The Human Phenome Project

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The Nature of Statistical Learning Theory

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Classification and Regression Trees.

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Density Estimation for Statistics and Data Analysis

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Molecular classification of cancer: class discovery and class prediction by gene expression monitoring

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Classification And Regression Trees

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Density Estimation for Statistics and Data Analysis

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Minimum information about a microarray experiment (MIAME)-toward standards for microarray data

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A probabilistic atlas and reference system for the human brain: International Consortium for Brain Mapping (ICBM)

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Multiclass cancer diagnosis using tumor gene expression signatures

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High-resolution haplotype structure in the human genome

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ALSPAC–The Avon Longitudinal Study of Parents and Children

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Allergy, Parasites, and the Hygiene Hypothesis

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Comprehensive Textbook of Psychiatry

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Blocks of Limited Haplotype Diversity Revealed by High-Resolution Scanning of Human Chromosome 21

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Accessing genetic variation: genotyping single nucleotide polymorphisms

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The minimum description length principle in coding and modeling

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A Twin Study of Genetic Relationships Between Psychotic Symptoms

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Use of population isolates for mapping complex traits

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Parasites and the Hygiene Hypothesis: Regulating the Immune System?

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Groups at risk in low birth weight infants and perinatal mortality.

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Selective serotonin reuptake inhibitors: a review of efficacy and tolerability in depression

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Rat genetics: attachign physiology and pharmacology to the genome

Jacob, H. J., & Kwitek, A. E. (2002) · Nature Reviews Genetics · doi:10.1038/nrg702 · Cited by 274 · see citing works

A mouse phenome project

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Random Forests--Random Features

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Ecological and individual predictors of birthweight in a northern Finland birth cohort 1986

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What Exactly Are Genomes, Genotypes and Phenotypes? And What About Phenomes?

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Large-scale, high-throughput screening for coagulation and hematologic phenotypes in mice*

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HapMap Launched With Pledges of $100 Million

Couzin, J. (2002) · Science · doi:10.1126/science.298.5595.941a · Cited by 72 · see citing works

Genetics of allergic disease

Feijen, M., Gerritsen, J., & Postma, D. S. (2000) · British Medical Bulletin · doi:10.1258/0007142001903580 · Cited by 54 · see citing works

Genetic analysis with hierarchical models

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UK launches ambitious tissue/data bank project

Mitchell, P. C. (2002) · Nature Biotechnology · doi:10.1038/nbt0602-529 · Cited by 5 · see citing works

Multi-omics approaches to disease

Hasin, Y., Seldin, M., & Lusis, A. (2017) · Genome Biology · doi:10.1186/s13059-017-1215-1 · Cited by 2,953 · see citing works

Global Mapping of the Yeast Genetic Interaction Network

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Evaluating the potential role of pleiotropy in Mendelian randomization studies

Hemani, G., Bowden, J., & Davey Smith, G. (2018) · Human Molecular Genetics · doi:10.1093/hmg/ddy163 · Cited by 1,496 · see citing works

Replicating genotype–phenotype associations

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Phenomics: the next challenge

Houle, D., Govindaraju, D. R., & Omholt, S. W. (2010) · Nature Reviews Genetics · doi:10.1038/nrg2897 · Cited by 1,276 · see citing works

Normal tissue reactions to radiotherapy: towards tailoring treatment dose by genotype

Barnett, G. C., West, C., Dunning, A. M., Elliott, R. M., Coles, C. E., Pharoah, P. D. P., & Burnet, N. G. (2009) · Nature reviews. Cancer · doi:10.1038/nrc2587 · Cited by 772 · see citing works

Rare-disease genetics in the era of next-generation sequencing: discovery to translation

Boycott, K. M., Vanstone, M. R., Bulman, D. E., & MacKenzie, A. (2013) · Nature Reviews Genetics · doi:10.1038/nrg3555 · Cited by 760 · see citing works

Common disorders are quantitative traits

Plomin, R., Haworth, C. M. A., & Davis, O. S. P. (2009) · Nature Reviews Genetics · doi:10.1038/nrg2670 · Cited by 670 · see citing works

A text-mining analysis of the human phenome

van Driel, M. A., Bruggeman, J., Vriend, G., Brunner, H. G., & Leunissen, J. A. M. (2006) · European Journal of Human Genetics · doi:10.1038/sj.ejhg.5201585 · Cited by 664 · see citing works

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

Age, Gene/Environment Susceptibility-Reykjavik Study: multidisciplinary applied phenomics

Harris, T. B., Launer, L. J., Eiriksdottir, G., Kjartansson, O., Jonsson, P. V., Sigurdsson, G., Thorgeirsson, G., Aspelund, T., Garcia, M. E., Cotch, M. F., et al. (2007) · American Journal of Epidemiology · doi:10.1093/aje/kwk115 · Cited by 592 · see citing works

Advanced sequencing technologies: methods and goals

Shendure, J., Mitra, R. D., Varma, C., & Church, G. M. (2004) · Nature Reviews Genetics · doi:10.1038/nrg1325 · Cited by 559 · see citing works

Zooming In on a Quantitative Trait for Tomato Yield Using Interspecific Introgressions

Fridman, E., Carrari, F. O., Liu, Y., Fernie, A. R., & Zamir, D. (2004) · Science · doi:10.1126/science.1101666 · Cited by 539 · see citing works

Abundant Pleiotropy in Human Complex Diseases and Traits

Sivakumaran, S., Agakov, F. V., Τheodoratou, E., Prendergast, J. G. D., Zgaga, L., Manolio, T. A., Rudan, I., McKeigue, P., Wilson, J. F., & Campbell, H. J. (2011) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2011.10.004 · Cited by 534 · see citing works

The endophenotype concept in psychiatric genetics

FLINT, J., & MUNAFÒ, M. R. (2007) · Psychological Medicine · doi:10.1017/s0033291706008750 · Cited by 477 · see citing works

Uncovering the structure of self-regulation through data-driven ontology discovery

Eisenberg, I. W., Bissett, P. G., Enkavi, A. Z., Li, J. K., MacKinnon, D. P., Marsch, L. A., & Poldrack, R. A. (2019) · Nature Communications · doi:10.1038/s41467-019-10301-1 · Cited by 473 · see citing works

Long-term neural and physiological phenotyping of a single human

Poldrack, R. A., Laumann, T. O., Koyejo, S., Gregory, B., Hover, A. M., Chen, M.-Y., Gorgolewski, K. J., Luci, J. J., Joo, S. J., Boyd, R. L., et al. (2015) · Nature Communications · doi:10.1038/ncomms9885 · Cited by 480 · see citing works

Deep phenotyping for precision medicine

Robinson, P. N. (2012) · Human Mutation · doi:10.1002/humu.22080 · Cited by 471 · see citing works

Systems biology and gene networks in neurodevelopmental and neurodegenerative disorders

Parikshak, N. N., Gandal, M. J., & Geschwind, D. H. (2015) · Nature Reviews Genetics · doi:10.1038/nrg3934 · Cited by 430 · see citing works

Royal Australian and New Zealand college of psychiatrists clinical practice guidelines for the treatment of schizophrenia and related disorders

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Toward constructing an endophenotype strategy for bipolar disorders

Hasler, G., Drevets, W. C., Gould, T. D., Gottesman, I. I., & Manji, H. K. (2006) · Biological Psychiatry · doi:10.1016/j.biopsych.2005.11.006 · Cited by 423 · see citing works

The modular nature of genetic diseases

Oti, M., & Brunner, H. G. (2006) · Clinical Genetics · doi:10.1111/j.1399-0004.2006.00708.x · Cited by 411 · see citing works

The Human Phenotype Ontology

Robinson, P. N., & Mundlos, S. (2010) · Clinical Genetics · doi:10.1111/j.1399-0004.2010.01436.x · Cited by 357 · see citing works

Comparing the human and chimpanzee genomes: Searching for needles in a haystackPreprint

Varki, A. P., & Altheide, T. K. (2005) · Genome Research · doi:10.1101/gr.3737405 · Cited by 339 · see citing works

The Developmental, Dimensional and Diagnostic Interview (3di): a novel computerized assessment for autism spectrum disorders

Skuse, D., Warrington, R., Bishop, D., Chowdhury, U., Lau, J., Mandy, W., & Place, M. (2004) · Journal of the American Academy of Child & Adolescent Psychiatry · doi:10.1097/00004583-200405000-00008 · Cited by 342 · see citing works

Association of DISC1/TRAX Haplotypes With Schizophrenia, Reduced Prefrontal Gray Matter, and Impaired Short- and Long-term Memory

Cannon, T. D., Hennah, W., van Erp, T. G. M., Thompson, P. M., Lonnqvist, J., Huttunen, M., Gasperoni, T., Tuulio-Henriksson, A., Pirkola, T., Toga, A. W., et al. (2005) · Archives of General Psychiatry · doi:10.1001/archpsyc.62.11.1205 · Cited by 337 · see citing works

Systematic Phenomics Analysis Deconvolutes Genes Mutated in Intellectual Disability into Biologically Coherent Modules

Kochinke, K., Zweier, C., Nijhof, B., Fencková, M., Čížek, P., Honti, F., Keerthikumar, S., Oortveld, M. A. W., Kleefstra, T., Kramer, J. M., et al. (2016) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2015.11.024 · Cited by 316 · see citing works

Navigating the channels and beyond: unravelling the genetics of the epilepsies

Helbig, I., Scheffer, I. E., Mulley, J. C., & Berkovic, S. F. (2008) · The Lancet Neurology · doi:10.1016/s1474-4422(08)70039-5 · Cited by 281 · see citing works

Unravelling the human genome–phenome relationship using phenome-wide association studies

Bush, W. S., Oetjens, M. T., & Crawford, D. C. (2016) · Nature Reviews Genetics · doi:10.1038/nrg.2015.36 · Cited by 267 · see citing works

Electronic health records-driven phenotyping: challenges, recent advances, and perspectives

Pathak, J., Kho, A. N., & Denny, J. C. (2013) · Journal of the American Medical Informatics Association · doi:10.1136/amiajnl-2013-002428 · Cited by 274 · see citing works

Examining impulsivity as an endophenotype using a behavioral approach: a DRD2 TaqI A and DRD4 48-bp VNTR association study.

Eisenberg, D. T. A., MacKillop, J., Modi, M. E., Beauchemin, J., Dang, D., Lisman, S. A., Lum, J. K., & Wilson, D. S. (2007) · Behavioral and Brain Functions · doi:10.1186/1744-9081-3-2 · Cited by 257 · see citing works

Cancer phenomics: RET and PTEN as illustrative models

Zbuk, K. M., & Eng, C. (2006) · Nature reviews. Cancer · doi:10.1038/nrc2037 · Cited by 240 · see citing works

Phenomics: the systematic study of phenotypes on a genome-wide scale

Bilder, R. M., Sabb, F. W., Cannon, T. D., London, E. D., Jentsch, J. D., Parker, D. S., Poldrack, R. A., Evans, C. J., & Freimer, N. B. (2009) · Neuroscience · doi:10.1016/j.neuroscience.2009.01.027 · Cited by 234 · see citing works

Advances in Electronic Phenotyping: From Rule-Based Definitions to Machine Learning Models

Banda, J. M., Seneviratne, M. G., Hernandez‐Boussard, T., & Shah, N. H. (2018) · Annual Review of Biomedical Data Science · doi:10.1146/annurev-biodatasci-080917-013315 · Cited by 223 · see citing works

An Official American Thoracic Society/European Respiratory Society Statement: Research Questions in Chronic Obstructive Pulmonary Disease

Celli, B. R., Decramer, M., Wedzicha, J. A., Wilson, K. C., Agustí, A. G. N., Criner, G. J., MacNee, W., Make, B. J., Rennard, S. I., Stockley, R. A., et al. (2015) · American Journal of Respiratory and Critical Care Medicine · doi:10.1164/rccm.201501-0044st · Cited by 222 · see citing works

Association of step counts over time with the risk of chronic disease in the All of Us Research Program

Master, H., Annis, J., Huang, S., Beckman, J. A., Ratsimbazafy, F., Marginean, K., Carroll, R., Natarajan, K., Harrell, F. E., Roden, D. M., et al. (2022) · Nature Medicine · doi:10.1038/s41591-022-02012-w · Cited by 221 · see citing works

Predictive validity of a method for evaluating temperament in young guide and service dogs

Duffy, D. L., & Serpell, J. A. (2012) · Applied Animal Behaviour Science · doi:10.1016/j.applanim.2012.02.011 · Cited by 220 · see citing works

Omics-Based Strategies in Precision Medicine: Toward a Paradigm Shift in Inborn Errors of Metabolism Investigations

Tebani, A., Afonso, C., Marret, S., & Bekri, S. (2016) · International Journal of Molecular Sciences · doi:10.3390/ijms17091555 · Cited by 214 · see citing works

Multi-omic data integration and analysis using systems genomics approaches: methods and applications in animal production, health and welfare

Suravajhala, P., Kogelman, L. J. A., & Kadarmideen, H. N. (2016) · Genetics Selection Evolution · doi:10.1186/s12711-016-0217-x · Cited by 219 · see citing works

Endophenotypes for psychiatric disorders: ready for primetime?

Bearden, C. E., & Freimer, N. B. (2006) · Trends in Genetics · doi:10.1016/j.tig.2006.04.004 · Cited by 206 · see citing works

LiDAR: An important tool for next-generation phenotyping technology of high potential for plant phenomics?

Lin, Y. (2015) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2015.10.011 · Cited by 207 · see citing works

Heart Failure in Pediatric Patients With Congenital Heart Disease

Hinton, R. B., & Ware, S. M. (2017) · Circulation Research · doi:10.1161/circresaha.116.308996 · Cited by 208 · see citing works

Creation and implications of a phenome-genome network

Butte, A. J., & Kohane, I. S. (2006) · Nature Biotechnology · doi:10.1038/nbt1150 · Cited by 204 · see citing works

Aggregometry detects platelet hyperreactivity in healthy individuals

Yee, D. L., Sun, C. W., Bergeron, A. L., Dong, J.-F., & Bray, P. F. (2005) · Blood · doi:10.1182/blood-2005-03-1290 · Cited by 203 · see citing works

An official American Thoracic Society/European Respiratory Society statement: research questions in COPD

Celli, B. R., Decramer, M., Wedzicha, J. A., Wilson, K. C., Agustí, A. G. N., Criner, G. J., MacNee, W., Make, B. J., Rennard, S. I., Stockley, R. A., et al. (2015) · European Respiratory Journal · doi:10.1183/09031936.00009015 · Cited by 201 · see citing works

The path to personalised medicine in COPD

Agustí, A. G. N. (2014) · Thorax · doi:10.1136/thoraxjnl-2014-205507 · Cited by 195 · see citing works

Reducing interscanner variability of activation in a multicenter fMRI study: Controlling for signal-to-fluctuation-noise-ratio (SFNR) differences

Friedman, L., & Glover, G. H. (2006) · NeuroImage · doi:10.1016/j.neuroimage.2006.07.012 · Cited by 191 · see citing works

Data integration and genomic medicine

Louie, B., Mork, P., Martin-Sanchez, F. J., Halevy, A. Y., & Tarczy-Hornoch, P. Z. (2006) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2006.02.007 · Cited by 186 · see citing works

Addressing the Complexity of Chronic Obstructive Pulmonary Disease: From Phenotypes and Biomarkers to Scale-Free Networks, Systems Biology, and P4 Medicine

Agustí, A. G. N., Sobradillo, P., & Celli, B. R. (2010) · American Journal of Respiratory and Critical Care Medicine · doi:10.1164/rccm.201009-1414pp · Cited by 184 · see citing works

Chronic Hyper-Hemolysis in Sickle Cell Anemia: Association of Vascular Complications and Mortality with Less Frequent Vasoocclusive Pain

Taylor, J. G., Nolan, V. G., Mendelsohn, L. G., Kato, G. J., Gladwin, M. T., & Steinberg, M. H. (2008) · PLoS ONE · doi:10.1371/journal.pone.0002095 · Cited by 184 · see citing works

Towards an Ontology of Cognitive Control

Lenartowicz, A., Kalar, D. J., Congdon, E. L., & Poldrack, R. A. (2010) · Topics in Cognitive Science · doi:10.1111/j.1756-8765.2010.01100.x · Cited by 177 · see citing works

From syndrome families to functional genomics

Brunner, H. G., & van Driel, M. A. (2004) · Nature Reviews Genetics · doi:10.1038/nrg1383 · Cited by 175 · see citing works

Classification of Human Population Based on HLA Gene Polymorphism and the Concept of Prakriti in Ayurveda

Patwardhan, B., Kalpana, J., & Arvind, C. (2005) · The Journal of Alternative and Complementary Medicine · doi:10.1089/acm.2005.11.349 · Cited by 174 · see citing works

The incompletely fulfilled promise of embryo transfer in cattle—why aren’t pregnancy rates greater and what can we do about it?

Hansen, P. J. (2020) · Journal of Animal Science · doi:10.1093/jas/skaa288 · Cited by 159 · see citing works

Tangled webs: Tracing the connections between genes and cognition

Fisher, S. E. (2006) · Cognition · doi:10.1016/j.cognition.2006.04.004 · Cited by 155 · see citing works

The use of pedigree, sib-pair and association studies of common diseases for genetic mapping and epidemiology

Freimer, N. B., & Sabatti, C. (2004) · Nature Genetics · doi:10.1038/ng1433 · Cited by 155 · see citing works

Synergy between medical informatics and bioinformatics: facilitating genomic medicine for future health care

Martin-Sanchez, F. J., Iakovidis, I., Norager, S., Maojo, V., de Groen, P., van der Lei, J., Jones, T., Abraham‐Fuchs, K., Apweiler, R., Babić, A., et al. (2003) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2003.09.003 · Cited by 151 · see citing works

Integrative phenomics reveals insight into the structure of phenotypic diversity in budding yeastPreprint

Skelly, D. A., Merrihew, G. E., Riffle, M. E., Connelly, C. F., Kerr, E. O., Johansson, M., Jaschob, D., Graczyk, B., Shulman, N., Wakefield, J., et al. (2013) · Genome Research · doi:10.1101/gr.155762.113 · Cited by 140 · see citing works

Neuropsychology 3.0: Evidence-Based Science and Practice

Bilder, R. M. (2010) · Journal of the International Neuropsychological Society · doi:10.1017/s1355617710001396 · Cited by 141 · see citing works

The use of 3D face shape modelling in dysmorphology

BMJ Publishing Group Ltd and Royal College of Paediatrics and Child Health (2007) · Archives of Disease in Childhood · doi:10.1136/adc.2006.103507 · Cited by 139 · see citing works

Oxytocin Receptor Gene Polymorphisms Are Associated with Human Directed Social Behavior in Dogs (Canis familiaris)

Kis, A., Bence, M., Lakatos, G., Pergel, E., Turcsán, B., Pluijmakers, J., Vas, J., Elek, Z., Brúder, I., Földi, L., et al. (2014) · PLoS ONE · doi:10.1371/journal.pone.0083993 · Cited by 138 · see citing works

Computer Vision, Machine Learning, and the Promise of Phenomics in Ecology and Evolutionary Biology

Lürig, M., Donoughe, S., SVENSSON, E. I., Porto, A., & Tsuboi, M. (2021) · Frontiers in Ecology and Evolution · doi:10.3389/fevo.2021.642774 · Cited by 136 · see citing works

Pharmacogenomics and ???Individualized Drug Therapy???

Nebert, D. W., Jorge-Nebert, L. F., & Vesell, E. S. (2003) · American Journal of PharmacoGenomics · doi:10.2165/00129785-200303060-00002 · Cited by 137 · see citing works

Arterial stiffness and influences of the metabolic syndrome: A cross-countries study

Scuteri, A., Cunha, P., Rosei, E. A., Badariere, J., Bekaert, S., Cockcroft, J. R., Cotter, J., Cucca, F., Buyzere, M. D., Meyer, T. D., et al. (2014) · Atherosclerosis · doi:10.1016/j.atherosclerosis.2014.01.041 · Cited by 134 · see citing works

Numbering the hairs on our heads: The shared challenge and promise of phenomics

Houle, D. (2009) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0906195106 · Cited by 132 · see citing works

Systems Pharmacology to Predict Drug Toxicity: Integration Across Levels of Biological Organization

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Chemobehavioural phenomics and behaviour-based psychiatric drug discovery in the zebrafish

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