no code implementations • 29 Apr 2020 • Md. Ashad Alam, Chuan Qiu, Hui Shen, Yu-Ping Wang, Hong-Wen Deng
In this paper, we propose a novel generalized kernel machine approach to identify higher-order composite effects in multi-view biomedical datasets.
no code implementations • 21 Dec 2018 • Md. Siraj-Ud-Doula, Md. Ashad Alam
In our study we conclude that Linear Discriminant Analysis and k-nearest neighbors are the best methods among all other methods
no code implementations • 5 Sep 2018 • Md. Ashad Alam, Mohammad Shahjama, Md. Ferdush Rahman
Identifying significant subsets of the genes, gene shaving is an essential and challenging issue for biomedical research for a huge number of genes and the complex nature of biological networks,.
no code implementations • 14 Jul 2017 • Md. Ashad Alam, Hui-Yi Lin, Vince Calhoun, Yu-Ping Wang
In this study, we tested the interaction effect of multimodal datasets using a novel method called the kernel method for detecting higher order interactions among biologically relevant mulit-view data.
no code implementations • 9 May 2017 • Md. Ashad Alam, Kenji Fukumizu, Yu-Ping Wang
Many unsupervised kernel methods rely on the estimation of the kernel covariance operator (kernel CO) or kernel cross-covariance operator (kernel CCO).
no code implementations • 15 Sep 2016 • Owen Richfield, Md. Ashad Alam, Vince Calhoun, Yu-Ping Wang
Kernel and Multiple Kernel Canonical Correlation Analysis (CCA) are employed to classify schizophrenic and healthy patients based on their SNPs, DNA Methylation and fMRI data.
no code implementations • 1 Jun 2016 • Md. Ashad Alam, Yu-Ping Wang
Second, we propose an IF of multiple kernel CCA, which can be applied for more than two datasets.
no code implementations • 1 Jun 2016 • Md. Ashad Alam, Osamu Komori, Yu-Ping Wang
Third, we propose a nonparametric robust KCCU method based on robust kernel CCA, which is designed for contaminated data and less sensitive to noise than classical kernel CCA.
no code implementations • 17 Feb 2016 • Md. Ashad Alam, Kenji Fukumizu, Yu-Ping Wang
Finally, we propose a method based on robust kernel CO and robust kernel CCO, called robust kernel CCA, which is designed for contaminated data and less sensitive to noise than classical kernel CCA.