About
49
Publications
13,021
Reads
How we measure 'reads'
A 'read' is counted each time someone views a publication summary (such as the title, abstract, and list of authors), clicks on a figure, or views or downloads the full-text. Learn more
353
Citations
Citations since 2017
Introduction
My research interest includes are in theoretical and computational aspects of statistical machine learning, robust statistics, and topological data analysis for multi-view data integration using kernel based methods.
Statistical machine learning: dimensionality reduction, feature selection, non-parametric models, and inference methods.
Deep learning: The advances in deep learning technologies provide new effective paradigms to obtain end-to-end learning models from complex, high-dimensional and heterogeneous biomedical data. Deep learning approaches could be vehicle for translating big biomedical data into improved human health.
Robust statistics: robustness of unsupervised kernel methods, non-parametric inference and robustness of linear multivariate approaches.
Additional affiliations
April 2019 - February 2020
June 2018 - present
November 2015 - September 2017
Publications
Publications (49)
Imaging genetic research has essentially focused on discovering unique and co-association effects, but typically ignoring to identify outliers or atypical objects in genetic as well as non-genetics variables. Identifying significant outliers is an essential and challenging issue for imaging genetics and multiple sources data analysis. Therefore, we...
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 CCO are sensitive to contaminated data, even when bounded positive definite kernels are used. To the best of our knowledge, there are few well-founded robust kernel methods...
Background:
Technological advances are enabling us to collect multimodal datasets at an increasing depth and resolution while with decreasing labors. Understanding complex interactions among multimodal datasets, however, is challenging.
New method:
In this study, we tested the interaction effect of multimodal datasets using a novel method called...
It is well known that the performance of kernel methods depends on the choice of appropriate kernels and associated parameters. While cross-validation (CV) is a useful method of kernel and parameter choice for supervised learning such as the support vector machines, there are no general well-founded methods for unsupervised kernel methods. This pap...
Background
Gene shaving (GS) is an essential and challenging tools for biomedical researchers due to the large number of genes in human genome and the complex nature of biological networks. Most GS methods are not applicable to non-linear and multi-view data sets. While the kernel based methods can overcome these problems, a well-founded positive d...
Introduction: Systematic variation is a common issue in metabolomics data analysis. Therefore, different scaling and normalization techniques are used to preprocess the data for metabolomics data analysis. Although several scaling methods are available in the literature, however, choice of scaling, transformation and/or normalization technique infl...
Systematic variation is a common issue in metabolomics data analysis. Therefore, different scaling and normalization techniques are used to preprocess the data for metabolomics data analysis. Although several scaling methods are available in the literature, however, choice of scaling, transformation and/or normalization technique influence the furt...
As one of the most prevalent post-transcriptional epigenetic modifications, N5-methylcytosine (m5C), plays an essential role in various cellular processes and disease pathogenesis. Therefore, it is important accurately identify m5C modifications in order to gain a deeper understanding of cellular processes and other possible functional mechanisms....
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 deviations in distribution when the observed samples are potentially contaminated with adversarial corrupted outliers (e.g., a fictional data distribution). Likewise, st...
MicroRNAs (miRNAs) are central players that regulate the post-transcriptional processes of gene expression. Binding of miRNAs to target mRNAs can repress their translation by inducing the degradation or by inhibiting the translation of the target mRNAs. High-throughput experimental approaches for miRNA target identification are costly and time-cons...
In recent years, a comprehensive study of complex disease with multi-view datasets (e.g., multi-omics and imaging scans) has been a focus and forefront in biomedical research. State-of-the-art biomedical technologies are enabling us to collect multi-view biomedical datasets for the study of complex diseases. While all the views of data tend to expl...
Neuropeptides (NPs) are the most versatile neurotransmitters in the immune systems that regulate AQ6 various central anxious hormones. An efficient and effective bioinformatics tool for rapid and accurate large-scale identification of NPs is critical in immunoinformatics, which is indispensable for basic research and drug development. Although a fe...
Redox-sensitive cysteine (RSC) thiol contributes to many biological processes. The identification of RSC plays an important role in clarifying some mechanisms of redox-sensitive factors; nonetheless, experimental investigation of RSCs is expensive and time-consuming. The computational approaches that quickly and accurately identify candidate RSCs u...
Pupylation is a type of reversible post-translational modification of proteins, which plays a key role in the cellular function of microbial organisms. Several proteomics methods have been developed for the prediction and analysis of pupylated proteins and pupylation sites. However, the traditional experimental methods are laborious and time-consum...
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 biomedical technologies are enabling us to collect multi-view biomedical datasets for the study of complex diseases. While all the views of data tend to explore complementary inf...
The prediction of algal chlorophyll-a and water clarity in lentic ecosystems is a hot issue due to rapid deteriorations of drinking water quality and eutrophication processes. Our key objectives of the study were to predict long-term algal chlorophyll-a and transparency (water clarity), measured as Secchi depth, in spatially heterogeneous and tempo...
The kernel canonical correlation analysis based U-statistic (KCCU) is being used to detect nonlinear gene-gene co-associations. Estimating the variance of the KCCU is however computationally intensive. In addition, the kernel canonical correlation analysis (kernel CCA) is not robust to contaminated data. Using a robust kernel mean element and a rob...
Classification is a key supervised machine learning technique, which is essential and interesting topic for ecological research. It deals a way to classify a dataset into subsets that share common designs. Particularly, there are many classification processes to select from, each creating firm assumptions about the data and about how classification...
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 subsets that share common patterns. Notably, there are many classification algorithms to choose from, each making certain assumptions about the data and about how classificat...
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,. Since positive definite kernel based methods on genomic information can improve the prediction of diseases, in this paper we proposed a new method, "kerne...
Identifying significant outliers or atypical objects from multimodal datasets is an essential and challenging issue for biomedical research. This problem is addressed, using the influence function of multiple kernel canonical correlation analysis. First, the influence function (IF) of the kernel mean element, the kernel covariance operator, the ker...
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. Using a semiparametric method on a reproducing kernel Hilbert space (RKHS), we used a standard mixed-effects linear model and derived a score-based...
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. Basically, the methods based on the gene are more effective than the methods based on a single SNP. Recent years, the kernel canonical correlation analysis (Classical kern...
Imaging genetic research has essentially focused on discovering unique and co-association effects, but typically ignoring to identify outliers or atypical objects in genetic as well as non-genetics variables. Identifying significant outliers is an essential and challenging issue for imaging genetics and multiple sources data analysis. Therefore, we...
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. Kernel and Multiple Kernel CCA are popular methods for finding nonlinear correlations between high-dimensional datasets. Data was gathered from 183 patients, 79 with schizoph...
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. Basically, the methods based on the gene are more effective than the methods based on a single SNP. Recent years, while the kernel canonical correlation analysis (Classica...
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 depend on the kernel covariance operator (kernel CO) or kernel cross-covariance operator (kernel CCO). They are sensitive to contaminated data, even when using bounded positi...
Mixed ligand transition metal complexes of Cu(II) and Cd(II) ions were synthesized, where maleic acid as primary ligand and heterocyclic amine bases as a secondary ligands have been used, respectively. The prepared complexes, [Cu(MA)(1,10-Phen)], [Cu(MA)(Py) 2 ] and [Cd(MA)(IQ) 2 ] were characterized by their conventional physical and chemical anal...
In kernel methods, choosing a suitable kernel is indispensable for favorable results. No well-founded methods, however, have been established in general for unsupervised learning. We focus on kernel Principal Component Analysis (kernel PCA), which is a nonlinear extension of principal component analysis and has been used electively for extracting n...
Kernel canonical correlation analysis (kernel CCA) is sensitive to the choice of appropriate kernels and associated parameters. To the best of our knowledge there is no general well-founded approach for choosing them. As we demonstrate with Gaussian kernels, the kernel CCA tends to show perfect correlation as the bandwidth parameter of the Gaussian...
Qualitative robustness, influence function, and breakdown point are three main concepts to judge an estimator from the viewpoint of robust estimation. It is important as well as interesting to study relation among them. This article attempts to present the concept of qualitative robustness as forwarded by first proponents and its later development....
Qualitative robustness, influence function, and breakdown point are three main concepts to judge an estimator from the viewpoint of robust estimation. It is important as well as interesting to study relation among them. This article attempts to present the concept of qualitative robustness as forwarded by first proponents and its later development....
A number of measures of canonical correlation coefficient are now used in multimedia related fields like object recognition, image segmentation facial expression recognition and pattern recognition in the different literature. Some robust forms of classical canonical correlation coefficient are introduced recently to address the robustness issue of...
A number of measures of canonical correlation coefficient are now used in pattern recognition in the different literature. Some robust forms of classical canonical correlation coefficient are introduced recently to address the robustness issue of the canonical coefficient in the presence of outliers and departure from normality. Also a few number o...
A number of methods are now available in the literature for measuring the coefficient of correlation. Many of them are introduced recently to address the robustness issue of the correlation coefficient in the presence of outliers and departure from normality. But not much work has been done to investigate their relative performances through simulat...
Metal cheletes of Fe(III), Co(II) and Ni(II) with bis(2,4,4-trimethyl pentyl)phosphinic acid (cyanex-272; abbreviated as LH) were synthesized. The prepared complexes have the compositions: 1. [Fe(L)3], 2. K[Co(L)3] and 3. [Ni(L)2]. Their conventional physical and chemical analysis had been done. Their anti-bacterial and antifungal activity had been...
Questions
Questions (6)
What are the difference and limitations of Biomedical informatics vs biomedical science?
I'd like to know the difference between MLP and DNN
Projects
Projects (5)
This project is important to my field because the proposed method has shown to have better performance than state-of-the-art-methods in gene saving and has identified many more significant gene interactions, suggesting that genes function in a concerted effort in colon cancer and Osteoporosis. In similar biomedical data analysis, kernel-based methods could be applied to select a potential subset of genes.
The first goal is to compare fifteen estimators of correlation coefficient available in literature through simulation, bootstrapping, influence function and estimators of influence function. Final goal is to present the concept of qualitative robustness as forwarded by first proponents and its later development
This project is important to my field because in theoretically the introduced principles can also be applied to many other kernel-based machine learning methods involving kernel CO or kernel CCO. In application, the proposed visualization method can detect influential patients as well as biomarkers (e.g. Schizophrenia patients) of multiple sources of data. The proposed method can determine significant biomarkers and brain region of interest
