Results 1 to 10 of about 2,615,001 (338)

Gene–gene interaction detection with deep learning [PDF]

Communications Biology, 2022
An open-source framework combines deep learning and permutations of gene interaction neural networks to detect complex gene–gene interactions and their significance in contributions to phenotypes.
Tianyu Cui, Khaoula El Mekkaoui, Jaakko Reinvall, Aki S. Havulinna, Pekka Marttinen, Samuel Kaski   +5 more
doaj   +8 more sources

A systematic analysis of gene–gene interaction in multiple sclerosis [PDF]

BMC Medical Genomics, 2022
Background For the most part, genome-wide association studies (GWAS) have only partially explained the heritability of complex diseases. One of their limitations is to assume independent contributions of individual variants to the phenotype.
Lotfi Slim, Clément Chatelain, Hélène de Foucauld, Chloé-Agathe Azencott   +3 more
doaj   +6 more sources

Gene-gene Interaction Analyses for Atrial Fibrillation [PDF]

Sci Rep, 2016
Atrial fibrillation (AF) is a heritable disease that affects more than thirty million individuals worldwide. Extensive efforts have been devoted to the study of genetic determinants of AF.
Albert, C.M. (Christine), Alonso, A. (Alvaro), Arking, D.E. (Dan), Barnard, J. (John), Bartz, T.M. (Traci M.), Benjamin, E.J. (Emelia), Chasman, D.I. (Daniel), Chen, L. (Lin), Chung, M.K. (Mina), Delgado, G.E. (Graciela E.), Ding, J. (Jingzhong), Dörr, M. (Marcus), Ellinor, P.T. (Patrick), Ford, I. (Ian), Franco, O.H. (Oscar), Geelhoed, B. (Bastiaan), Gudnason, V. (Vilmundur), Harris, T.B. (Tamara), Harst, P. (Pim) van der, Heckbert, S.R. (Susan), Heeringa, J. (Jan), Hendricks, A.E. (Audrey E.), Hofman, A. (Albert), Jukema, J.W. (Jan Wouter), Kacprowski, T. (Tim), Kathiresan, S. (Sekar), Klarin, D. (Derek), Kleber, M.E. (Marcus), Kors, J.A. (Jan), Kääb, S. (Stefan), Launer, L.J. (Lenore), Lin, H. (Honghuang), Liu, Y. (YongMei), Lohman, K. (Kurt), Lubitz, S.A. (Steven), Lunetta, K.L. (Kathryn), MacFarlane, P.W. (Peter), Meitinger, T. (Thomas), Mueller-Nurasyid, M., März, W. (Winfried), Natarajan, P. (Pradeep), Niemeijer, M.N. (Maartje), Psaty, B.M. (Bruce), Rienstra, S.A., Rose, L.M. (Lynda), Rotter, J.I. (Jerome I.), Sinner, M.F. (Moritz), Smith, A.V. (Albert Vernon), Smith, J.D. (Jonathan), Soliman, E.Z. (Elsayed Z.), Stricker, B.H.Ch. (Bruno), Trompet, S. (Stella), Uitterlinden, A.G. (André), Verweij, N. (Niek), Völzke, H. (Henry), Wagoner, D.R. (David) van, Waldenberger, M. (Melanie), Weiss, S. (Stefan), Xie, Z. (Zhijun)   +58 more
core   +11 more sources

Weak gene–gene interaction facilitates the evolution of gene expression plasticity [PDF]

BMC Biology, 2023
Background Individual organisms may exhibit phenotypic plasticity when they acclimate to different conditions. Such plastic responses may facilitate or constrain the adaptation of their descendant populations to new environments, complicating their ...
Hao-Chih Kuo, Cheng-Te Yao, Ben-Yang Liao, Meng-Pin Weng, Feng Dong, Yu-Cheng Hsu, Chih-Ming Hung   +6 more
doaj   +2 more sources

A trans‐omics assessment of gene–gene interaction in early‐stage NSCLC [PDF]

Molecular Oncology, 2023
Epigenome‐wide gene–gene (G × G) interactions associated with non‐small‐cell lung cancer (NSCLC) survival may provide insights into molecular mechanisms and therapeutic targets.
Jiajin Chen, Yunjie Song, Yi Li, Yongyue Wei, Sipeng Shen, Yang Zhao, Dongfang You, Li Su, Maria Moksnes Bjaanæs, Anna Karlsson, Maria Planck, Johan Staaf, Åslaug Helland, Manel Esteller, Hongbing Shen, David C. Christiani, Ruyang Zhang, Feng Chen   +17 more
doaj   +2 more sources

Molecular Basis of Gene-Gene Interaction: Cyclic Cross-Regulation of Gene Expression and Post-GWAS Gene-Gene Interaction Involved in Atrial Fibrillation. [PDF]

PLoS Genetics, 2015
Atrial fibrillation (AF) is the most common cardiac arrhythmia at the clinic. Recent GWAS identified several variants associated with AF, but they account for
Yufeng Huang, Chuchu Wang, Yufeng Yao, Xiaoyu Zuo, Shanshan Chen, Chengqi Xu, Hongfu Zhang, Qiulun Lu, Le Chang, Fan Wang, Pengxia Wang, Rongfeng Zhang, Zhenkun Hu, Qixue Song, Xiaowei Yang, Cong Li, Sisi Li, Yuanyuan Zhao, Qin Yang, Dan Yin, Xiaojing Wang, Wenxia Si, Xiuchun Li, Xin Xiong, Dan Wang, Yuan Huang, Chunyan Luo, Jia Li, Jingjing Wang, Jing Chen, Longfei Wang, Li Wang, Meng Han, Jian Ye, Feifei Chen, Jingqiu Liu, Ying Liu, Gang Wu, Bo Yang, Xiang Cheng, Yuhua Liao, Yanxia Wu, Tie Ke, Qiuyun Chen, Xin Tu, Robert Elston, Shaoqi Rao, Yanzong Yang, Yunlong Xia, Qing K Wang   +49 more
doaj   +2 more sources

Biomedical literature mining: graph kernel-based learning for gene–gene interaction extraction [PDF]

European Journal of Medical Research
The supervised machine learning method is often used for biomedical relationship extraction. The disadvantage is that it requires much time and money to manually establish an annotated dataset. Based on distant supervision, the knowledge base is combined
Ai-Ru Hsieh, Chen-Yu Tsai
doaj   +2 more sources

Mito-SiPE is a sequence-independent and PCR-free mtDNA enrichment method for accurate ultra-deep mitochondrial sequencing

Communications Biology, 2022
Mito-SiPE is a mitochondrial DNA (mtDNA) enrichment method that can derive high-quality mtDNA for rare heteroplasmy analysis, without relying on PCR or probes.
Darren J. Walsh, David J. Bernard, Faith Pangilinan, Madison Esposito, Denise Harold, Anne Parle-McDermott, Lawrence C. Brody   +6 more
doaj   +1 more source

Gene–gene and gene-environment interactions on cord blood total IgE in Chinese Han children

Allergy, Asthma & Clinical Immunology, 2021
Background IL13, IL4, IL4RA, FCER1B and ADRB2 are susceptible genes of asthma and atopy. Our previous study has found gene–gene interactions on asthma between these genes in Chinese Han children. Whether the interactions begin in fetal stage, and whether
Li Hua, Quanhua Liu, Jing Li, Xianbo Zuo, Qian Chen, Jingyang Li, Yuwei Wang, Haipei Liu, Zhaobo Shen, Yi Li, Zenan Ma, Shengdong Dong, Ruoxu Ji, Dingzhu Fang, Yi Chen, Wenwei Zhong, Jun Zhang, Jianhua Zhang, Yixiao Bao   +18 more
doaj   +1 more source

Gene-environment interaction in yeast gene expression. [PDF]

PLoS Biology, 2008
The effects of genetic variants on phenotypic traits often depend on environmental and physiological conditions, but such gene-environment interactions are poorly understood.
Erin N Smith, Leonid Kruglyak
doaj   +1 more source