A5011371797- Bioinformatics and Genomic Networks
- Genetic Associations and Epidemiology
- Gene expression and cancer classification
- Bone health and osteoporosis research
- RNA modifications and cancer
- Hip disorders and treatments
- RNA Research and Splicing
- Hip and Femur Fractures
- Radiomics and Machine Learning in Medical Imaging
- Cancer-related molecular mechanisms research
- Ferroptosis and cancer prognosis
- Hepatitis C virus research
- Liver Disease Diagnosis and Treatment
- Gene Regulatory Network Analysis
- Genomics and Chromatin Dynamics
- Nanoplatforms for cancer theranostics
- Nanoparticle-Based Drug Delivery
- Molecular Biology Techniques and Applications
Michigan Technological University
2021-2024
Michigan United
2022-2024
Linyi University
2024
Qilu Normal University
2024
Alternative splicing commonly generates unproductive mRNA transcripts that harbor premature termination codons, leading to their degradation by nonsense-mediated decay (NMD). These events reduce overall protein expression levels of affected genes, potentially contributing gene regulation and disease mechanisms. Here, we present LeafCutter2, which enables identification quantification from short-read RNA-seq data. LeafCutter2 requires minimal annotations (start stop codons) annotate...
Background Hip fracture occurs when an applied force exceeds the that proximal femur can support (the load or “strength”) and have devastating consequences with poor functional outcomes. Proximal femoral strengths for specific loading conditions be computed by subject-specific finite element analysis (FEA) using quantitative computerized tomography (QCT) images. However, radiation availability of QCT limit its clinical usability. Alternative low-dose widely available measurements, such as...
The emergence of genetic data coupled to longitudinal electronic medical records (EMRs) offers the possibility phenome-wide association studies (PheWAS). In PheWAS, whole phenome can be divided into numerous phenotypic categories according architecture across phenotypes. Currently, statistical analyses for PheWAS are mainly univariate analyses, which test between one variant and phenotype at a time. this article, we derived novel powerful multivariate method PheWAS. proposed involves three...
Joint analysis of multiple correlated phenotypes for genome-wide association studies (GWAS) can identify and interpret pleiotropic loci which are essential to understand pleiotropy in diseases complex traits. Meanwhile, constructing a network based on associations between genotypes provides new insight analyze phenotypes, explore whether might be related each other at higher level cellular organismal organization. In this paper, we first develop bipartite signed by linking into Genotype...
Abstract The emergence of genetic data coupled to longitudinal electronic medical records (EMRs) offers the possibility phenome-wide association studies (PheWAS). In PheWAS, whole phenome can be divided into numerous phenotypic categories according architecture across phenotypes. Currently, statistical analyses for PheWAS are mainly univariate analyses, which test between one variant and phenotype at a time. this article, we derived novel powerful multivariate method PheWAS. proposed...
Abstract Joint analysis of multiple correlated phenotypes for genome-wide association studies (GWAS) can identify and interpret pleiotropic loci which are essential to understand pleiotropy in diseases complex traits. Meanwhile, constructing a network based on associations between genotypes provides new insight analyze phenotypes, explore whether might be related each other at higher level cellular organismal organization. In this paper, we first develop bipartite signed by linking into...
Abstract Although genome-wide association studies (GWAS) have been successfully applied to a variety of complex diseases and identified many genetic variants underlying via single marker tests, there is still considerable heritability that could not be explained by GWAS. One alternative approach overcome the missing caused heterogeneity gene-based analysis, which considers aggregate effects multiple in test. Another transcriptome-wide study (TWAS). TWAS aggregates genomic information into...
Integration of heterogeneous and high-dimensional multi-omics data is becoming increasingly important in understanding genetic data. Each omics technique only provides a limited view the underlying biological process integrating layers simultaneously would lead to more comprehensive detailed diseases phenotypes. However, one obstacle faced when performing integration existence unpaired due instrument sensitivity cost. Studies may fail if certain aspects subjects are missing or incomplete. In...
Four statistical selection methods for inferring transcription factor (TF)-target gene (TG) pairs were developed by coupling mean squared error (MSE) or Huber loss function, with elastic net (ENET) least absolute shrinkage and operator (Lasso) penalty. Two also pathway regulatory networks (GRNs) combining MSE function a network (Net)-based To solve these regressions, we ameliorated an accelerated proximal gradient descent (APGD) algorithm to optimize parameter processes, resulting in equally...
Abstract Large-scale genome-wide association studies (GWAS) have been successfully applied to a wide range of genetic variants underlying complex diseases. The network-based penalized regression approach has developed overcome the challenges caused by computational efficiency for analyzing high-dimensional genomic data incorporating biological network. In this paper, we propose gene selection networks into case-control DNA sequence or methylation data. Instead using traditional dimension...
The aim of this paper is to design a deep learning-based model predict proximal femoral strength using multi-view information fusion. Method: We developed new models variational autoencoder (MVAE) for feature representation learning and product expert (PoE) applied the proposed an in-house Louisiana Osteoporosis Study (LOS) cohort with 931 male subjects, including 345 African Americans 586 Caucasians. With analytical solution Gaussian distribution, we adopted inference train designed...
Abstract Background. Analyses of a bipartite Genotype and Phenotype Network (GPN), linking the genetic variants phenotypes based on statistical associations, provide an integrative approach to elucidate complexities relationships across diseases identify pleiotropicloci. In this study, we assess contributions constructing well-defined GPN with clear representation associations by comparing network properties random network, including connectivity, centrality, community structure. Then,...
Therapeutic approaches of combining various treatments have appealed intensive interests for tumor therapy. Nevertheless, these strategies remain suffered from many obstacles the intricate microenvironment (TME, e.g. over-expressed GSH,...
Abstract Analyses of a bipartite Genotype and Phenotype Network (GPN), linking the genetic variants phenotypes based on statistical associations, provide an integrative approach to elucidate complexities relationships across diseases identify pleiotropic loci. In this study, we first assess contributions constructing well-defined GPN with clear representation associations by comparing network properties random network, including connectivity, centrality, community structure. Next, construct...
Abstract Although genome-wide association studies (GWAS) have been successfully applied to a variety of complex diseases and identified many genetic variants underlying diseases, there is still considerable heritability that could not be explained by GWAS. One alternative approach overcome the missing caused heterogeneity gene-based analysis, which considers aggregate effects multiple in single test. Another transcriptome-wide study (TWAS). TWAS aggregates genomic information into...