ToppGene Suite for gene list enrichment analysis and candidate gene prioritization

Data Mining

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The human disease network

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Towards a proteome-scale map of the human protein–protein interaction network

Rual, J.-F. M., Venkatesan, K., Hao, T., Hirozane-Kishikawa, T., Dricot, A., Li, N., Berriz, G. F., Gibbons, F. D., Dreze, M., Ayivi-Guedehoussou, N., et al. (2005) · Nature · doi:10.1038/nature04209 · Cited by 2,904 · see citing works

Genome-wide association defines more than 30 distinct susceptibility loci for Crohn's disease

the NIDDK IBD Genetics Consortium, Barrett, J. C., the Belgian-French IBD Consortium, the Wellcome Trust Case Control Consortium, Hansoul, S., Nicolae, D. L., Cho, J. H., Duerr, R. H., Rioux, J. D., Brant, S. R., et al. (2008) · Nature Genetics · doi:10.1038/ng.175 · Cited by 2,593 · see citing works

A Human Protein-Protein Interaction Network: A Resource for Annotating the Proteome

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BIND--The Biomolecular Interaction Network Database

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Walking the Interactome for Prioritization of Candidate Disease Genes

Köhler, S., Bauer, S., Horn, D., & Robinson, P. N. (2008) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2008.02.013 · Cited by 1,268 · see citing works

BIND: the Biomolecular Interaction Network Database

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Gene prioritization through genomic data fusion

Aerts, S., Lambrechts, D., Maity, S., Loo, P. V., Coessens, B., Smet, F. D., Tranchevent, L.-C., Moor, B. D., Marynen, P., Hassan, B. A., et al. (2006) · Nature Biotechnology · doi:10.1038/nbt1203 · Cited by 891 · see citing works

The BioGRID Interaction Database: 2008 update

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Newly identified genetic risk variants for celiac disease related to the immune response

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Network‐based global inference of human disease genes

Wu, X., Jiang, R., Zhang, M. Q., & Li, S. (2008) · Molecular Systems Biology · doi:10.1038/msb.2008.27 · Cited by 654 · see citing works

Human protein reference database as a discovery resource for proteomics

Peri, S. (2003) · Nucleic Acids Research · doi:10.1093/nar/gkh070 · Cited by 606 · see citing works

Common variants in the NLRP3 region contribute to Crohn's disease susceptibility

Villani, A., Lemire, M., Fortin, G., Louis, E. J., Silverberg, M. S., Collette, C., Baba, N., Libioulle, C., Bélaïche, J., Bitton, A., et al. (2008) · Nature Genetics · doi:10.1038/ng.285 · Cited by 526 · see citing works

Discovering disease-genes by topological features in human protein–protein interaction network

Xu, J., & Li, Y. (2006) · Bioinformatics · doi:10.1093/bioinformatics/btl467 · Cited by 461 · see citing works

Genetic determinants of ulcerative colitis include the ECM1 locus and five loci implicated in Crohn's disease

Fisher, S. A., Tremelling, M., Anderson, C. A., Gwilliam, R., Bumpstead, S., Prescott, N. J., Nimmo, E. R., Massey, D. C. O., Berzuini, C., Johnson, C., et al. (2008) · Nature Genetics · doi:10.1038/ng.145 · Cited by 424 · see citing works

Algorithms for estimating relative importance in networks

White, S., & Smyth, P. (2003) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/956750.956782 · Cited by 410 · see citing works

Human disease genes

Jiménez‐Sánchez, G., Childs, B., & Valle, D. L. (2001) · Nature · doi:10.1038/35057050 · Cited by 381 · see citing works

Disease candidate gene identification and prioritization using protein interaction networks

Chen, J., Aronow, B. J., & Jegga, A. G. (2009) · BMC Bioinformatics · doi:10.1186/1471-2105-10-73 · Cited by 366 · see citing works

Candidate Gene Identification Approach: Progress and Challenges

Zhu, M., & Zhao, S. (2007) · International Journal of Biological Sciences · doi:10.7150/ijbs.3.420 · Cited by 360 · see citing works

Replication of signals from recent studies of Crohn's disease identifies previously unknown disease loci for ulcerative colitis

Franke, A., Balschun, T. C., Karlsen, T. H., Hedderich, J., May, S., Lu, T. T., Schuldt, D., Nikolaus, S., Rosenstiel, P. C., Krawczak, M., et al. (2008) · Nature Genetics · doi:10.1038/ng.148 · Cited by 352 · see citing works

A common MYBPC3 (cardiac myosin binding protein C) variant associated with cardiomyopathies in South Asia

Dhandapany, P. S., Sadayappan, S., Xue, Y., Powell, G. T., Rani, D. S., Nallari, P., Singh, T., Khullar, M., Soares, P. A., Bahl, A., et al. (2009) · Nature Genetics · doi:10.1038/ng.309 · Cited by 285 · see citing works

Improved human disease candidate gene prioritization using mouse phenotype

Chen, J., Xu, H., Aronow, B. J., & Jegga, A. G. (2007) · BMC Bioinformatics · doi:10.1186/1471-2105-8-392 · Cited by 284 · see citing works

Mutation of the gene encoding the ROR2 tyrosine kinase causes autosomal recessive Robinow syndrome

van Bokhoven, H., Celli, J., Kayserili, H., van Beusekom, E., Balcı, S., Brussel, W., Skovby, F., Kerr, B. A., Perçin, E. F., Akarsu, N. A., et al. (2000) · Nature Genetics · doi:10.1038/78113 · Cited by 271 · see citing works

Speeding disease gene discovery by sequence based candidate prioritization

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SUSPECTS: enabling fast and effective prioritization of positional candidates

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POCUS: mining genomic sequence annotation to predict disease genes

Turner, F. S., Clutterbuck, D. R., & Semple, C. A. M. (2003) · Genome biology · doi:10.1186/gb-2003-4-11-r75 · Cited by 225 · see citing works

Replication and extension of genome-wide association study results for obesity in 4923 adults from northern Sweden

Renström, F., Payne, F., Nordström, A., Brito, E. C., Rolandsson, O., Hallmans, G., Barroso, I., Nordström, P., & Franks, P. W. (2009) · Human Molecular Genetics · doi:10.1093/hmg/ddp041 · Cited by 225 · see citing works

A similarity-based method for genome-wide prediction of disease-relevant human genes

Freudenberg, J., & Propping, P. (2002) · Bioinformatics · doi:10.1093/bioinformatics/18.suppl_2.s110 · Cited by 218 · see citing works

ENDEAVOUR update: a web resource for gene prioritization in multiple species

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Integration of text- and data-mining using ontologies successfully selects disease gene candidates

Tiffin, N. (2005) · Nucleic Acids Research · doi:10.1093/nar/gki296 · Cited by 210 · see citing works

Disruption of Abcc6 in the mouse: novel insight in the pathogenesis of pseudoxanthoma elasticum

Gorgels, T. G. M. F., Hu, X., Scheffer, G. L., van der Wal, A. C., Toonstra, J., de Jong, P. T. V. M., Kuppevelt, T. H. V., Levelt, C. N., de Wolf, A., Loves, W. J. P., et al. (2005) · Human Molecular Genetics · doi:10.1093/hmg/ddi183 · Cited by 206 · see citing works

Protein interactions and disease: computational approaches to uncover the etiology of diseases

Kann, M. G. (2007) · Briefings in Bioinformatics · doi:10.1093/bib/bbm031 · Cited by 202 · see citing works

Convergent functional genomics of genome‐wide association data for bipolar disorder: Comprehensive identification of candidate genes, pathways and mechanisms

Le‐Niculescu, H., Patel, S. D., Bhat, M., Kuczenski, R., Faraone, S. V., Tsuang, M. T., McMahon, F. J., Schork, N. J., Nurnberger, J. I., & Niculescu, A. B. (2009) · American Journal of Medical Genetics Part B: Neuropsychiatric Genetics · doi:10.1002/ajmg.b.30887 · Cited by 201 · see citing works

Exploration of biological network centralities with CentiBiN

Junker, B. H., Koschützki, D., & Schreiber, F. (2006) · BMC Bioinformatics · doi:10.1186/1471-2105-7-219 · Cited by 200 · see citing works

Analysis of protein sequence and interaction data for candidate disease gene prediction

George, R. A., Liu, J. Y., Feng, L. L., Bryson‐Richardson, R. J., Fatkin, D., & Wouters, M. A. (2006) · Nucleic Acids Research · doi:10.1093/nar/gkl707 · Cited by 155 · see citing works

Genes2Networks: connecting lists of gene symbols using mammalian protein interactions databases

Berger, S., Posner, J. M., & Ma’ayan, A. (2007) · BMC Bioinformatics · doi:10.1186/1471-2105-8-372 · Cited by 150 · see citing works

MINING ALZHEIMER DISEASE RELEVANT PROTEINS FROM INTEGRATED PROTEIN INTERACTOME DATA

Chen, J. Y., Shen, C., & Sivachenko, A. (2005) · Pacific Symposium on Biocomputing (PSB) · doi:10.1142/9789812701626_0034 · Cited by 148 · see citing works

Computational disease gene identification: a concert of methods prioritizes type 2 diabetes and obesity candidate genes

Tiffin, N. (2006) · Nucleic Acids Research · doi:10.1093/nar/gkl381 · Cited by 144 · see citing works

Fuzzy Measures on the Gene Ontology for Gene Product Similarity

Popescu, M., Keller, J. M., & Mitchell, J. A. (2006) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2006.37 · Cited by 141 · see citing works

Human disease genes: patterns and predictions

Smith, N. G. C., & Eyre‐Walker, A. (2003) · Gene · doi:10.1016/s0378-1119(03)00772-8 · Cited by 99 · see citing works

Identification of candidate disease genes by integrating Gene Ontologies and protein-interaction networks: case study of primary immunodeficiencies

Ortutay, C., & Vihinen, M. (2008) · Nucleic Acids Research · doi:10.1093/nar/gkn982 · Cited by 86 · see citing works

Prioritization of Positional Candidate Genes Using Multiple Web-Based Software Tools

Thornblad, T., Elliott, K., Jowett, J. B. M., & Visscher, P. M. (2007) · Twin Research and Human Genetics · doi:10.1375/twin.10.6.861 · Cited by 33 · see citing works

Murine genetic models of human disease

Clarke, A. R. (1994) · Current Opinion in Genetics & Development · doi:10.1016/0959-437x(94)90035-3 · Cited by 17 · see citing works

Enrichr: a comprehensive gene set enrichment analysis web server 2016 update

Kuleshov, M. V., Jones, M. R., Rouillard, A. D., Fernandez, N. F., Duan, Q., Wang, Z., Koplev, S., Jenkins, S. L., Jagodnik, K. M., Lachmann, A., et al. (2016) · Nucleic Acids Research · doi:10.1093/nar/gkw377 · Cited by 10,799 · see citing works

Large-Scale Exome Sequencing Study Implicates Both Developmental and Functional Changes in the Neurobiology of Autism

Satterstrom, F. K., Kosmicki, J. A., Wang, J., Breen, M. S., De Rubeis, S., An, J.-Y., Peng, M., Collins, R., Grove, J., Klei, L., et al. (2020) · Cell · doi:10.1016/j.cell.2019.12.036 · Cited by 2,321 · see citing works

Transcriptome-Based Network Analysis Reveals a Spectrum Model of Human Macrophage Activation

Xue, J., Schmidt, S. V., Sander, J., Draffehn, A., Krebs, W., Quester, I., De Nardo, D., Gohel, T. D., Emde, M., Schmidleithner, L., et al. (2014) · Immunity · doi:10.1016/j.immuni.2014.01.006 · Cited by 2,210 · see citing works

Cells of the adult human heart

Litviňuková, M., Talavera-López, C., Maatz, H., Reichart, D., Worth, C. L., Lindberg, E. L., Kanda, M., Polanski, K., Heinig, M., Lee, M., et al. (2020) · Nature · doi:10.1038/s41586-020-2797-4 · Cited by 1,729 · see citing works

APOE4 Causes Widespread Molecular and Cellular Alterations Associated with Alzheimer’s Disease Phenotypes in Human iPSC-Derived Brain Cell Types

Lin, Y.-T., Seo, J., Gao, F., Feldman, H. M., Wen, H.-L., Penney, J., Cam, H. P., Gjoneska, E., Raja, W. K., Cheng, J., et al. (2018) · Neuron · doi:10.1016/j.neuron.2018.05.008 · Cited by 1,125 · see citing works

Gene co-expression analysis for functional classification and gene–disease predictions

van Dam, S., Võsa, U., van der Graaf, A., Franke, L., & de Magalhães, J. P. (2017) · Briefings in Bioinformatics · doi:10.1093/bib/bbw139 · Cited by 1,086 · see citing works

High-Spatial-Resolution Multi-Omics Sequencing via Deterministic Barcoding in Tissue

Liu, Y., Yang, M., Deng, Y., Su, G., Enninful, A., Guo, C. C., Tebaldi, T., Zhang, D., Kim, D., Bai, Z., et al. (2020) · Cell · doi:10.1016/j.cell.2020.10.026 · Cited by 966 · see citing works

Machine learning for integrating data in biology and medicine: principles, practice, and opportunities

Zitnik, M., Nguyen, F., Wang, B., Leskovec, J., Goldenberg, A., & Hoffman, M. M. (2019) · Information Fusion · doi:10.1016/j.inffus.2018.09.012 · Cited by 675 · see citing works

Systematic assessment of tissue dissociation and storage biases in single-cell and single-nucleus RNA-seq workflows

Denisenko, E., Guo, B. B., Jones, M., Hou, R., de Kock, L., Lassmann, T., Poppe, D., Clément, O., Simmons, R. K., Lister, R., et al. (2020) · Genome Biology · doi:10.1186/s13059-020-02048-6 · Cited by 686 · see citing works

Hierarchy of transcriptomic specialization across human cortex captured by structural neuroimaging topography

Burt, J. B., Demirtaş, M., Eckner, W. J., Navejar, N. M., Ji, J. L., Martin, W. J., Bernacchia, A., Anticevic, A., & Murray, J. D. (2018) · Nature Neuroscience · doi:10.1038/s41593-018-0195-0 · Cited by 657 · see citing works

Canonical genetic signatures of the adult human brain

Hawrylycz, M., Miller, J. A., Menon, V., Feng, D., Dolbeare, T., Guillozet-Bongaarts, A. L., Jegga, A. G., Aronow, B. J., Lee, C.-K., Bernard, A., et al. (2015) · Nature Neuroscience · doi:10.1038/nn.4171 · Cited by 663 · see citing works

Broad defects in the energy metabolism of leukocytes underlie immunoparalysis in sepsis

Cheng, S.-C., Scicluna, B. P., Arts, R. J. W., Gresnigt, M. S., Lachmandas, E., Giamarellos-Bourboulis, E. J., Kox, M., Manjeri, G. R., Wagenaars, J. A. L., Cremer, O. L., et al. (2016) · Nature Immunology · doi:10.1038/ni.3398 · Cited by 637 · see citing works

Single-nucleus and single-cell transcriptomes compared in matched cortical cell types

Bakken, T. E., Hodge, R. D., Miller, J. A., Yao, Z., Nguyen, T. N., Aevermann, B., Barkan, E., Bertagnolli, D., Casper, T., Dee, N., et al. (2018) · PLoS ONE · doi:10.1371/journal.pone.0209648 · Cited by 608 · see citing works

Distinct Mesenchymal Lineages and Niches Promote Epithelial Self-Renewal and Myofibrogenesis in the Lung

Zepp, J. A., Zacharias, W. J., Frank, D. B., Cavanaugh, C. A., Zhou, S., Morley, M. P., & Morrisey, E. E. (2017) · Cell · doi:10.1016/j.cell.2017.07.034 · Cited by 567 · see citing works

Maturation and circuit integration of transplanted human cortical organoids

Revah, O., Gore, F., Kelley, K. W., Andersen, J., Sakai, N., Chen, X., Li, M.-Y., Birey, F., Yang, X., Saw, N. L., et al. (2022) · Nature · doi:10.1038/s41586-022-05277-w · Cited by 504 · see citing works

Human cardiac organoids for the modelling of myocardial infarction and drug cardiotoxicity

Richards, D. J., Li, Y., Kerr, C. M., Yao, J., Beeson, G. C., Coyle, R. C., Chen, X., Jia, J., Damon, B., Wilson, R., et al. (2020) · Nature Biomedical Engineering · doi:10.1038/s41551-020-0539-4 · Cited by 439 · see citing works

Nuclear FAK Controls Chemokine Transcription, Tregs, and Evasion of Anti-tumor Immunity

Serrels, A., Lund, T., Serrels, B., Byron, A., McPherson, R. C., von Kriegsheim, A., Gómez-Cuadrado, L., Canel, M., Muir, M., Ring, J. E., et al. (2015) · Cell · doi:10.1016/j.cell.2015.09.001 · Cited by 407 · see citing works

Computational tools for prioritizing candidate genes: boosting disease gene discovery

Moreau, Y., & Tranchevent, L.-C. (2012) · Nature Reviews Genetics · doi:10.1038/nrg3253 · Cited by 417 · see citing works

Integrating host response and unbiased microbe detection for lower respiratory tract infection diagnosis in critically ill adults

Langelier, C., Kalantar, K. L., Moazed, F., Wilson, M. R., Crawford, E. D., Deiss, T., Belzer, A., Bolourchi, S., Caldera, S., Fung, M., et al. (2018) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1809700115 · Cited by 411 · see citing works

Gaining comprehensive biological insight into the transcriptome by performing a broad-spectrum RNA-seq analysis

Sahraeian, S. M. E., Mohiyuddin, M., Sebra, R., Tilgner, H., Afshar, P. T., Au, K. F., Bani Asadi, N., Gerstein, M. B., Wong, W. H., Snyder, M. P., et al. (2017) · Nature Communications · doi:10.1038/s41467-017-00050-4 · Cited by 376 · see citing works

<i>CHD8</i> regulates neurodevelopmental pathways associated with autism spectrum disorder in neural progenitors

Sugathan, A., Biagioli, M., Golzio, C., Erdin, S., Blumenthal, I., Manavalan, P., Ragavendran, A., Brand, H., Lucente, D., Miles, J., et al. (2014) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1405266111 · Cited by 342 · see citing works

Small molecules enable highly efficient neuronal conversion of human fibroblasts.

Ladewig, J., Mertens, J., Kesavan, J., Doerr, J., Poppe, D., Glaue, F., Herms, S., Wernet, P., Kögler, G., Müller, F.-J., et al. (2012) · Nature Methods · doi:10.1038/nmeth.1972 · Cited by 316 · see citing works

Human neocortical expansion involves glutamatergic neuron diversification

Berg, J., Sorensen, S. A., Ting, J. T., Miller, J. A., Chartrand, T., Buchin, A., Bakken, T. E., Budzillo, A., Dee, N., Ding, S.-L., et al. (2021) · Nature · doi:10.1038/s41586-021-03813-8 · Cited by 285 · see citing works

SALMON: survival analysis learning with multi-omics neural networks on breast cancer

Huang, Z., Zhan, X., Xiang, S., Johnson, T. S., Helm, B., Yu, C. Y., Zhang, J., Salama, P., Rizkalla, M., Han, Z., et al. (2019) · Frontiers in Genetics · doi:10.3389/fgene.2019.00166 · Cited by 262 · see citing works

CD38-NAD+Axis Regulates Immunotherapeutic Anti-Tumor T Cell Response

Chatterjee, S., Daenthanasanmak, A., Chakraborty, P., Wyatt, M. W., Dhar, P., Selvam, S. P., Fu, J., Zhang, J., Nguyen, H., Kang, I., et al. (2018) · Cell Metabolism · doi:10.1016/j.cmet.2017.10.006 · Cited by 270 · see citing works

The Human Cell Atlas bone marrow single-cell interactive web portal

Hay, S. B., Ferchen, K., Chetal, K., Grimes, H. L., & Salomonis, N. (2018) · Experimental Hematology · doi:10.1016/j.exphem.2018.09.004 · Cited by 256 · see citing works

A general approach for detecting expressed mutations in AML cells using single cell RNA-sequencing

Petti, A. A., Williams, S. R., Miller, C. A., Fiddes, I. T., Srivatsan, S. N., Chen, D. Y., Fronick, C. C., Fulton, R. S., Church, D. M., & Ley, T. J. (2019) · Nature Communications · doi:10.1038/s41467-019-11591-1 · Cited by 215 · see citing works

Regulatory variants at KLF14 influence type 2 diabetes risk via a female-specific effect on adipocyte size and body composition

Small, K. S., Todorčević, M., Civelek, M., El-Sayed Moustafa, J. S., Wang, X., Simon, M. M., Fernandez-Tajes, J., Mahajan, A., Horikoshi, M., Hugill, A., et al. (2018) · Nature Genetics · doi:10.1038/s41588-018-0088-x · Cited by 168 · see citing works

Variation among intact tissue samples reveals the core transcriptional features of human CNS cell classes

Kelley, K. W., Nakao-Inoue, H., Molofsky, A. V., & Oldham, M. C. (2018) · Nature Neuroscience · doi:10.1038/s41593-018-0216-z · Cited by 198 · see citing works

Sodium butyrate rescues dopaminergic cells from alpha-synuclein-induced transcriptional deregulation and DNA damage

Paiva, I., Pinho, R., Pavlou, M. A., Hennion, M., Wales, P., Schütz, A.-L., Rajput, A., Szegő, É. M., Kerimoglu, C., Gerhardt, E., et al. (2017) · Human Molecular Genetics · doi:10.1093/hmg/ddx114 · Cited by 185 · see citing works

SLICE: determining cell differentiation and lineage based on single cell entropy

Guo, M., Bao, E. L., Wagner, M., Whitsett, J. A., & Xu, Y. (2016) · Nucleic Acids Research · doi:10.1093/nar/gkw1278 · Cited by 178 · see citing works

Improving the diagnostic yield of exome- sequencing by predicting gene–phenotype associations using large-scale gene expression analysis

Deelen, P., van Dam, S., Herkert, J. C., Karjalainen, J. M., Brugge, H., Abbott, K. M., van Diemen, C. C., van der Zwaag, P. A., Gerkes, E. H., Zonneveld-Huijssoon, E., et al. (2019) · Nature Communications · doi:10.1038/s41467-019-10649-4 · Cited by 138 · see citing works

YAP1/TAZ-TEAD transcriptional networks maintain skin homeostasis by regulating cell proliferation and limiting KLF4 activity

Yuan, Y., Park, J., Feng, A., Awasthi, P., Wang, Z., Chen, Q., & Iglesias-Bartolome, R. (2020) · Nature Communications · doi:10.1038/s41467-020-15301-0 · Cited by 137 · see citing works

DSCAM/PAK1 pathway suppression reverses neurogenesis deficits in iPSC-derived cerebral organoids from patients with Down syndrome

Tang, X.-Y., Xu, L., Wang, J., Hong, Y., Wang, Y., Zhu, Q., Wang, D., Zhang, X.-Y., Liu, C.-Y., Fang, K.-H., et al. (2021) · Journal of Clinical Investigation · doi:10.1172/jci135763 · Cited by 143 · see citing works

Gene expression links functional networks across cortex and striatum

Anderson, K. M., Krienen, F. M., Choi, E. Y., Reinen, J. M., Yeo, B. T. T., & Holmes, A. J. (2018) · Nature Communications · doi:10.1038/s41467-018-03811-x · Cited by 138 · see citing works

Pharmacological inhibition of apical sodium-dependent bile acid transporter changes bile composition and blocks progression of sclerosing cholangitis in multidrug resistance 2 knockout mice

Miethke, A. G., Zhang, W., Simmons, J., Taylor, A. E., Shi, T., Shanmukhappa, S. K., Karns, R., White, S., Jegga, A. G., Lages, C. S., et al. (2016) · Hepatology · doi:10.1002/hep.27973 · Cited by 141 · see citing works

Neuropathological and transcriptomic characteristics of the aged brain

Miller, J. A., Guillozet-Bongaarts, A., Gibbons, L. E., Postupna, N., Renz, A., Beller, A. E., Sunkin, S. M., Ng, L., Rose, S. E., Smith, K. A., et al. (2017) · eLife · doi:10.7554/elife.31126 · Cited by 134 · see citing works

Defining the molecular character of the developing and adult kidney podocyte

Brunskill, E. W., Georgas, K., Rumballe, B., Little, M. H., & Potter, S. S. (2011) · PLoS ONE · doi:10.1371/journal.pone.0024640 · Cited by 128 · see citing works

Genome-wide gene expression analysis in cancer cells reveals 3D growth to affect ECM and processes associated with cell adhesion but not DNA repair

Zschenker, O., Streichert, T., Hehlgans, S., & Cordes, N. (2012) · PLoS ONE · doi:10.1371/journal.pone.0034279 · Cited by 129 · see citing works

Transcriptome organization for chronic alcohol abuse in human brain

Farris, S. P., Arasappan, D., Hunicke-Smith, S., Harris, R. A., & Mayfield, R. D. (2015) · Molecular Psychiatry · doi:10.1038/mp.2014.159 · Cited by 111 · see citing works

Network-based screen in iPSC-derived cells reveals therapeutic candidate for heart valve disease

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Multi-omics analysis of multiple missions to space reveal a theme of lipid dysregulation in mouse liver

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