research
∙
08/01/2022
Weighted Scaling Approach for Metabolomics Data Analysis
Systematic variation is a common issue in metabolomics data analysis. Th...
share
research
∙
01/13/2022
A robust kernel machine regression towards biomarker selection in multi-omics datasets of osteoporosis for drug discovery
Many statistical machine approaches could ultimately highlight novel fea...
research
∙
04/29/2020
A generalized kernel machine approach to identify higher-order composite effects in multi-view datasets
In recent years, a comprehensive study of multi-view datasets (e.g., mul...
research
∙
12/21/2018
Ecological Data Analysis Based on Machine Learning Algorithms
Classification is an important supervised machine learning method, which...
research
∙
09/05/2018
Gene Shaving using influence function of a kernel method
Identifying significant subsets of the genes, gene shaving is an essenti...
research
∙
07/14/2017
Kernel Method for Detecting Higher Order Interactions in multi-view Data: An Application to Imaging, Genetics, and Epigenetics
In this study, we tested the interaction effect of multimodal datasets u...
research
∙
05/09/2017
Influence Function and Robust Variant of Kernel Canonical Correlation Analysis
Many unsupervised kernel methods rely on the estimation of the kernel co...
research
∙
09/15/2016
Learning Schizophrenia Imaging Genetics Data Via Multiple Kernel Canonical Correlation Analysis
Kernel and Multiple Kernel Canonical Correlation Analysis (CCA) are empl...
research
∙
06/01/2016
Gene-Gene association for Imaging Genetics Data using Robust Kernel Canonical Correlation Analysis
In genome-wide interaction studies, to detect gene-gene interactions, mo...
research
∙
06/01/2016
Identifying Outliers using Influence Function of Multiple Kernel Canonical Correlation Analysis
Imaging genetic research has essentially focused on discovering unique a...
research
∙
02/17/2016
