpretable Machine Learning ◽  
Genetics And Genomics ◽  
Complex Patterns

Machine learning (ML) has emerged as a critical tool for making sense of the growing amount of genetic and genomic data available because of its ability to find complex patterns in high dimensional and heterogeneous data. While the complexity of ML models is what makes them powerful, it also makes them difficult to interpret. Fortunately, recent efforts to develop approaches that make the inner workings of ML models understandable to humans have improved our ability to make novel biological insights using ML. Here we discuss the importance of interpretable ML, different strategies for interpreting ML models, and examples of how these strategies have been applied. Finally, we identify challenges and promising future directions for interpretable ML in genetics and genomics.

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