Integrating Genetic and Network Analysis to Characterize Genes Related to Mouse Weight

Emergence of Scaling in Random Networks

Barabási, A.-L., & Albert, R. (1999) · Science · doi:10.1126/science.286.5439.509 · Cited by 36,135

DAVID: Database for Annotation, Visualization, and Integrated Discovery

Dennis, G., Sherman, B. T., Hosack, D. A., Yang, J., Gao, W., Lane, H. C., & Lempicki, R. A. (2003) · Genome Biology · doi:10.1186/gb-2003-4-5-p3 · Cited by 9,327

Network biology: understanding the cell's functional organization

Barabási, A.-L., & Oltvai, Z. N. (2004) · Nature Reviews Genetics · doi:10.1038/nrg1272 · Cited by 7,903

Error and attack tolerance of complex networks

Albert, R., Jeong, H., & Barabási, A.-L. (2000) · Nature · doi:10.1038/35019019 · Cited by 7,534

Error and attack tolerance of complex networksPreprint

Albert, R., Jeong, H., & Barabási, A.-Ĺ. (2000) · Cited by 6,554

A General Framework for Weighted Gene Co-Expression Network Analysis

Zhang, B., & Horvath, S. (2005) · Statistical Applications in Genetics and Molecular Biology · doi:10.2202/1544-6115.1128 · Cited by 5,684

Lethality and centrality in protein networks

Jeong, H., Mason, S., Barabási, A., & Oltvai, Z. (2001) · Nature · doi:10.1038/35075138 · Cited by 5,327

The Large-Scale Organization of Metabolic Networks

Jeong, H., Tombor, B., Albert, R., Oltvai, Z. N., & Barabási, A.-L. (2000) · Nature · doi:10.1038/35036627 · Cited by 4,972

Hierarchical Organization of Modularity in Metabolic Networks

Ravasz, E., Somera, A. L., Mongru, D. A., Oltvai, Z. N., & Barabási, A.-L. (2002) · Science · doi:10.1126/science.1073374 · Cited by 4,224

From molecular to modular cell biology.

Hartwell, L. H., Hopfield, J. J., Leibler, S., & Murray, A. W. (1999) · Nature · doi:10.1038/35011540 · Cited by 3,632

Systems biology analysis of sjögren's syndrome and mucosa‐associated lymphoid tissue lymphoma in parotid glands

Hu, S., Zhou, M., Jiang, J., Wang, J., Elashoff, D. A., Gorr, S., Michie, S. A., Spijkervet, F. K. L., Bootsma, H., Kallenberg, C. G. M., et al. (2008) · Arthritis & Rheumatism · doi:10.1002/art.24150 · Cited by 67

Prediction of Human Disease-Related Gene Clusters by Clustering Analysis

Sun, P. G., Gao, L., & Han, S. (2011) · International Journal of Biological Sciences · doi:10.7150/ijbs.7.61 · Cited by 63

mgm: Estimating Time-Varying Mixed Graphical Models in High-Dimensional DataPreprint

Haslbeck, J. M. B., & Waldorp, L. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1510.06871 · Cited by 142

Lysosomal Stress in Obese Adipose Tissue Macrophages Contributes to MITF-Dependent Gpnmb Induction

Gabriel, T. L., Tol, M. J., Ottenhoff, R., van Roomen, C. P. A. A., Aten, J., Claessen, N., Hooibrink, B., de Weijer, B. A., Serlie, M. J. M., Argmann, C., et al. (2014) · Diabetes · doi:10.2337/db13-1720 · Cited by 66

Weighted gene co-expression network analysis of expression data of monozygotic twins identifies specific modules and hub genes related to BMI

Wang, W., Jiang, W., Hou, L., Duan, H., Wu, Y., Xu, C., Tan, Q., Li, S., & Zhang, D. (2017) · BMC Genomics · doi:10.1186/s12864-017-4257-6 · Cited by 66

A Systems Biology Approach to Drug Discovery

Zhu, J., Zhang, B., & Schadt, E. E. (2008) · Advances in genetics · doi:10.1016/s0065-2660(07)00421-x · Cited by 63

Celsius: a community resource for Affymetrix microarray data

Day, A., Carlson, M. R. J., Dong, J., O’Connor, B. D., & Nelson, S. F. (2007) · Genome biology · doi:10.1186/gb-2007-8-6-r112 · Cited by 63

Gene regulation underlies environmental adaptation in house micePreprint

Mack, K. L., Ballinger, M. A., Phifer‐Rixey, M., & Nachman, M. W. (2018) · Genome Research · doi:10.1101/gr.238998.118 · Cited by 58

Identification of a gene module associated with BMD through the integration of network analysis and genome-wide association data

Farber, C. R. (2010) · Journal of Bone and Mineral Research · doi:10.1002/jbmr.138 · Cited by 63

Bicc1 is a genetic determinant of osteoblastogenesis and bone mineral density

Mesner, L. D., Ray, B. J., Hsu, Y., Manichaikul, A. W., Lum, E., Bryda, E. C., Rich, S. S., Rosen, C. J., Criqui, M. H., Allison, M., et al. (2014) · Journal of Clinical Investigation · doi:10.1172/jci73072 · Cited by 60

Floral transcriptomes reveal gene networks in pineapple floral growth and fruit development

Wang, L., Li, Y., Jin, X., Liu, L., Dai, X., Liu, Y., Zhao, L., Zheng, P., Wang, X., Liu, Y., et al. (2020) · Communications Biology · doi:10.1038/s42003-020-01235-2 · Cited by 63

Genetic diversity and striatal gene networks: focus on the heterogeneous stock-collaborative cross (HS-CC) mouse

Iancu, O. D., Darakjian, P., Walter, N. A. R., Malmanger, B., Oberbeck, D. L., Belknap, J. K., McWeeney, S. K., & HITZEMANN, R. J. (2010) · BMC Genomics · doi:10.1186/1471-2164-11-585 · Cited by 60

Coronary Artery Endothelial Transcriptome In Vivo

Civelek, M., Manduchi, E., Riley, R., Stoeckert, C. J., & Davies, P. F. (2011) · Circulation Cardiovascular Genetics · doi:10.1161/circgenetics.110.958926 · Cited by 60

Systems Biology Approaches and Applications in Obesity, Diabetes, and Cardiovascular Diseases

Meng, Q., Mäkinen, V., Luk, H., & Yang, X. (2012) · Current Cardiovascular Risk Reports · doi:10.1007/s12170-012-0280-y · Cited by 58

Characterizing Dynamic Changes in the Human Blood Transcriptional Network

Zhu, J., Chen, Y., Leonardson, A. S., Wang, K., Lamb, J. R., Emilsson, V., & Schadt, E. E. (2010) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1000671 · Cited by 57

Advances in systems biology are enhancing our understanding of disease and moving us closer to novel disease treatments

Schadt, E. E., Zhang, B., & Zhu, J. (2009) · Genetica · doi:10.1007/s10709-009-9359-x · Cited by 57

Systems Genetic Analysis of Osteoblast-Lineage Cells

Calabrese, G. M., Bennett, B. J., Orozco, L. D., Kang, H. M., Eskin, E., Dombret, C., Backer, O. D., Lusis, A. J., & Farber, C. R. (2012) · PLoS Genetics · doi:10.1371/journal.pgen.1003150 · Cited by 59

Islet proteomics reveals genetic variation in dopamine production resulting in altered insulin secretion

Mitok, K. A., Freiberger, E. C., Schueler, K. L., Rabaglia, M. E., Stapleton, D. S., Kwiecien, N. W., Malec, P. A., Hebert, A. S., Broman, A. T., Kennedy, R. T., et al. (2018) · Journal of Biological Chemistry · doi:10.1074/jbc.ra117.001102 · Cited by 61

Effects of atherogenic diet on hepatic gene expression across mouse strains

Shockley, K. R., Witmer, D. R., Burgess-Herbert, S. L., Paigen, B. J., & Churchill, G. A. (2009) · Physiological Genomics · doi:10.1152/physiolgenomics.90350.2008 · Cited by 56

Identification of Tissue-Specific Protein-Coding and Noncoding Transcripts across 14 Human Tissues Using RNA-seq

Zhu, J., Chen, G., Zhu, S., Li, S., Wen, Z., Li, B., Zheng, Y., & Shi, L. M. (2016) · Scientific Reports · doi:10.1038/srep28400 · Cited by 58

Optimal local community detection in social networks based on density drop of subgraphs

Qi, X., Tang, W., Wu, Y., Guo, G., Fuller, E., & Zhang, C. (2013) · Pattern Recognition Letters · doi:10.1016/j.patrec.2013.09.008 · Cited by 55

Gene Network Effects on Brain Microstructure and Intellectual Performance Identified in 472 Twins

Chiang, M., Marina Barysheva, A., McMahon, K. L., de Zubicaray, G. I., Johnson, K., Montgomery, G. W., Martin, N. G., Toga, A. W., Wright, M. J., Shapshak, P., et al. (2012) · Journal of Neuroscience · doi:10.1523/jneurosci.5993-11.2012 · Cited by 56

High-Resolution Mapping of Gene Expression Using Association in an Outbred Mouse Stock

Ghazalpour, A., Doss, S., Kang, H. M., Farber, C. R., Wen, P.-Z., Brozell, A., Rivera, R. M. C., Eskin, E., Smith, D., Drake, T. A., et al. (2008) · PLoS Genetics · doi:10.1371/journal.pgen.1000149 · Cited by 53

Network module detection: Affinity search technique with the multi-node topological overlap measure

Li, A., & Horvath, S. (2009) · BMC Research Notes · doi:10.1186/1756-0500-2-142 · Cited by 56

Co-expression Network Analysis Identified COL8A1 Is Associated with the Progression and Prognosis in Human Colon Adenocarcinoma

Shang, J., Wang, F., Chen, P., Wang, X., Ding, F. H., Liu, S., & Zhao, Q. (2018) · Digestive Diseases and Sciences · doi:10.1007/s10620-018-4996-5 · Cited by 52

Data-driven assessment of eQTL mapping methods

Michaelson, J. J., Alberts, R., Schughart, K., & Beyer, A. (2010) · BMC Genomics · doi:10.1186/1471-2164-11-502 · Cited by 50

Gene Co-expression Analysis to Characterize Genes Related to Marbling Trait in Hanwoo (Korean) Cattle

Lim, D., Lee, S. H., Kim, N.-K., Cho, Y.-M., Chai, H., Seong, H., & Kim, H. (2013) · Asian-Australasian Journal of Animal Sciences · doi:10.5713/ajas.2012.12375 · Cited by 51

Discrete Molecular Classes of Ovarian Cancer Suggestive of Unique Mechanisms of Transformation and Metastases

Gardi, N., Deshpande, T. U., Kamble, S. C., Budhe, S. R., & Bapat, S. A. (2013) · Clinical Cancer Research · doi:10.1158/1078-0432.ccr-13-2063 · Cited by 50

Gene co-expression analysis of tomato seed maturation reveals tissue-specific regulatory networks and hubs associated with the acquisition of desiccation tolerance and seed vigour

Bizouerne, E., Buitink, J., Vu, B. L., Vu, J. L., Esteban, E., Pasha, A., Provart, N. J., Verdier, J., & Leprince, O. (2021) · BMC Plant Biology · doi:10.1186/s12870-021-02889-8 · Cited by 51

Integrating Global Gene Expression Analysis and Genetics

Farber, C. R., & Lusis, A. J. (2008) · Advances in genetics · doi:10.1016/s0065-2660(07)00420-8 · Cited by 52

Systems genetics analysis of cancer susceptibility: from mouse models to humans

Quigley, D. A., & Balmain, A. (2009) · Nature Reviews Genetics · doi:10.1038/nrg2617 · Cited by 48

Systems-Level Analysis of Genome-Wide Association Data

Farber, C. R. (2013) · G3 Genes Genomes Genetics · doi:10.1534/g3.112.004788 · Cited by 51

Identification of the Biomarkers and Pathological Process of Osteoarthritis: Weighted Gene Co-expression Network Analysis

Gu, H.-Y., Yang, M., Guo, J., Zhang, C., Lin, L., Liu, Y., & Wei, R. (2019) · Frontiers in Physiology · doi:10.3389/fphys.2019.00275 · Cited by 50

Epistatic Networks Jointly Influence Phenotypes Related to Metabolic Disease and Gene Expression in Diversity Outbred Mice

Tyler, A. L., Ji, B., Gatti, D. M., Munger, S. C., Churchill, G. A., Svenson, K. L., & Carter, G. W. (2017) · Genetics · doi:10.1534/genetics.116.198051 · Cited by 52

Gene Bionetwork Analysis of Ovarian Primordial Follicle Development

Nilsson, E. E., Savenkova, M. I., Schindler, R. L., Zhang, B., Schadt, E. E., & Skinner, M. K. (2010) · PLoS ONE · doi:10.1371/journal.pone.0011637 · Cited by 47

Experimental Chagas disease-induced perturbations of the fecal microbiome and metabolome

McCall, L., Tripathi, A., Vargas, F., Knight, R. D., Dorrestein, P. C., & Siqueira-Neto, J. L. (2018) · PLoS neglected tropical diseases · doi:10.1371/journal.pntd.0006344 · Cited by 49

Identification of glioblastoma gene prognosis modules based on weighted gene co-expression network analysis

Xu, P., Yang, J., Liu, J., Yang, X., Liao, J., Yuan, F., Xu, Y., Liu, B., & Chen, Q. (2018) · BMC Medical Genomics · doi:10.1186/s12920-018-0407-1 · Cited by 47

The relevance between the immune response-related gene module and clinical traits in head and neck squamous cell carcinoma

Song, Y., Pan, Y., & Liu, J. (2019) · Cancer Management and Research · doi:10.2147/cmar.s201177 · Cited by 47

Integration of biological networks and pathways with genetic association studies

Sun, Y. V. (2012) · Human Genetics · doi:10.1007/s00439-012-1198-7 · Cited by 47

Sex Specific Gene Regulation and Expression QTLs in Mouse Macrophages from a Strain Intercross

Bhasin, J., Chakrabarti, E., Peng, D., Kulkarni, A., Chen, X., & Smith, J. D. (2008) · PLoS ONE · doi:10.1371/journal.pone.0001435 · Cited by 47

Heterogeneous Stock Populations for Analysis of Complex Traits

Woods, L. C. S., & Mott, R. (2016) · Methods in molecular biology · doi:10.1007/978-1-4939-6427-7_2 · Cited by 43

A cross-species transcriptomics approach to identify genes involved in leaf development

Street, N. R., Sjödin, A., Bylesjö, M., Gustafsson, P., Trygg, J., & Jansson, S. (2008) · BMC Genomics · doi:10.1186/1471-2164-9-589 · Cited by 45

Large-scale analysis of Arabidopsis transcription reveals a basal co-regulation network

Atias, O., Chor, B., & Chamovitz, D. (2009) · BMC Systems Biology · doi:10.1186/1752-0509-3-86 · Cited by 43

Construction and application of a co-expression network in Mycobacterium tuberculosis

Jiang, J. H., Sun, X. L., Wu, W., Li, L., Wu, H., Zhang, L., Yu, G., & Li, Y. (2016) · Scientific Reports · doi:10.1038/srep28422 · Cited by 43

Genes located in a chromosomal inversion are correlated with territorial song in white‐throated sparrows

Zinzow-Kramer, W. M., Horton, B. M., McKee, C., Michaud, J. M., Tharp, G. K., Thomas, J. W., Tuttle, E. M., Yi, S. V., & Maney, D. L. (2015) · Genes Brain & Behavior · doi:10.1111/gbb.12252 · Cited by 44

Identification of Genes and Networks Driving Cardiovascular and Metabolic Phenotypes in a Mouse F2 Intercross

Derry, J. M. J., Zhong, J., Molony, C. M., MacNeil, D., GuhaThakurta, D., Zhang, B., Mudgett, J. S., Small, K. M., Fertak, L. E., Guimond, A., et al. (2010) · PLoS ONE · doi:10.1371/journal.pone.0014319 · Cited by 44

Network-based approach identified cell cycle genes as predictor of overall survival in lung adenocarcinoma patients

Li, Y., Tang, H., Sun, Z., Bungum, A. O., Edell, E. S., Lingle, W. L., Stoddard, S. M., Zhang, M., Jen, J., Yang, P., et al. (2013) · Lung Cancer · doi:10.1016/j.lungcan.2012.12.022 · Cited by 41

Systems biology asks new questions about sex differences

Arnold, A. P., van Nas, A., & Lusis, A. J. (2009) · Trends in Endocrinology and Metabolism · doi:10.1016/j.tem.2009.06.007 · Cited by 42

Pathway hunting by random survival forests

Chen, X., & Ishwaran, H. (2012) · Bioinformatics · doi:10.1093/bioinformatics/bts643 · Cited by 41

Variation in Rubisco activase (RCAβ) gene promoters and expression in soybean [Glycine max (L.) Merr.]

Chao, M., Yin, Z., Hao, D., Zhang, J., Song, H., Ning, A., Xu, X., & Yu, D. (2013) · Journal of Experimental Botany · doi:10.1093/jxb/ert346 · Cited by 39

Simultaneous supervised clustering and feature selection over a graph

Shen, X., Huang, H., & Pan, W. (2012) · Biometrika · doi:10.1093/biomet/ass038 · Cited by 39

Gene bionetworks involved in the epigenetic transgenerational inheritance of altered mate preference: environmental epigenetics and evolutionary biology

Skinner, M. K., Savenkova, M. I., Zhang, B., Gore, A. C., & Crews, D. P. (2014) · BMC Genomics · doi:10.1186/1471-2164-15-377 · Cited by 43

Meta-analysis of heterogeneous data sources for genome-scale identification of risk genes in complex phenotypes

Pers, T. H., Hansen, N. T., Lage, K., Koefoed, P., Dworzyński, P., Miller, M. L., Flint, T. J., Mellerup, E. T., Dam, H., Andreassen, O. A., et al. (2011) · Genetic Epidemiology · doi:10.1002/gepi.20580 · Cited by 38

Protein expression based multimarker analysis of breast cancer samples

Presson, A. P., Yoon, N. K., Bagryanova, L., Mah, V. H., Alavi, M., Maresh, E. L., Rajasekaran, A. K., Goodglick, L., Chia, D., & Horvath, S. (2011) · BMC Cancer · doi:10.1186/1471-2407-11-230 · Cited by 38

Discovery of Consensus Gene Signature and Intermodular Connectivity Defining Self-Renewal of Human Embryonic Stem Cells

Kim, J., Khalid, O. S., Namazi, A., Tu, T. G., Elie, O., Lee, C., & Kim, Y. Y. (2014) · Stem Cells · doi:10.1002/stem.1675 · Cited by 37

Inferring Causal Associations between Genes and Disease via the Mapping of Expression Quantitative Trait Loci

Sieberts, S. K., & Schadt, E. E. (2019) · Wiley eBooks · doi:10.1002/9781119487845.ch25 · Cited by 37

Incorporating gene co-expression network in identification of cancer prognosis markers

Ma, S., Shi, M., Li, Y., Yi, D., & Shia, B. (2010) · BMC Bioinformatics · doi:10.1186/1471-2105-11-271 · Cited by 36

Serious limitations of the QTL/Microarray approach for QTL gene discovery

Verdugo, R. A., Farber, C. R., Warden, C. H., & Medrano, J. F. F. (2010) · BMC Biology · doi:10.1186/1741-7007-8-96 · Cited by 39

Identifying RBM47, HCK, CD53, TYROBP, and HAVCR2 as Hub Genes in Advanced Atherosclerotic Plaques by Network-Based Analysis and Validation

Liu, C.-Y., Zhang, H., Chen, Y., Wang, S., Chen, Z., Liu, Z., & Wang, J. (2021) · Frontiers in Genetics · doi:10.3389/fgene.2020.602908 · Cited by 36

Gene Coexpression Network and Module Analysis across 52 Human Tissues

He, B., Xu, J., Tian, Y., Liao, B., Lang, J., Lin, H., Mo, X., Lu, Q., Tian, G., & Bing, P. (2020) · BioMed Research International · doi:10.1155/2020/6782046 · Cited by 37

Identification of prognostic markers of high grade prostate cancer through an integrated bioinformatics approach

Huang, H., Zhang, Q., Ye, C., Lv, J.-M., Liu, X., Chen, L., Wu, H., Yin, L., Cui, X., Xu, D.-F., et al. (2017) · Journal of Cancer Research and Clinical Oncology · doi:10.1007/s00432-017-2497-0 · Cited by 35

Estimation of covariance and precision matrix, network structure, and a view toward systems biology

Kuismin, M., & Sillanpää, M. J. (2017) · Wiley Interdisciplinary Reviews Computational Statistics · doi:10.1002/wics.1415 · Cited by 38

Exploring Shared Pathogenesis of Alzheimer’s Disease and Type 2 Diabetes Mellitus via Co-expression Networks Analysis

Zhu, Y., Ding, X., She, Z., Bai, X., Nie, Z., Wang, F., Wang, F., & Geng, X. (2020) · Current Alzheimer Research · doi:10.2174/1567205017666200810164932 · Cited by 36

Network-Based Identification of Biomarkers Coexpressed with Multiple Pathways

Guo, N. L., & Wan, Y. (2014) · Cancer Informatics · doi:10.4137/cin.s14054 · Cited by 35

Using Network Component Analysis to Dissect Regulatory Networks Mediated by Transcription Factors in Yeast

Ye, C., Galbraith, S. J., Liao, J. C., & Eskin, E. (2009) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1000311 · Cited by 34

Modeling Causality for Pairs of Phenotypes in System Genetics

Neto, E. C., Broman, A. T., Keller, M. P., Attie, A., Zhang, B., Zhu, J., & Yandell, B. S. (2013) · Genetics · doi:10.1534/genetics.112.147124 · Cited by 34

Upstream transcription factor 1 influences plasma lipid and metabolic traits in mice

Wu, S., Mar‐Heyming, R., Dugum, E. Z., Kolaitis, N. A., Qi, H., Pajukanta, P., Castellani, L. W., Lusis, A. J., & Drake, T. A. (2009) · Human Molecular Genetics · doi:10.1093/hmg/ddp526 · Cited by 33

Adipose Co-expression networks across Finns and Mexicans identify novel triglyceride-associated genes

Haas, B. E., Horvath, S., Pietiläinen, K. H., Cantor, R. M., Nikkola, E., Weissglas‐Volkov, D., Rissanen, A., Civelek, M., Cruz‐Bautista, I., Riba, L., et al. (2012) · BMC Medical Genomics · doi:10.1186/1755-8794-5-61 · Cited by 35

Review of Weighted Gene Coexpression Network Analysis

Fuller, T. F., Langfelder, P., Presson, A. P., & Horvath, S. (2011) · Springer eBooks · doi:10.1007/978-3-642-16345-6_18 · Cited by 34

Genes and Gene Networks Implicated in Aggression Related Behaviour

Malki, K., Pain, O., Du Rietz, E., Tosto, M. G., Paya-Cano, J., Sandnabba, K. N., de Boer, S., Schalkwyk, L. C., & Sluyter, F. (2014) · neurogenetics · doi:10.1007/s10048-014-0417-x · Cited by 33

Computational systems biology of aging

Kriete, A., Lechner, M. S., Clearfield, D., & Bohmann, D. (2010) · WIREs Systems Biology and Medicine · doi:10.1002/wsbm.126 · Cited by 34

Expression quantitative trait loci infer the regulation of isoflavone accumulation in soybean (Glycine max L. Merr.) seed

Wang, Y., Han, Y., Teng, W., Zhao, X., Li, Y., Wu, L., Li, D., & Li, W. (2014) · BMC Genomics · doi:10.1186/1471-2164-15-680 · Cited by 32

A modular analysis of microglia gene expression, insights into the aged phenotype

Cho, C. E., Damle, S. S., Wancewicz, E. V., Mukhopadhyay, S., Hart, C. E., Mazur, C., Swayze, E. E., & Kamme, F. (2019) · BMC Genomics · doi:10.1186/s12864-019-5549-9 · Cited by 32

Multi-view clustering for multi-omics data using unified embedding

Mitra, S., Saha, S., & Hasanuzzaman, M. (2020) · Scientific Reports · doi:10.1038/s41598-020-70229-1 · Cited by 32

Deciphering the molecular basis of human cardiovascular disease through network biology

Chan, S. Y., White, K., & Loscalzo, J. (2012) · Current Opinion in Cardiology · doi:10.1097/hco.0b013e3283515b31 · Cited by 33

A Systematic Approach to Multifactorial Cardiovascular Disease

Schwartz, S. M., Schwartz, H. T., Horvath, S., Schadt, E., & Lee, S. (2012) · Arteriosclerosis Thrombosis and Vascular Biology · doi:10.1161/atvbaha.112.300123 · Cited by 33

Genome-wide identification and differentially expression analysis of lncRNAs in tilapia

Li, B. J., Jiang, D. L., Meng, Z. N., Zhang, Y., Zhu, Z. X., Lin, H., & Xia, J. (2018) · BMC Genomics · doi:10.1186/s12864-018-5115-x · Cited by 31

Identification of Laying-Related SNP Markers in Geese Using RAD Sequencing

Yu, S., Chu, W., Zhang, L., Han, H., Zhao, R.-X., Wu, W., Zhu, J., Dodson, M. V., Wei, W., Liu, H., et al. (2015) · PLoS ONE · doi:10.1371/journal.pone.0131572 · Cited by 33

Functional Modules Distinguish Human Induced Pluripotent Stem Cells from Embryonic Stem Cells

Wang, A., Huang, K. Y., Shen, Y., Xue, Z., Cai, C., Horvath, S., & Fan, G. (2011) · Stem Cells and Development · doi:10.1089/scd.2010.0574 · Cited by 30

Transcriptional regulation of bark freezing tolerance in apple (Malus domestica Borkh.)

Liang, Y.-H., Wang, S., Zhao, C., Ma, X., Zhao, Y., Shao, J., Li, Y., Li, H., Song, H., Li, H., et al. (2020) · Horticulture Research · doi:10.1038/s41438-020-00432-8 · Cited by 33

Phytotoxic effect and molecular mechanism induced by graphene towards alfalfa (Medicago sativa L.) by integrating transcriptomic and metabolomics analysis

Zhao, C., Guo, Z., Niu, J., Xu, N., Sui, X., Kareem, H. A., Hassan, M. U., Yan, M., Zhang, Q., Wang, Z., et al. (2021) · Chemosphere · doi:10.1016/j.chemosphere.2021.133368 · Cited by 30

Intra- and inter-individual genetic differences in gene expressionPreprint

Cowley, M. J., Cotsapas, C. J., Williams, R. B. H., Chan, E. K. F., Pulvers, J. N., Liu, M. Y., Luo, O. J., Nott, D. J., & Little, P. (2008) · Nature Precedings · doi:10.1007/s00335-009-9181-x · Cited by 32

Systems Biology Approaches to a Rational Drug Discovery Paradigm

Prathipati, P., & Mizuguchi, K. (2015) · Current Topics in Medicinal Chemistry · doi:10.2174/1568026615666150826114524 · Cited by 32

Integrative Genomics Strategies to Elucidate the Complexity of Drug Response

Kasarskis, A., Yang, X., & Schadt, E. E. (2011) · Pharmacogenomics · doi:10.2217/pgs.11.115 · Cited by 29

An introduction to network analysis for studies of medication use

Askar, M., Nozal‐Cañadas, R., & Svendsen, K. (2021) · Research in Social and Administrative Pharmacy · doi:10.1016/j.sapharm.2021.06.021 · Cited by 29

Integration of summary data from GWAS and eQTL studies identified novel causal BMD genes with functional predictions

Meng, X.-H., Chen, X., Greenbaum, J., Zeng, Q., You, S.-L., Xiao, H., Tan, L., & Deng, H. (2018) · Bone · doi:10.1016/j.bone.2018.05.012 · Cited by 30

Future of Osteoporosis Genetics: Enhancing Genome-Wide Association Studies

Farber, C. R., & Lusis, A. J. (2009) · Journal of Bone and Mineral Research · doi:10.1359/jbmr.091101 · Cited by 29

An Integrative Analysis of Meningioma Tumors Reveals the Determinant Genes and Pathways of Malignant Transformation

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The i...

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