Enabling Precision Cardiology Through Multiscale Biology and Systems Medicine
Open accessAbstract
The traditional paradigm of cardiovascular disease research derives insight from large-scale, broadly inclusive clinical studies of well-characterized pathologies. These insights are then put into practice according to standardized clinical guidelines. However, stagnation in the development of new cardiovascular therapies and variability in therapeutic response implies that this paradigm is insufficient for reducing the cardiovascular disease burden. In this state-of-the-art review, we examine 3 interconnected ideas we put forth as key concepts for enabling a transition to precision cardiology: 1) precision characterization of cardiovascular disease with machine learning methods; 2) the application of network models of disease to embrace disease complexity; and 3) using insights from the previous 2 ideas to enable pharmacology and polypharmacology systems for more precise drug-to-patient matching and patient-disease stratification. We conclude by exploring the challenges of applying a precision approach to cardiology, which arise from a deficit of the required resources and infrastructure, and emerging evidence for the clinical effectiveness of this nascent approach.
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Statistical machine learning methods have been used to interrogate, model, and learn from complex multi-omics data to help address uncertainties about the connections between different types of data.
“Statistical machine learning methods have also been used to interrogate, model, and learn from complex multi-omics data to help to address uncertainties about the connections between different types of data39.”
From Exploiting machine learning for end-to-end drug discovery and development (2019) · cites this paper as [39]
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Cerami, E., Gao, J., Dogrusoz, U., Gross, B. E., Sumer, S. O., Aksoy, B. A., Jacobsen, A., Byrne, C. J., Heuer, M. L., Larsson, E., et al. (2012) · Cancer Discovery · doi:10.1158/2159-8290.cd-12-0095 · Cited by 17,455 · see citing works
Yusuf, S., Hawken, S., Ôunpuu, S., Dans, T., Avezum, A., Lanas, F., McQueen, M., Budaj, A., Pais, P., Varigos, J., et al. (2004) · The Lancet · doi:10.1016/s0140-6736(04)17018-9 · Cited by 12,026 · see citing works
James, P. A., Oparil, S., Carter, B. L., Cushman, W. C., Dennison-Himmelfarb, C., Handler, J., Lackland, D. T., LeFevre, M. L., MacKenzie, T. D., Ogedegbe, O., et al. (2014) · JAMA · doi:10.1001/jama.2013.284427 · Cited by 7,950 · see citing works
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 · see citing works
A New Initiative on Precision Medicine
Collins, F. S., & Varmus, H. (2015) · New England Journal of Medicine · doi:10.1056/nejmp1500523 · Cited by 5,202 · see citing works
The Effect of Carvedilol on Morbidity and Mortality in Patients with Chronic Heart Failure
Packer, M., Bristow, M. R., Cohn, J. N., Colucci, W. S., Fowler, M. B., Gilbert, E. M., & Shusterman, N. H. (1996) · New England Journal of Medicine · doi:10.1056/nejm199605233342101 · Cited by 4,649 · see citing works
Network pharmacology: the next paradigm in drug discovery
Hopkins, A. L. (2008) · Nature Chemical Biology · doi:10.1038/nchembio.118 · Cited by 4,622 · see citing works
Charles, C., Gafni, A., & Whelan, T. (1997) · Social Science & Medicine · doi:10.1016/s0277-9536(96)00221-3 · Cited by 4,137 · see citing works
Lung cancer: current therapies and new targeted treatments
Hirsch, F. R., Scagliotti, G. V., Mulshine, J. L., Kwon, R., Curran, W. J., Wu, Y.-L. L. C., & Paz-Ares, L. G. (2016) · The Lancet · doi:10.1016/s0140-6736(16)30958-8 · Cited by 3,278 · see citing works
Heidenreich, P. A., Trogdon, J. G., Khavjou, O. A., Butler, J., Dracup, K., Ezekowitz, M. D., Finkelstein, E. A., Hong, Y., Johnston, S. C., Khera, A., et al. (2011) · Circulation · doi:10.1161/cir.0b013e31820a55f5 · Cited by 3,208 · see citing works
Visscher, P. M., Brown, M. A., McCarthy, M. I., & Yang, J. (2012) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2011.11.029 · Cited by 2,425 · see citing works
Heart Rate, Life Expectancy and the Cardiovascular System: Therapeutic Considerations
Boudoulas, K. D., BORER, J. S., & Boudoulas, H. K. (2015) · Cardiology · doi:10.1159/000435947 · Cited by 1,952 · see citing works
AHA Guidelines for Primary Prevention of Cardiovascular Disease and Stroke: 2002 Update
Pearson, T. A., Blair, S. N., Daniels, S. R., Eckel, R. H., Fair, J. M., Fortmann, S. P., Franklin, B. A., Goldstein, L. B., Greenland, P., Grundy, S. M., et al. (2002) · Circulation · doi:10.1161/01.cir.0000020190.45892.75 · Cited by 1,852 · see citing works
Interactome Networks and Human Disease
Vidal, M., Cusick, M. E., & Barabási, A.-L. (2011) · Cell · doi:10.1016/j.cell.2011.02.016 · Cited by 1,771 · see citing works
Miotto, R., Li, L., Kidd, B. A., & Dudley, J. T. (2016) · Scientific Reports · doi:10.1038/srep26094 · Cited by 1,710 · see citing works
Self-citationDeep learning for digital pathology image analysis: A comprehensive tutorial with selected use cases
Janowczyk, A. R., & Madabhushi, A. (2016) · Journal of Pathology Informatics · doi:10.4103/2153-3539.186902 · Cited by 1,355 · see citing works
Houle, D., Govindaraju, D. R., & Omholt, S. W. (2010) · Nature Reviews Genetics · doi:10.1038/nrg2897 · Cited by 1,276 · see citing works
Multi-target therapeutics: when the whole is greater than the sum of the parts
Zimmermann, G. R., Lehár, J., & Keith, C. T. (2007) · Drug Discovery Today · doi:10.1016/j.drudis.2006.11.008 · Cited by 1,105 · see citing works
Using recurrent neural network models for early detection of heart failure onset
Choi, E., Schuetz, A., Stewart, W. F., & Sun, J. (2017) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocw112 · Cited by 928 · see citing works
The productivity crisis in pharmaceutical R&D
Pammolli, F., Magazzini, L., & Riccaboni, M. (2011) · Nature Reviews Drug Discovery · doi:10.1038/nrd3405 · Cited by 918 · see citing works
Decline in Rates of Death and Heart Failure in Acute Coronary Syndromes, 1999-2006
Fox, K. A. A., Steg, P. G., Eagle, K. A., Goodman, S. G., Anderson, F. A., Granger, C. B., Flather, M. D., Budaj, A., Quill, A. L., Gore, J. M., et al. (2007) · JAMA · doi:10.1001/jama.297.17.1892 · Cited by 874 · see citing works
National Human Genome Research Institute
National Cancer Institute (2020) · Definitions · doi:10.32388/mrapt2 · Cited by 872 · see citing works
Sirota, M., Dudley, J. T., Kim, J., Chiang, A. P.-F., Morgan, A. A., Sweet-Cordero, A., Sage, J., & Butte, A. J. (2011) · Science Translational Medicine · doi:10.1126/scitranslmed.3001318 · Cited by 801 · see citing works
Self-citationDeep Learning in Drug Discovery
Gawehn, E., Hiss, J. A., & Schneider, G. (2016) · Molecular Informatics · doi:10.1002/minf.201501008 · Cited by 723 · see citing works
Social Network Analysis: Foundations and Frontiers on Advantage
Burt, R. S., Kilduff, M. J., & Tasselli, S. (2013) · Annual Review of Psychology · doi:10.1146/annurev-psych-113011-143828 · Cited by 708 · see citing works
The Electronic Medical Records and Genomics (eMERGE) Network: past, present, and future
Gottesman, O., Kuivaniemi, H., Tromp, G. C., Faucett, W. A., Li, R., Manolio, T. A., Sanderson, S. C., Kannry, J. L., Zinberg, R. E., Basford, M., et al. (2013) · Genetics in Medicine · doi:10.1038/gim.2013.72 · Cited by 698 · see citing works
Clinical assessment incorporating a personal genome
Ashley, E. A., Butte, A. J., Wheeler, M. T., Chen, R., Klein, T. E., Dewey, F. E., Dudley, J. T., Ormond, K. E., Pavlovic, A., Morgan, A. A., et al. (2010) · The Lancet · doi:10.1016/s0140-6736(10)60452-7 · Cited by 674 · see citing works
Self-citationSildenafil: from angina to erectile dysfunction to pulmonary hypertension and beyond
Ghofrani, H. A., Osterloh, I. H., & Grimminger, F. (2006) · Nature Reviews Drug Discovery · doi:10.1038/nrd2030 · Cited by 632 · see citing works
Guttmacher, A. E., & Collins, F. S. (2002) · New England Journal of Medicine · doi:10.1056/nejmra012240 · Cited by 630 · see citing works
Aliper, A., Plis, S., Artemov, A., Ulloa, A., Mamoshina, P., & Zhavoronkov, A. (2016) · Molecular Pharmaceutics · doi:10.1021/acs.molpharmaceut.6b00248 · Cited by 610 · see citing works
Evolution of biological complexity
Adami, C., Ofria, C., & Collier, T. C. (2000) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.97.9.4463 · Cited by 592 · see citing works
Ho, P. M., Magid, D. J., Shetterly, S. M., Olson, K. L., Maddox, T. M., Peterson, P. N., Masoudi, F. A., & Rumsfeld, J. S. (2008) · American Heart Journal · doi:10.1016/j.ahj.2007.12.011 · Cited by 587 · see citing works
Recent trends in cardiovascular mortality in the United States and public health goals.
Sidney, S., Quesenberry, C. P., Jaffe, M. G., Sorel, M., Nguyen-Huynh, M. N., Kushi, L. H., Go, A. S., & Rana, J. S. (2016) · JAMA Cardiology · doi:10.1001/jamacardio.2016.1326 · Cited by 569 · see citing works
The Precision Medicine Initiative
Ashley, E. A. (2015) · JAMA · doi:10.1001/jama.2015.3595 · Cited by 562 · see citing works
Goldstein, B. A., Návar, A. M., & Carter, R. E. (2016) · European Heart Journal · doi:10.1093/eurheartj/ehw302 · Cited by 551 · see citing works
Millan, M. J. (2006) · Pharmacology & Therapeutics · doi:10.1016/j.pharmthera.2005.11.006 · Cited by 549 · see citing works
Identification of type 2 diabetes subgroups through topological analysis of patient similarity
Li, L., Cheng, W., Glicksberg, B. S., Gottesman, O., Tamler, R., Chen, R., Böttinger, E. P., & Dudley, J. T. (2015) · Science Translational Medicine · doi:10.1126/scitranslmed.aaa9364 · Cited by 548 · see citing works
Self-citationAmbrosius, W. T., Sink, K. M., Foy, C. G., Berlowitz, D. R., Cheung, A. K., Cushman, W. C., Fine, L. J., Goff, D. C., Johnson, K., Killeen, A. A., et al. (2014) · Clinical Trials · doi:10.1177/1740774514537404 · Cited by 544 · see citing works
Self-citationHuynh, B. Q., Li, H., & Giger, M. L. (2016) · Journal of medical imaging · doi:10.1117/1.jmi.3.3.034501 · Cited by 541 · see citing works
Shifting from the single to the multitarget paradigm in drug discovery
Medina‐Franco, J. L., Giulianotti, M. A., Welmaker, G. S., & Houghten, R. A. (2013) · Drug Discovery Today · doi:10.1016/j.drudis.2013.01.008 · Cited by 486 · see citing works
Genomic Medicine — An Updated Primer
Feero, W. G., Guttmacher, A. E., & Collins, F. S. (2010) · New England Journal of Medicine · doi:10.1056/nejmra0907175 · Cited by 440 · see citing works
PROLONGATION OF SURVIVAL IN CONGESTIVE CARDIOMYOPATHY BY BETA-RECEPTOR BLOCKADE
Swedberg, K. B., Hjalmarson, Å. C., Waagstein, F., & Wallentin, I. (1979) · The Lancet · doi:10.1016/s0140-6736(79)92010-5 · Cited by 418 · see citing works
Narula, S., Shameer, K., Omar, A. M. S., Dudley, J. T., & Sengupta, P. P. (2016) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2016.08.062 · Cited by 414 · see citing works
Self-citationTrimethoprim-Sulfamethoxazole Revisited
Masters, P. A., O’Bryan, T. A., Zurlo, J. J., Miller, D. Q., & Joshi, N. (2003) · Archives of Internal Medicine · doi:10.1001/archinte.163.4.402 · Cited by 410 · see citing works
Ensembles of Deep Learning Architectures for the Early Diagnosis of the Alzheimer’s Disease
Ortíz, A., Munilla, J., Górriz, J. M., & Ramı́rez, J. (2016) · International Journal of Neural Systems · doi:10.1142/s0129065716500258 · Cited by 404 · see citing works
Big data analytics to improve cardiovascular care: promise and challenges
Rumsfeld, J. S., Joynt, K. E., & Maddox, T. M. (2016) · Nature Reviews Cardiology · doi:10.1038/nrcardio.2016.42 · Cited by 398 · see citing works
Challenges and opportunities of drug repositioning
Novac, N. (2013) · Trends in Pharmacological Sciences · doi:10.1016/j.tips.2013.03.004 · Cited by 376 · see citing works
PheKB: a catalog and workflow for creating electronic phenotype algorithms for transportability
Kirby, J., Speltz, P., Rasmussen, L. V., Basford, M., Gottesman, O., Peissig, P. L., Pacheco, J. A., Tromp, G. C., Pathak, J., Carrell, D., et al. (2016) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocv202 · Cited by 369 · see citing works
Unsupervised Profiling Methods for Fraud Detection
Bolton, R. J., & Hand, D. J. (2002) · Cited by 352 · see citing works
Ngo, T. A., Lu, Z., & Carneiro, G. (2016) · Medical Image Analysis · doi:10.1016/j.media.2016.05.009 · Cited by 334 · see citing works
In silico methods for drug repurposing and pharmacology
Hodos, R. A., Kidd, B. A., Shameer, K., Readhead, B., & Dudley, J. T. (2016) · WIREs Systems Biology and Medicine · doi:10.1002/wsbm.1337 · Cited by 333 · see citing works
Self-citationCoronary stents: historical development, current status and future directions
Iqbal, J., Gunn, J. P., & Serruys, P. W. J. C. (2013) · British Medical Bulletin · doi:10.1093/bmb/ldt009 · Cited by 319 · see citing works
CHARGE-Heart Failure Consortium, Surendran, P., EchoGen Consortium, METASTROKE Consortium, GIANT Consortium, EPIC-InterAct Consortium, Study, L. C., Wellcome Trust Case Control Consortium, Understanding Society Scientific Group, EPIC-CVD Consortium, et al. (2016) · Nature Genetics · doi:10.1038/ng.3654 · Cited by 312 · see citing works
DeepChrome: deep-learning for predicting gene expression from histone modifications
Singh, R., Lanchantin, J., Robins, G., & Qi, Y. (2016) · Bioinformatics · doi:10.1093/bioinformatics/btw427 · Cited by 303 · see citing works
Drug repositioning for Alzheimer's disease
Corbett, A., Pickett, J., Burns, A. S., Corcoran, J. P. T., Dunnett, S. B., Edison, P., Hagan, J. J., Holmes, C., Jones, E., Katona, C., et al. (2012) · Nature Reviews Drug Discovery · doi:10.1038/nrd3869 · Cited by 302 · see citing works
Drug repurposing in oncology—patient and health systems opportunities
Bertolini, F., Sukhatme, V. P., & Bouche, G. (2015) · Nature Reviews Clinical Oncology · doi:10.1038/nrclinonc.2015.169 · Cited by 289 · see citing works
Franzén, O., Ermel, R., Cohain, A., Akers, N. K., Di Narzo, A., Talukdar, H. A., Foroughi-Asl, H., Giambartolomei, C., Fullard, J. F., Sukhavasi, K., et al. (2016) · Science · doi:10.1126/science.aad6970 · Cited by 288 · see citing works
Self-citationHistory of the design of captopril and related inhibitors of angiotensin converting enzyme.
Cushman, D. W., & Ondetti, M. A. (1991) · Hypertension · doi:10.1161/01.hyp.17.4.589 · Cited by 270 · see citing works
Kullo, I. J., Jouni, H., Austin, E. E., Brown, S.-A., Kruisselbrink, T. M., Isseh, I. N., Haddad, R. A., Marroush, T. S., Shameer, K., Olson, J. E., et al. (2016) · Circulation · doi:10.1161/circulationaha.115.020109 · Cited by 267 · see citing works
Self-citationClinical Implications of Chronic Heart Failure Phenotypes Defined by Cluster Analysis
Ahmad, T., Pencina, M. J., Schulte, P. J., O'Brien, E. C., Whellan, D. J., Piña, I. L., Kitzman, D. W., Lee, K. L., O’Connor, C. M., & Felker, G. M. (2014) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2014.07.979 · Cited by 262 · see citing works
Multi-Target Drugs: The Trend of Drug Research and Development
Lu, J., Pan, W., Yuanjia, H., & Wang, Y. (2012) · PLoS ONE · doi:10.1371/journal.pone.0040262 · Cited by 259 · see citing works
Żemojtel, T., Köhler, S., Mackenroth, L., Jäger, M., Hecht, J., Krawitz, P. M., Graul-Neumann, L. M., Doelken, S. C., Ehmke, N., Spielmann, M., et al. (2014) · Science Translational Medicine · doi:10.1126/scitranslmed.3009262 · Cited by 256 · see citing works
Precision medicine in cardiology
Antman, E. M., & Loscalzo, J. (2016) · Nature Reviews Cardiology · doi:10.1038/nrcardio.2016.101 · Cited by 251 · see citing works
Cost-Effectiveness of Hypertension Therapy According to 2014 Guidelines
Moran, A. E., Odden, M. C., Thanataveerat, A., Tzong, K. Y., Rasmussen, P. W., Guzman, D., Williams, L. W., Bibbins-Domingo, K. B., Coxson, P. G., & Goldman, L. (2015) · New England Journal of Medicine · doi:10.1056/nejmsa1406751 · Cited by 237 · see citing works
The Next Era: Deep Learning in Pharmaceutical Research
Ekins, S. (2016) · Pharmaceutical Research · doi:10.1007/s11095-016-2029-7 · Cited by 237 · see citing works
Topological data analysis for discovery in preclinical spinal cord injury and traumatic brain injury
Nielson, J. L., Paquette, J., Liu, A. W., Guandique, C. F., Tovar, C. A., Inoue, T., Irvine, K., Gensel, J. C., Kloke, J., Petrossian, T. C., et al. (2015) · Nature Communications · doi:10.1038/ncomms9581 · Cited by 211 · see citing works
Pw, C., Tk, C., & Nl, D. (1964) · PubMed · Cited by 206 · see citing works
Cognitive Machine-Learning Algorithm for Cardiac Imaging
Sengupta, P. P., Huang, Y., Bansal, M., Ashrafi, A., Fisher, M., Shameer, K., Gall, W., & Dudley, J. T. (2016) · Circulation Cardiovascular Imaging · doi:10.1161/circimaging.115.004330 · Cited by 205 · see citing works
Self-citationCardiovascular Drug Development
Fordyce, C. B., Roe, M. T., Ahmad, T., Libby, P., Borer, J. S., Hiatt, W. R., Bristow, M. R., Packer, M., Wasserman, S. M., Braunstein, N., et al. (2015) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2015.03.016 · Cited by 202 · see citing works
Li, M. J., Liu, Z., Wang, P., Wong, M. P., Nelson, M. R., Kocher, J., Yeager, M., Sham, P. C., Chanock, S. J., Xia, Z., et al. (2015) · Nucleic Acids Research · doi:10.1093/nar/gkv1317 · Cited by 186 · see citing works
Sildenafil in the treatment of pulmonary hypertension
Barnett, C. F., & Machado, R. F. (2006) · Vascular Health and Risk Management · doi:10.2147/vhrm.2006.2.4.411 · Cited by 181 · see citing works
Pleiotropic effects of statins: new therapeutic targets in drug design
Bedi, O., Dhawan, V., Sharma, P. L., & Kumar, P. (2016) · Naunyn-Schmiedeberg s Archives of Pharmacology · doi:10.1007/s00210-016-1252-4 · Cited by 178 · see citing works
Escaping the EHR Trap — The Future of Health IT
Mandl, K. D., & Kohane, I. S. (2012) · New England Journal of Medicine · doi:10.1056/nejmp1203102 · Cited by 174 · see citing works
Kessler, T., Vilne, B., & Schunkert, H. (2016) · EMBO Molecular Medicine · doi:10.15252/emmm.201506174 · Cited by 172 · see citing works
Bharadwaj, U., Eckols, T. K., Kolosov, M. I., Kasembeli, M. M., Adam, A., Torres, D., Zhang, X., Dobrolecki, L. E., Wei, W., Lewis, M. T., et al. (2014) · Oncogene · doi:10.1038/onc.2014.72 · Cited by 162 · see citing works
Deftereos, S., Giannopoulos, G., Papoutsidakis, N., Panagopoulou, V., Kossyvakis, C., Raisakis, K., Cleman, M. W., & Stefanadis, C. (2013) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2013.08.726 · Cited by 161 · see citing works
Pharmaco-miR: linking microRNAs and drug effects
Rukov, J. L., Wilentzik, R., Jaffe, I., Vinther, J., & Shomron, N. (2013) · Briefings in Bioinformatics · doi:10.1093/bib/bbs082 · Cited by 153 · see citing works
Tan, J., Ung, M. H., Cheng, C., & Greene, C. S. (2014) · Pacific Symposium on Biocomputing (PSB) · doi:10.1142/9789814644730_0014 · Cited by 152 · see citing works
Predictive computational phenotyping and biomarker discovery using reference-free genome comparisons
Drouin, A., Giguère, S., Déraspe, M., Marchand, M., Tyers, M. D., Loo, V. G., Bourgault, A., Laviolette, F., & Corbeil, J. (2016) · BMC Genomics · doi:10.1186/s12864-016-2889-6 · Cited by 150 · see citing works
Shameer, K., Denny, J. C., Ding, K., Jouni, H., Crosslin, D. R., de Andrade, M., Chute, C. G., Peissig, P. L., Pacheco, J. A., Li, R., et al. (2013) · Human Genetics · doi:10.1007/s00439-013-1355-7 · Cited by 142 · see citing works
Self-citationGenome-Wide Significant Loci: How Important Are They?
Björkegren, J., Kovacic, J. C., Dudley, J. T., & Schadt, E. E. (2015) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2014.12.033 · Cited by 141 · see citing works
Self-citationCross-Tissue Regulatory Gene Networks in Coronary Artery Disease
Talukdar, H. A., Asl, H. F., Jain, R. K., Ermel, R., Ruusalepp, A., Franzén, O., Kidd, B. A., Readhead, B., Giannarelli, C., Kovacic, J. C., et al. (2016) · Cell Systems · doi:10.1016/j.cels.2016.02.002 · Cited by 136 · see citing works
Self-citationPathogenesis of essential hypertension: historical paradigms and modern insights
Johnson, R. J., Feig, D. I., Nakagawa, T., Sánchez‐Lozada, L. G., & Rodríguez‐Iturbe, B. (2008) · Journal of Hypertension · doi:10.1097/hjh.0b013e3282f29876 · Cited by 130 · see citing works
The Effect of Beta Adrenergic Blockade on Patterns of Urinary Sodium Excretion
Epstein, S. E., & D, E. B. M. (1966) · Annals of Internal Medicine · doi:10.7326/0003-4819-65-1-20 · Cited by 126 · see citing works
Quinupristin/dalfopristin: A therapeutic review
Allington, D. R. (2001) · Clinical Therapeutics · doi:10.1016/s0149-2918(01)80028-x · Cited by 126 · see citing works
Polygenic overlap between schizophrenia risk and antipsychotic response: a genomic medicine approach
Ruderfer, D. M., Charney, A. W., Readhead, B., Kidd, B. A., Kähler, A. K., Kenny, P. J., Keiser, M. J., Moran, J. L., Hultman, C. M., Scott, S. A., et al. (2016) · The Lancet Psychiatry · doi:10.1016/s2215-0366(15)00553-2 · Cited by 122 · see citing works
Self-citationSanchez, L. S., de la Monte, S. M., Filippov, G., Jones, R. C., Zapol, W. M., & Bloch, K. D. (1998) · Pediatric Research · doi:10.1203/00006450-199802000-00002 · Cited by 119 · see citing works
Ammad-ud-din, M., Khan, S. A., Malani, D., Murumägi, A., Kallioniemi, O., Aittokallio, T. A., & Samuel Kaski, A. (2016) · Bioinformatics · doi:10.1093/bioinformatics/btw433 · Cited by 118 · see citing works
Peiris, D., Joshi, R., Webster, R. J., Groenestein, P., Usherwood, T. P., Heeley, E. L., Turnbull, F., Lipman, A., & Patel, A. A. (2009) · Journal of Medical Internet Research · doi:10.2196/jmir.1258 · Cited by 115 · see citing works
Mihos, C. G., Salas, M. J., & Santana, O. (2010) · Cardiology in Review · doi:10.1097/crd.0b013e3181f52a7f · Cited by 95 · see citing works
Systems biology approaches to adverse drug effects: the example of cardio-oncology
Brown, S.-A. N., Sandhu, N. P., & Herrmann, J. (2015) · Nature Reviews Clinical Oncology · doi:10.1038/nrclinonc.2015.168 · Cited by 95 · see citing works
Understanding Protein–Nanoparticle Interaction: A New Gateway to Disease Therapeutics
Giri, K., Shameer, K., Zimmermann, M. T., Saha, S., Chakraborty, P. K., Sharma, A., Arvizo, R. R., Madden, B. J., McCormick, D., Kocher, J.-P. A., et al. (2014) · Bioconjugate Chemistry · doi:10.1021/bc500084f · Cited by 94 · see citing works
Self-citationShameer, K., Readhead, B., & Dudley, J. T. (2015) · Current Topics in Medicinal Chemistry · doi:10.2174/1568026615666150112103510 · Cited by 92 · see citing works
Self-citationBioinformatics and Medical Informatics: Collaborations on the Road to Genomic Medicine?
Maojo, V., & Kulikowski, C. A. (2003) · Journal of the American Medical Informatics Association · doi:10.1197/jamia.m1305 · Cited by 90 · see citing works
Vural, S., Wang, X., & Guda, C. (2016) · BMC Systems Biology · doi:10.1186/s12918-016-0306-z · Cited by 80 · see citing works
The disconnect between phase II and phase III trials of drugs for heart failure
Vaduganathan, M., Greene, S. J., Ambrosy, A. P., Gheorghiade, M., & Butler, J. (2013) · Nature Reviews Cardiology · doi:10.1038/nrcardio.2012.181 · Cited by 77 · see citing works
Realism in drug discovery—could Cassandra be right?
Horrobin, D. F. (2001) · Nature Biotechnology · doi:10.1038/nbt1201-1099 · Cited by 77 · see citing works
Modern Management of Acute Myocardial Infarction
Yang, E. H., Brilakis, E. S., Reeder, G. S., & Gersh, B. J. (2006) · Current Problems in Cardiology · doi:10.1016/j.cpcardiol.2006.08.004 · Cited by 75 · see citing works
Antonopoulos, A. S., Margaritis, M., Shirodaria, C. C., & Antoniades, C. A. (2012) · Thrombosis and Haemostasis · doi:10.1160/th12-05-0337 · Cited by 73 · see citing works
Li, H., Zhao, X., Liu, Y., Zhe, M., Wang, D., Yang, F., & Shi, Q. (2016) · Cellular Physiology and Biochemistry · doi:10.1159/000447794 · Cited by 71 · see citing works
Pink, J., Pirmohamed, M., Lane, S., & Hughes, D. A. (2013) · Clinical Pharmacology & Therapeutics · doi:10.1038/clpt.2013.190 · Cited by 67 · see citing works
EHDViz: clinical dashboard development using open-source technologies
Badgeley, M. A., Shameer, K., Glicksberg, B. S., Tomlinson, M., Levin, M. A., McCormick, P. J., Kasarskis, A., Reich, D. L., & Dudley, J. T. (2016) · BMJ Open · doi:10.1136/bmjopen-2015-010579 · Cited by 65 · see citing works
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Gheorghiade, M., Larson, C. J., Shah, S. J., Greene, S. J., Cleland, J. G. F., Colucci, W. S., Dunnmon, P. M., Epstein, S. E., Kim, R. J., Parsey, R. V., et al. (2016) · Circulation Heart Failure · doi:10.1161/circheartfailure.115.002727 · Cited by 64 · see citing works
Precision medicine in NSCLC and pathology: how does ALK fit in the pathway?
Kerr, K. M., & López‐Ríos, F. (2016) · Annals of Oncology · doi:10.1093/annonc/mdw302 · Cited by 63 · see citing works
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Crepaldi, G., Scirè, C. A., Carrara, G., Sakellariou, G., Caporali, R. F., Hmamouchi, I., Dougados, M., & Montecucco, C. (2016) · PLoS ONE · doi:10.1371/journal.pone.0146991 · Cited by 59 · see citing works
Healthcare and the information age: implications for medical education
Carlile, S., & Sefton, A. J. (1998) · The Medical Journal of Australia · doi:10.5694/j.1326-5377.1998.tb138963.x · Cited by 57 · see citing works
Opportunities for the Cardiovascular Community in the Precision Medicine Initiative
Shah, S. H., Arnett, D. K., Houser, S. R., Ginsburg, G. S., MacRae, C. A., Mital, S. R., Loscalzo, J., & Hall, J. L. (2016) · Circulation · doi:10.1161/circulationaha.115.019475 · Cited by 57 · see citing works
The digital revolution in phenotyping
Oellrich, A., Collier, N., Groza, T., Rebholz‐Schuhmann, D., Shah, N. H., Bodenreider, O., Boland, M. R., Georgiev, I., Liu, H., Livingston, K., et al. (2015) · Briefings in Bioinformatics · doi:10.1093/bib/bbv083 · Cited by 50 · see citing works
Topological Data Analysis Generates High-Resolution, Genome-wide Maps of Human Recombination
Cámara, P. G., Rosenbloom, D. I. S., Emmett, K., Levine, A. J., & Rabadán, R. (2016) · Cell Systems · doi:10.1016/j.cels.2016.05.008 · Cited by 50 · see citing works
Genome-wide association studies of late-onset cardiovascular disease
Smith, J. G., & Newton‐Cheh, C. (2015) · Journal of Molecular and Cellular Cardiology · doi:10.1016/j.yjmcc.2015.04.004 · Cited by 49 · see citing works
Vaitsis, C., Nilsson, G. H., & Zary, N. (2014) · PeerJ · doi:10.7717/peerj.683 · Cited by 48 · see citing works
Kullo, I. J., Shameer, K., Jouni, H., Lesnick, T. G., Pathak, J., Chute, C. G., & de Andrade, M. (2014) · Frontiers in Genetics · doi:10.3389/fgene.2014.00166 · Cited by 47 · see citing works
Self-citationThe failure of torcetrapib: what have we learned?
JOY, T. R., & Hegele, R. A. (2008) · British Journal of Pharmacology · doi:10.1038/bjp.2008.248 · Cited by 44 · see citing works
Hinks, T., Zhou, X., Staples, K. J., Dimitrov, B. D., Manta, A., Petrossian, T. C., Lum, P. Y., Smith, C. G., Ward, J., Howarth, P. H., et al. (2015) · The Lancet · doi:10.1016/s0140-6736(15)60357-9 · Cited by 44 · see citing works
Eden, C., Johnson, K. W., Gottesman, O., Böttinger, E. P., & Abul‐Husn, N. S. (2016) · Personalized Medicine · doi:10.2217/pme.15.58 · Cited by 44 · see citing works
Self-citationGlicksberg, B. S., Li, L., Badgeley, M. A., Shameer, K., Kosoy, R., Beckmann, N. D., Pho, N. H., Hakenberg, J., Ma, M., Ayers, K. L., et al. (2016) · Bioinformatics · doi:10.1093/bioinformatics/btw282 · Cited by 44 · see citing works
Self-citationPlanning for US Precision Medicine Initiative underway
Jaffe, S. (2015) · The Lancet · doi:10.1016/s0140-6736(15)61124-2 · Cited by 43 · see citing works
Gijsberts, C., den Ruijter, H. D., Asselbergs, F., Chan, M., de Kleijn, D. D., & Hoefer, I. (2015) · Global Heart · doi:10.1016/j.gheart.2014.11.004 · Cited by 43 · see citing works
Cost-effectiveness of pharmacogenetic-guided dosing of warfarin in the United Kingdom and Sweden
Verhoef, T. I., Redekop, K., Langenskiöld, S., Kamali, F., Wadelius, M., Burnside, G., der Zee, A. H. M., Hughes, D. A., & Pirmohamed, M. (2016) · The Pharmacogenomics Journal · doi:10.1038/tpj.2016.41 · Cited by 42 · see citing works
Tang, X., Baheti, S., Shameer, K., Thompson, K. J., Wills, Q. F., Niu, N., Holcomb, I. N., Boutet, S. C., Ramakrishnan, R., Kachergus, J. M., et al. (2014) · Nucleic Acids Research · doi:10.1093/nar/gku1005 · Cited by 41 · see citing works
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Francis, G. S., Cogswell, R. J., & Thenappan, T. (2014) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2014.07.978 · Cited by 41 · see citing works
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Pjanic, M., Miller, C. L., Wirka, R. C., Kim, J. B., DiRenzo, D. M., & Quertermous, T. (2016) · Current Cardiology Reports · doi:10.1007/s11886-016-0777-y · Cited by 40 · see citing works
Cholesteryl Ester Transfer Protein Inhibition Is Not Yet Dead—Pro
Barter, P. J., & Rye, K. (2016) · Arteriosclerosis Thrombosis and Vascular Biology · doi:10.1161/atvbaha.115.306879 · Cited by 36 · see citing works
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Alfonsi, J., Hegele, R. A., & Gryn, S. E. (2016) · Current Atherosclerosis Reports · doi:10.1007/s11883-016-0573-6 · Cited by 35 · see citing works
Kleber, M. E., Goliasch, G., Grammer, T. B., Pilz, S., Tomaschitz, A., Silbernagel, G., Maurer, G., März, W., & Niessner, A. (2014) · Journal of Internal Medicine · doi:10.1111/joim.12189 · Cited by 32 · see citing works
Network Medicine in Disease Analysis and Therapeutics
Chen, B., & Butte, A. J. (2013) · Clinical Pharmacology & Therapeutics · doi:10.1038/clpt.2013.181 · Cited by 30 · see citing works
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Shameer, K., Pugalenthi, G., Kandaswamy, K. K., & Sowdhamini, R. (2011) · Protein and Peptide Letters · doi:10.2174/092986611796378729 · Cited by 30 · see citing works
Self-citationDarabi, F., Aghaei, M., Movahedian, A., Pourmoghadas, A., & Sarrafzadegan, N. (2016) · Molecular and Cellular Biochemistry · doi:10.1007/s11010-016-2805-z · Cited by 30 · see citing works
Shameer, K., Klee, E. W., Dalenberg, A. K., & Kullo, I. J. (2014) · Circulation Cardiovascular Genetics · doi:10.1161/circgenetics.113.000233 · Cited by 28 · see citing works
Self-citationAllen, N. B., Lloyd‐Jones, D. M., Hwang, S., Rasmussen‐Torvik, L. J., Fornage, M., Morrison, A. C., Baldridge, A. S., Boerwinkle, E., Levy, D. A., Cupples, L. A., et al. (2016) · American Heart Journal · doi:10.1016/j.ahj.2015.12.022 · Cited by 28 · see citing works
Genoux, A., Lichtenstein, L., Ferrières, J., Duparc, T., Bongard, V., Vervueren, P., Combes, G., Taraszkiewicz, D., Elbaz, M., Galinier, M., et al. (2016) · BMC Medicine · doi:10.1186/s12916-016-0672-9 · Cited by 28 · see citing works
You, J. H. S. (2013) · Pharmacogenetics and Genomics · doi:10.1097/fpc.0000000000000014 · Cited by 27 · see citing works
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Liang, Z., Huang, J. X., Zeng, X., & Zhang, G. (2016) · BMC Medical Genomics · doi:10.1186/s12920-016-0207-4 · Cited by 24 · see citing works
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Tashakkor, A. Y., Moghaddamjou, A., Chen, L. Y. C., & Cheung, W. Y. (2013) · Current Oncology · doi:10.3747/co.20.1470 · Cited by 23 · see citing works
Modern Management of Acute Myocardial Infarction
Welch, T. D., Yang, E. H., Reeder, G. S., & Gersh, B. J. (2012) · Current Problems in Cardiology · doi:10.1016/j.cpcardiol.2012.03.002 · Cited by 23 · see citing works
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Glicksberg, B. S., Li, L., Cheng, W., Shameer, K., Hakenberg, J., Castellanos, R., Ma, M., Shi, L., Shah, H., Dudley, J. T., et al. (2014) · Pacific Symposium on Biocomputing (PSB) · doi:10.1142/9789814644730_0039 · Cited by 22 · see citing works
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Stella, G. M., Gentile, A., Balderacchi, A. M., Meloni, F., Milan, M., & Benvenuti, S. (2016) · Journal of Translational Medicine · doi:10.1186/s12967-016-1008-4 · Cited by 22 · see citing works
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McLain, J. H., Alsterda, A. J., & Arora, R. R. (2016) · Journal of Cardiovascular Pharmacology and Therapeutics · doi:10.1177/1074248416662349 · Cited by 19 · see citing works
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Birnbaum, Y., & Ye, Y. (2012) · Current Atherosclerosis Reports · doi:10.1007/s11883-012-0232-5 · Cited by 17 · see citing works
Shameer, K., Pugalenthi, G., Kandaswamy, K. K., Suganthan, P. N., Archunan, G., & Sowdhamini, R. (2010) · Bioinformatics and Biology Insights · doi:10.4137/bbi.s4464 · Cited by 16 · see citing works
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Aelion, C. M., Airhihenbuwa, C. O., Alemagno, S. A., Amler, R. W., Arnett, D. K., Balas, E. A., Bertozzi, S., Blakely, C. H., Boerwinkle, E., Brandt‐Rauf, P. W., et al. (2016) · The Lancet Oncology · doi:10.1016/s1470-2045(16)30054-7 · Cited by 16 · see citing works
Kent, S. T., Shimbo, D., Huang, L., Diaz, K. M., Kilgore, M. L., Oparil, S., & Muntner, P. (2014) · PLoS ONE · doi:10.1371/journal.pone.0105888 · Cited by 15 · see citing works
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Chalkidou, K., & Rawlins, S. C. (2011) · Drug Discovery Today · doi:10.1016/j.drudis.2011.08.015 · Cited by 12 · see citing works
Stone, P. A., Thompson, S. N., Khan, M., Northfield, E., Schillinger, R. M., & Skaff, P. R. (2016) · Annals of Vascular Surgery · doi:10.1016/j.avsg.2016.08.004 · Cited by 12 · see citing works
CardioPulse ArticlesCardiovascular precision medicine: hope or hype?
Pitt, G. S. (2015) · European Heart Journal · doi:10.1093/eurheartj/ehv226 · Cited by 11 · see citing works
Alagappan, M., Jiang, D., Denko, N., & Koong, A. C. (2016) · Advances in Experimental Medicine and Biology · doi:10.1007/978-3-319-26666-4_15 · Cited by 10 · see citing works
Wang, H., Liao, C., Yen, T., Chen, S., Lee, L., Hsieh, C., Lin, C., & Ng, S. (2016) · Oncotarget · doi:10.18632/oncotarget.11762 · Cited by 9 · see citing works
Stamile, C., Kocevar, G., Cotton, F., Maes, F., Sappey‐Marinier, D., & Huffel, S. V. (2016) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2016.2597963 · Cited by 6 · see citing works
Association of Soluble Fibrinogen-like Protein 2 with the Severity of Coronary Artery Disease
Cheng, J., Chen, Y., Xu, B., Wu, J., & He, F. (2016) · Internal Medicine · doi:10.2169/internalmedicine.55.6149 · Cited by 5 · see citing works
Systems Biology and Clinical Phenotypes of Heart Failure Syndrome
Louridas, G. E., & Lourida, K. G. (2015) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2014.12.051 · Cited by 4 · see citing works
Cost-Effectiveness of Hypertension Therapy According to 2014 Guidelines
(2015) · New England Journal of Medicine · doi:10.1056/nejmx150016 · Cited by 4 · see citing works
Rymer, J. A., & Newby, L. K. (2015) · Circulation · doi:10.1161/circulationaha.115.015707 · Cited by 3 · see citing works
Heart Failure Update: Diagnosis and Classification.
Eckstein, D., & Korabathina, R. (2016) · PubMed · Cited by 3 · see citing works
Oncology Nurses and the Cancer Moonshot 2020
Sheldon, L. K. (2016) · Clinical journal of oncology nursing · doi:10.1188/16.cjon.355-356 · Cited by 3 · see citing works
Applying Cluster Analysis to Data of Previously Published Chronic Heart Failure Trials
Madias, J. E. (2015) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2014.11.070 · Cited by 2 · see citing works
Ahmad, T., & Felker, G. M. (2015) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2014.12.050 · Cited by 1 · see citing works
Shurlock, B. (2014) · European Heart Journal · doi:10.1093/eurheartj/ehu434 · Cited by 1 · see citing works
Cost-Effectiveness Of A Novel Pharmacist Guided Warfarin Pharmacogenetic Service
Gor, D., Kim, K., Chumnumwat, S., Galanter, W., You, J. H. S., Walton, S., Garofalo, J., Duarte, J. D., Krishnan, J. A., Bauman, J. E., et al. (2015) · Value in Health · doi:10.1016/j.jval.2015.09.866 · Cited by 1 · see citing works
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Artificial intelligence in cardiology
Johnson, K. W., Torres Soto, J., Glicksberg, B. S., Shameer, K., Miotto, R., Ali, M., Ashley, E., & Dudley, J. T. (2018) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2018.03.521 · Cited by 1,202 · see citing works
Self-citationThe ‘Digital Twin’ to enable the vision of precision cardiology
Acero, J. C., Margara, F., Marciniak, M., Rodero, C., Lončarić, F., Feng, Y., Gilbert, A., Fernandes, J. F., Bukhari, S. H. A., Wajdan, A., et al. (2020) · European Heart Journal · doi:10.1093/eurheartj/ehaa159 · Cited by 987 · see citing works
Seyhan, A. A. (2019) · Translational Medicine Communications · doi:10.1186/s41231-019-0050-7 · Cited by 731 · see citing works
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Ekins, S., Puhl, A. C., Zorn, K. M., Lane, T. R., Russo, D. P., Klein, J. J., Hickey, A. J., & Clark, A. M. (2019) · Nature Materials · doi:10.1038/s41563-019-0338-z · Cited by 578 · see citing works
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Shameer, K., Johnson, K. W., Glicksberg, B. S., Dudley, J. T., & Sengupta, P. P. (2018) · Heart · doi:10.1136/heartjnl-2017-311198 · Cited by 555 · see citing works
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Somani, S. S., Russak, A. J., Richter, F., Zhao, S., Vaid, A., Chaudhry, F. F., Freitas, J. K. D., Naik, N., Miotto, R., Nadkarni, G. N., et al. (2020) · EP Europace · doi:10.1093/europace/euaa377 · Cited by 267 · see citing works
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Bello, G. A., Dawes, T. J. W., Duan, J., Biffi, C., de Marvao, A., Howard, L. S. G. E., Gibbs, J. S. R., Wilkins, M. R., Cook, S. A., Rueckert, D., et al. (2019) · Nature Machine Intelligence · doi:10.1038/s42256-019-0019-2 · Cited by 231 · see citing works
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Kagiyama, N., Shrestha, S., Farjo, P. D., & Sengupta, P. P. (2019) · Journal of the American Heart Association · doi:10.1161/jaha.119.012788 · Cited by 183 · see citing works
Self-citationBhavnani, S. P., Parakh, K., Atreja, A., Druz, R. S., Graham, G. N., Hayek, S. S., Krumholz, H. M., Maddox, T. M., Majmudar, M. D., Rumsfeld, J. S., et al. (2017) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2017.10.018 · Cited by 162 · see citing works
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Myocardial Interstitial Fibrosis in Nonischemic Heart Disease, Part 3/4
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Glicksberg, B. S., Johnson, K. W., & Dudley, J. T. (2018) · Human Molecular Genetics · doi:10.1093/hmg/ddy114 · Cited by 84 · see citing works
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Taking Systems Medicine to Heart
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Liu, J., Li, D., Dong, W., Liu, Y., Wu, Y., Tang, D.-X., Zhang, F., Qiu, M., Hua, Q., He, J., et al. (2021) · Signal Transduction and Targeted Therapy · doi:10.1038/s41392-021-00741-x · Cited by 41 · see citing works
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Dovrolis, N., Kolios, G. C., Spyrou, G. M., & Maroulakou, I. G. (2017) · Briefings in Bioinformatics · doi:10.1093/bib/bbx154 · Cited by 34 · see citing works
Zilocchi, M., Broderick, K., Phanse, S., Aly, K. A., & Babu, M. (2020) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2020.09.008 · Cited by 32 · see citing works
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Besnier, M., Finemore, M., Yu, C. H., Kott, K. A., Vernon, S. T., Seebacher, N. A., Genetzakis, E., Furman, A., Tang, O., Davis, R. L., et al. (2021) · Antioxidants · doi:10.3390/antiox10101547 · Cited by 18 · see citing works
Abdulkareem, M., Kenawy, A. A. M., Rauseo, E., Lee, A. M., Sojoudi, A., Amir-Khalili, A., Lekadir, K., Young, A. A., Barnes, M. R., Barckow, P., et al. (2022) · Frontiers in Cardiovascular Medicine · doi:10.3389/fcvm.2022.894503 · Cited by 18 · see citing works
Gallo, G., Volpe, M., Battistoni, A., Russo, D., Tocci, G., & Musumeci, B. (2021) · Frontiers in Physiology · doi:10.3389/fphys.2021.652163 · Cited by 17 · see citing works
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Sonawane, A. R., Aïkawa, E., & Aikawa, M. (2022) · Frontiers in Cardiovascular Medicine · doi:10.3389/fcvm.2022.873582 · Cited by 17 · see citing works
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Chen, X., LEUNG, Y.-L. A., & Jiangang, S. H. E. N. (2022) · Digital Chinese Medicine · doi:10.1016/j.dcmed.2022.12.003 · Cited by 17 · see citing works
Mehrpour, O., Saeedi, F., Hoyte, C. O., Goss, F. R., & Shirazi, F. “. (2022) · BMC Pharmacology and Toxicology · doi:10.1186/s40360-022-00588-0 · Cited by 15 · see citing works
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Sutanto, H. (2024) · Current Problems in Cardiology · doi:10.1016/j.cpcardiol.2024.102454 · Cited by 14 · see citing works
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Shrestha, S., & Sengupta, P. P. (2018) · Journal of the American Society of Echocardiography · doi:10.1016/j.echo.2018.10.003 · Cited by 14 · see citing works
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Baladrón, C., de Diego, J. J. G., & Amat-Santos, I. J. (2020) · Revista Española de Cardiología (English Edition) · doi:10.1016/j.rec.2020.06.036 · Cited by 14 · see citing works
Abdulkareem, M., Brahier, M. S., Zou, F., Taylor, A. C., Thomaides, A. X., Bergquist, P. J., Srichai, M. B., Lee, A. M., Vargas, J. D., & Petersen, S. E. (2022) · Frontiers in Cardiovascular Medicine · doi:10.3389/fcvm.2022.822269 · Cited by 14 · see citing works
Johnson, K. W., Glicksberg, B. S., Shameer, K., Vengrenyuk, Y., Krittanawong, C., Russak, A. J., Sharma, S. K., Narula, J., Dudley, J. T., & Kini, A. S. (2019) · EBioMedicine · doi:10.1016/j.ebiom.2019.05.007 · Cited by 14 · see citing works
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Bamba, H., Singh, G., John, J., Inban, P., Prajjwal, P., Alhussain, H. M., & Marsool, M. D. M. (2024) · Current Problems in Cardiology · doi:10.1016/j.cpcardiol.2024.102470 · Cited by 12 · see citing works
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Cheng, X., Manandhar, I., Aryal, S., & Joe, B. (2021) · Comprehensive physiology · doi:10.1002/cphy.c200034 · Cited by 21 · see citing works
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Zhu, F., Li, P., & Sheng, Y. (2022) · Frontiers in Pharmacology · doi:10.3389/fphar.2022.1004181 · Cited by 12 · see citing works
Shameer, K., Dow, G. T., Glicksberg, B. S., Johnson, K. W., Ze, Y., Tomlinson, M., Readhead, B., Dudley, J. T., & Kullo, I. J. (2018) · Europe PMC (PubMed Central) · Cited by 11 · see citing works
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Kaiser, H., Kvist‐Hansen, A., Becker, C., Wang, X., McCauley, B. D., Krakauer, M., Gørtz, P. M., Henningsen, K. M. A., Zachariae, C. O. C., Skov, L., et al. (2021) · JMIR Research Protocols · doi:10.2196/28669 · Cited by 8 · see citing works
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Matkovich, S. J. (2019) · Current Opinion in Cardiology · doi:10.1097/hco.0000000000000611 · Cited by 7 · see citing works
Thakar, J., Thatcher, T. H., Smith, M. R., Woeller, C. F., Walker, D. I., Utell, M. J., Hopke, P. K., Mallon, T. M., Krahl, P. L., Rohrbeck, P., et al. (2019) · Journal of Occupational and Environmental Medicine · doi:10.1097/jom.0000000000001710 · Cited by 7 · see citing works
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Baladrón, C., de Diego, J. J. G., & Amat-Santos, I. J. (2020) · Revista Española de Cardiología · doi:10.1016/j.recesp.2020.06.017 · Cited by 7 · see citing works
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Khelimskii, D. A., Гозоевич, Б. А., Krymcov, O., Baranov, A. A., Manukian, S. N., & Lazarev, M. (2024) · Heliyon · doi:10.1016/j.heliyon.2024.e36691 · Cited by 7 · see citing works
Manoel, P. Z., Dike, I. C., Anis, H., Yassin, N., Wojtara, M. S., & Uwishema, O. (2024) · Health Science Reports · doi:10.1002/hsr2.70173 · Cited by 6 · see citing works
Johnson, K. W., Freitas, J. K. D., Glicksberg, B. S., Bobe, J. R., & Dudley, J. T. (2018) · Pacific Symposium on Biocomputing (PSB) · doi:10.1142/9789813279827_0038 · Cited by 5 · see citing works
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Abdulkareem, M., Aung, N., & Petersen, S. E. (2022) · Contemporary medical imaging · doi:10.1007/978-3-030-92087-6_8 · Cited by 4 · see citing works
Hutchison, E. R., Zhang, Y., Nampally, S., Neelufer, I. K. A., Malkov, V., Weatherall, J., Khan, F., & Shameer, K. (2021) · medRxiv · doi:10.1101/2021.11.12.21266277 · Cited by 4 · see citing works
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Nikita, Akash, Upmanyu, B., Sreeshma Raj, K., Bhalla, P., Narula, S., & Chaudhary, K. (2026) · Progress in molecular biology and translational science · doi:10.1016/bs.pmbts.2026.03.011 · Cited by 3 · see citing works
Ozsahin, D. U., Isa, N. A., & Uzun, B. (2022) · Diagnostics · doi:10.3390/diagnostics12122943 · Cited by 3 · see citing works
Mishkin, I. A., Koncevaya, A. V., & Drapkina, O. M. (2025) · The European Physical Journal Special Topics · doi:10.1140/epjs/s11734-024-01451-5 · Cited by 3 · see citing works
Mouriño-Álvarez, L., Martín-Rojas, T., Corros-Vicente, C., Corbacho-Alonso, N., Padial, L. R., Solı́s, J., & Barderas, M. G. (2020) · Journal of Clinical Medicine · doi:10.3390/jcm9082421 · Cited by 3 · see citing works
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Karmakar, P. S., Trivedi, A., & Gaitonde, V. V. (2020) · IntechOpen eBooks · doi:10.5772/intechopen.90922 · Cited by 3 · see citing works
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Chandrashekhar, Y., & Johnson, K. W. (2019) · JACC. Cardiovascular imaging · doi:10.1016/j.jcmg.2018.12.005 · Cited by 3 · see citing works
Self-citationPark, H. W., Kang, T., Seo, Y. H., & PARK, J.-H. (2026) · The Korean Journal of Internal Medicine · doi:10.3904/kjim.2025.104 · Cited by 2 · see citing works
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Tripathi, S., & Chumnanvej, S. (2025) · IGI Global eBooks · doi:10.4018/979-8-3373-0659-9.ch006 · Cited by 2 · see citing works
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Kharlamov, A., & Lamberts, M. (2023) · BMC Cardiovascular Disorders · doi:10.1186/s12872-022-02971-5 · Cited by 2 · see citing works
AI-based diagnosis techniques for cardiac disease analysis and predictions
Ansari, M. A., Mehrotra, R., Tripathi, P., & Agrawal, R. (2021) · Elsevier eBooks · doi:10.1016/b978-0-323-85064-3.00002-9 · Cited by 1 · see citing works
A macrophage gene-regulatory network linked to clinical severity of coronary artery disease
Ma, L., Tamis‐Holland, J. E., Mocci, G., Wolhuter, K., Bryce, N. S., Sajja, S., Amadori, L., Pradhan, P., Chong, P. S., Sukhavasi, K., et al. (2025) · Basic Research in Cardiology · doi:10.1007/s00395-025-01105-0 · Cited by 1 · see citing works
Self-citationClinical Research in Cardiovascular Disease using Metabolomics
Pinto, R. C., Dehghan, A., Barros, A. S., Graça, G., Diaz, S. O., & Leite-Moreira, A. F. (2020) · Elsevier eBooks · doi:10.1016/b978-0-12-801238-3.11648-4
Repurposing the atypical antidepressant trazodone for atherosclerotic cardiovascular diseasePreprint
Johnson, K. W., Narula, J., Glicksberg, B. S., Shameer, K., Chaudhry, F., Yahi, A., Khan, N. S., Amadori, L., Krittanawong, C., Readhead, B., et al. (2021) · Research Square · doi:10.21203/rs.3.rs-567817/v1
Self-citationDiagnostics and Prevention: Landscape for Technology Innovation in Precision Cardiovascular Medicine
Mauch, J., Thachil, V., & Tang, W. H. W. (2022) · Elsevier eBooks · doi:10.1016/b978-0-12-816861-5.00004-6
Ly, B., Pop, M., Cochet, H., Duchateau, N., O’Regan, D. P., & Maxime Sermesant, B. (2023) · Springer eBooks · doi:10.1007/978-3-031-05071-8_6
Tolu-Akinnawo, O. Z., Greek, A., Jaamour, D., Azeez, H. A., Ibekwe, J. P., Adeleke, O. A., Bolarinwa, A. C., Adenuga, O., Jung, J. H., & Awoyemi, T. (2025) · Journal of Cardiovascular Pharmacology · doi:10.1097/fjc.0000000000001788
Ibrahim, B. (2026) · Briefings in Bioinformatics · doi:10.1093/bib/bbaf729
Multi-omic analysis of human PHACTR1 signaling networks
Wolhuter, K., Ma, L., Bryce, N. S., Contreras, O., Mellett, N. A., Zhong, L., Thekkedam, C. G., Iismaa, S. E., Giles, C., Harvey, R. P., et al. (2026) · Communications Biology · doi:10.1038/s42003-026-09542-w
Self-citationPatel, V., Navale, A. M., & Singh, P. (2026) · SN Comprehensive Clinical Medicine · doi:10.1007/s42399-026-02306-x
Statistical Methods for Longitudinal Cardiovascular Disease Research Design: A Narrative Review
Chen, Y., Wei, Y., Yang, Y., Li, C., (341591), X. W., Xue, T., Ruan, Y., Feng, G., & (43681), T. Z. (2026) · Cardiovascular Innovations and Applications · doi:10.15212/cvia.2026.0015
Shahverdi, E., Schulz, M., Schneider, C., & Lange, M. (2026) · Echocardiography · doi:10.1111/echo.70582
Artificial Intelligence in Pulmonary Hypertension: Current State and Future Prospects
Li, Z., Xu, X., Li, J., 方瑀紳, He, Y., Tang, J., Yang, Z., Xie, H., & Yuan, H. (2026) · Reviews in Cardiovascular Medicine · doi:10.31083/rcm48114
Amir, M., Khalid, M., Zaheer, L., Nazar, S., Khursheed, M. A., Hussain, M., & Ahmad, T. (2026) · Molecular Biology Reports · doi:10.1007/s11033-026-12872-x
Artificial Intelligence in Aortic Disease
Carbone, A., Bossone, E., Al-Sergani, H., Alkasab, M., & Geraci, F. (2026) · Heart Failure Clinics · doi:10.1016/j.hfc.2026.08.002
Artificial intelligence in interventional cardiology: present landscape and future directions
Li, J., Li, Y., Zhu, Q., & Liu, X. (2026) · Vessel Plus · doi:10.20517/2574-1209.2026.12
- Authors
- Johnson, K. W., Shameer, K., Glicksberg, B. S., Readhead, B., Sengupta, P. P., Björkegren, J. L. M., Kovacic, J. C., & Dudley, J. T.
- Affiliations
- Mount Sinai Health System (US)
- Icahn School of Medicine at Mount Sinai (US)
- Karolinska Institutet (SE)
- Corresponding author
- Dudley, Joel T.
- ORCID
- Venue
- JACC: Basic to Translational Science
- Publisher
- Elsevier BV
- Published
- 2017-06
- Volume
- 2
- Issue
- 3
- Pages
- 311-327
- ISSN
- 2452-302X (ISSN-L)
- PMC
- PMC6034501
- OpenAlex
- W2679733416
- Cited by
- 83
- References
- 158
- Citations by year
- 2026: 11 · 2025: 5 · 2024: 5 · 2023: 4 · 2022: 12 · 2021: 14 · 2020: 12 · 2019: 11 · 2018: 7 · 2017: 2
- FWCI
- 0.24
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- JACC Basic to Translational Science (publishedVersion, cc-by-nc-nd)
- Europe PMC (PubMed Central) (publishedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- AstraZeneca
- GlaxoSmithKline
- F. Hoffmann-La Roche
- Janssen Pharmaceuticals
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Keywords
- precision cardiology, cardiovascular disease, machine learning, systems medicine, polypharmacology, patient stratification
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