Genome-wide association studies (GWAS) have extensively analyzed single SNP effects on a wide variety of common and complex diseases and found many genetic variants associated with diseases. However, there is still a large portion of the genetic variants left unexplained. This missing heritability problem might be due to the analytical strategy that limits analyses to only single SNPs. One of possible approaches to the missing heritability problem is to consider identifying multi-SNP effects or gene-gene interactions. The multifactor dimensionality reduction method has been widely used to detect gene-gene interactions based on the constructive induction by classifying high-dimensional genotype combinations into one-dimensional variable with two attributes of high risk and low risk for the case-control study. Many modifications of MDR have been proposed and also extended to the survival phenotype. In this study, we propose several extensions of MDR for the survival phenotype and compare the proposed extensions with earlier MDR through comprehensive simulation studies.
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http://dx.doi.org/10.1155/2015/671859 | DOI Listing |
Nat Comput Sci
December 2024
Computational Biology and Bioinformatics Program, Yale University, New Haven, CT, USA.
In single-cell sequencing analysis, several computational methods have been developed to map the cellular state space, but little has been done to map or create embeddings of the gene space. Here we formulate the gene embedding problem, design tasks with simulated single-cell data to evaluate representations, and establish ten relevant baselines. We then present a graph signal processing approach, called gene signal pattern analysis (GSPA), that learns rich gene representations from single-cell data using a dictionary of diffusion wavelets on the cell-cell graph.
View Article and Find Full Text PDFGigascience
January 2024
National Genomics Data Center, China National Center for Bioinformation, Beijing 100101, China.
Background: Exploring the cellular processes of genes from the aspects of biological networks is of great interest to understanding the properties of complex diseases and biological systems. Biological networks, such as protein-protein interaction networks and gene regulatory networks, provide insights into the molecular basis of cellular processes and often form functional clusters in different tissue and disease contexts.
Results: We present scGraph2Vec, a deep learning framework for generating informative gene embeddings.
J Dent Res
December 2024
Department of Pediatric Dentistry and Dental Public Health, Adams School of Dentistry, University of North Carolina at Chapel Hill, NC, USA.
Early childhood caries (ECC) is the most common noncommunicable childhood disease-an important health problem with known environmental and social/behavioral influences lacking consensus genetic risk loci. To address this knowledge gap, we conducted a genome-wide association study of ECC in a multiancestry population of U.S.
View Article and Find Full Text PDFPLoS One
December 2024
Faculté de Médecine Dentaire, Groupe de Recherche en Ecologie Buccale, Université Laval, Québec, QC, Canada.
To explore an alternative strategy to chemotherapy to combat oral cancer, natural products and their derivates constitute one promising approach. In the last previous study, we have demonstrated the potential anti-tumor properties of anethole; an aromatic compound abundantly present in nature that serves as a major active ingredient found in plants like anise and fennel. In the current study, we aimed to investigate how this molecule inhibits oral cancer cell proliferation and induces apoptosis.
View Article and Find Full Text PDFNeural Regen Res
December 2024
Department of Neurology, Institute of Neuroscience, Key Laboratory of Neurogenetics and Channelopathies of Guangdong Province and the Ministry of Education of China, the Second Affiliated Hospital, Guangzhou Medical University, Guangzhou, Guangdong Province, China.
Complex genetic architecture is the major cause of heterogeneity in epilepsy, which poses challenges for accurate diagnosis and precise treatment. A large number of epilepsy candidate genes have been identified from clinical studies, particularly with the widespread use of next-generation sequencing. Validating these candidate genes is emerging as a valuable yet challenging task.
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