6 citations · 18 across the 11 of their papers we have counts for
11 papers
RKUM: An R Package for Robust Kernel Unsupervised Methods
Md Ashad Alam
RKUM is an R package developed for implementing robust kernel-based unsupervised methods. It provides functions for estimating the robust kernel covariance operator (CO) and the ro…
Weighted Scaling Approach for Metabolomics Data Analysis
Biplab Biswas, Nishith Kumar, Md Aminul Hoque +1
Systematic variation is a common issue in metabolomics data analysis. Therefore, different scaling and normalization techniques are used to preprocess the data for metabolomics dat…
A robust kernel machine regression towards biomarker selection in multi-omics datasets of osteoporosis for drug discovery
Md Ashad Alam, Hui Shen, Hong-Wen Deng
Many statistical machine approaches could ultimately highlight novel features of the etiology of complex diseases by analyzing multi-omics data. However, they are sensitive to some…
A generalized kernel machine approach to identify higher-order composite effects in multi-view datasets
Md Ashad Alam, Chuan Qiu, Hui Shen +2
In recent years, a comprehensive study of multi-view datasets (e.g., multi-omics and imaging scans) has been a focus and forefront in biomedical research. State-of-the-art biomedic…
Ecological Data Analysis Based on Machine Learning Algorithms
Md. Siraj-Ud-Doula, Md. Ashad Alam
Classification is an important supervised machine learning method, which is necessary and challenging issue for ecological research. It offers a way to classify a dataset into subs…
Gene Shaving using influence function of a kernel method
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 biolog…
Kernel Method for Detecting Higher Order Interactions in multi-view Data: An Application to Imaging, Genetics, and Epigenetics
Md. Ashad Alam, Hui-Yi Lin, Vince Calhoun +1
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 rele…
Influence Function and Robust Variant of Kernel Canonical Correlation Analysis
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). Both kernel CO and kernel CC…
Learning Schizophrenia Imaging Genetics Data Via Multiple Kernel Canonical Correlation Analysis
Owen Richfield, Md. Ashad Alam, Vince Calhoun +1
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. Kern…
Gene-Gene association for Imaging Genetics Data using Robust Kernel Canonical Correlation Analysis
Md ashad Alam, Osamu Komori, Yu-Ping Wang
In genome-wide interaction studies, to detect gene-gene interactions, most methods are divided into two folds: single nucleotide polymorphisms (SNP) based and gene-based methods. B…
Robust Kernel (Cross-) Covariance Operators in Reproducing Kernel Hilbert Space toward Kernel Methods
Md. Ashad Alam, Kenji Fukumizu, Yu-Ping Wang
To the best of our knowledge, there are no general well-founded robust methods for statistical unsupervised learning. Most of the unsupervised methods explicitly or implicitly depe…