R: A Language and Environment for Statistical Computing

Chronic kidney disease alters intestinal microbial flora

Vaziri, N. D., Wong, J., Pahl, M., Piceno, Y. M., Yuan, J., DeSantis, T. Z., Ni, Z., Nguyen, T.-H., & Andersen, G. L. (2013) · Kidney International · doi:10.1038/ki.2012.345 · Cited by 1,164

How to do a meta‐analysis

Field, A. P., & Gillett, R. (2010) · British Journal of Mathematical and Statistical Psychology · doi:10.1348/000711010x502733 · Cited by 1,162

The Musicality of Non-Musicians: An Index for Assessing Musical Sophistication in the General Population

Müllensiefen, D., Gingras, B., Musil, J., & Stewart, L. (2014) · PLoS ONE · doi:10.1371/journal.pone.0089642 · Cited by 1,155

Emotional intelligence predicts academic performance: a meta-analysis

MacCann, C., Jiang, Y., Brown, L. E. R., Double, K. S., Bucich, M., & Minbashian, A. (2020) · Psychological Bulletin · doi:10.1037/bul0000219 · Cited by 1,121

Incidence of Endocrine Dysfunction Following the Use of Different Immune Checkpoint Inhibitor Regimens: A Systematic Review and Meta-analysis

Barroso-Sousa, R., Barry, W. T., Garrido-Castro, A. C., Hodi, F. S., Min, L., Krop, I. E., & Tolaney, S. M. (2018) · JAMA Oncology · doi:10.1001/jamaoncol.2017.3064 · Cited by 1,115

Modulation of Myelopoiesis Progenitors Is an Integral Component of Trained Immunity

Mitroulis, I., Ruppova, K., Wang, B., Chen, L.-S., Grzybek, M., Grinenko, T., Eugster, A., Troullinaki, M., Palladini, A., Kourtzelis, I., et al. (2018) · Cell · doi:10.1016/j.cell.2017.11.034 · Cited by 1,100

Organoid Profiling Identifies Common Responders to Chemotherapy in Pancreatic Cancer

Tiriac, H., Belleau, P., Engle, D. D., Plenker, D., Deschênes, A., Somerville, T. D. D., Froeling, F. E. M., Burkhart, R. A., Denroche, R. E., Jang, G.-H., et al. (2018) · Cancer Discovery · doi:10.1158/2159-8290.cd-18-0349 · Cited by 1,092

Confirmatory path analysis in a generalized multilevel context

Shipley, B. (2009) · Ecology · doi:10.1890/08-1034.1 · Cited by 1,083

Improving sleep quality leads to better mental health: A meta-analysis of randomised controlled trials

Scott, A. J., Webb, T. L., Martyn-St James, M., Rowse, G., & Weich, S. (2021) · Sleep Medicine Reviews · doi:10.1016/j.smrv.2021.101556 · Cited by 1,094

Green, circular, bio economy: a comparative analysis of sustainability avenues

D'Amato, D., Droste, N., Allen, B., Kettunen, M., Lähtinen, K., Korhonen, J., Leskinen, P., Matthies, B. D., & Toppinen, A. (2017) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2017.09.053 · Cited by 1,077

Stromal gene expression defines poor-prognosis subtypes in colorectal cancer

Calon, A., Lonardo, E., Berenguer-Llergo, A., Espinet, E., Hernando-Momblona, X., Iglesias, M., Sevillano, M., Palomo-Ponce, S., Tauriello, D. V. F., Byrom, D., et al. (2015) · Nature Genetics · doi:10.1038/ng.3225 · Cited by 1,070

block CV : An r package for generating spatially or environmentally separated folds for k ‐fold cross‐validation of species distribution models

Valavi, R., Elith, J., Lahoz‐Monfort, J. J., & Guillera‐Arroita, G. (2019) · Methods in Ecology and Evolution · doi:10.1111/2041-210x.13107 · Cited by 1,056

A consensus guide to capturing the ability to inhibit actions and impulsive behaviors in the stop-signal task

Verbruggen, F., Aron, A. R., Band, G. P., Beste, C., Bissett, P. G., Brockett, A. T., Brown, J. W., Chamberlain, S. R., Chambers, C. D., Colonius, H., et al. (2019) · eLife · doi:10.7554/elife.46323 · Cited by 990

Age Group Comparability of Raw Accelerometer Output from Wrist- and Hip-Worn Monitors

HILDEBRAND, M., VAN HEES, V. T., HANSEN, B. H., & EKELUND, U. (2014) · Medicine & Science in Sports & Exercise · doi:10.1249/mss.0000000000000289 · Cited by 1,043

Estimating mean QALYs in trial‐based cost‐effectiveness analysis: the importance of controlling for baseline utility

Manca, A., Hawkins, N., & Sculpher, M. J. (2005) · Health Economics · doi:10.1002/hec.944 · Cited by 1,024

Improvements in the GISTEMP uncertainty model

Lenssen, N. J. L., Schmidt, G. A., Hansen, J. E., Menne, M. J., Persin, A., Ruedy, R., & Zyss, D. (2019) · Journal of Geophysical Research: Atmospheres · doi:10.1029/2018jd029522 · Cited by 962

Google Scholar, Microsoft Academic, Scopus, Dimensions, Web of Science, and OpenCitations’ COCI: A Multidisciplinary Comparison of Coverage via Citations

Martín-Martín, A., Thelwall, M., Orduna-Malea, E., & Delgado López-Cózar, E. (2021) · Scientometrics · doi:10.1007/s11192-020-03690-4 · Cited by 1,018

Alzheimer’s Disease Risk Gene CD33 Inhibits Microglial Uptake of Amyloid Beta

Griciuc, A., Serrano-Pozo, A., Parrado, A. R., Lesinski, A. N., Asselin, C. N., Mullin, K., Hooli, B., Choi, S. H., Hyman, B. T., & Tanzi, R. E. (2013) · Neuron · doi:10.1016/j.neuron.2013.04.014 · Cited by 1,004

Adjustment of microbial nitrogen use efficiency to carbon:nitrogen imbalances regulates soil nitrogen cycling

Mooshammer, M., Wanek, W., Hämmerle, I., Fuchslueger, L., Hofhansl, F., Knoltsch, A., Schnecker, J., Takriti, M., Watzka, M., Wild, B., et al. (2014) · Nature Communications · doi:10.1038/ncomms4694 · Cited by 1,010

Global Pyrogeography: the Current and Future Distribution of Wildfire

Krawchuk, M. A., Moritz, M. A., Parisien, M.-A., Van Dorn, J., & Hayhoe, K. (2009) · PLoS ONE · doi:10.1371/journal.pone.0005102 · Cited by 1,000

Physiological plasticity increases resilience of ectothermic animals to climate change

Seebacher, F., White, C. R., & Franklin, C. E. (2015) · Nature Climate Change · doi:10.1038/nclimate2457 · Cited by 998

Ten‐Year Effects of the Advanced Cognitive Training for Independent and Vital Elderly Cognitive Training Trial on Cognition and Everyday Functioning in Older Adults

Rebok, G. W., Ball, K., Guey, L. T., Jones, R. N., Kim, H., King, J. W., Marsiske, M., Morris, J. N., Tennstedt, S. L., Unverzagt, F. W., et al. (2014) · Journal of the American Geriatrics Society · doi:10.1111/jgs.12607 · Cited by 965

Student Motivation and Associated Outcomes: A Meta-Analysis From Self-Determination Theory

Howard, J. L., Bureau, J., Guay, F., Chong, J. X. Y., & Ryan, R. M. (2021) · Perspectives on Psychological Science · doi:10.1177/1745691620966789 · Cited by 1,001

Estimation of national, regional, and global prevalence of alcohol use during pregnancy and fetal alcohol syndrome: a systematic review and meta-analysis

Popova, S., Lange, S., Probst, C., Gmel, G., & Rehm, J. (2017) · The Lancet Global Health · doi:10.1016/s2214-109x(17)30021-9 · Cited by 937

Wild Mouse Gut Microbiota Promotes Host Fitness and Improves Disease Resistance

Rosshart, S. P., Vassallo, B. G., Angeletti, D., Hutchinson, D. S., Morgan, A. P., Takeda, K., Hickman, H. D., McCulloch, J. A., Badger, J. H., Ajami, N. J., et al. (2017) · Cell · doi:10.1016/j.cell.2017.09.016 · Cited by 862

Dual proteome-scale networks reveal cell-specific remodeling of the human interactome

Huttlin, E. L., Bruckner, R. J., Navarrete-Perea, J., Cannon, J. R., Baltier, K., Gebreab, F., Gygi, M. P., Thornock, A., Zarraga, G., Tam, S., et al. (2020) · doi:10.1101/2020.01.19.905109 · Cited by 960

Health-protective behaviour, social media usage and conspiracy belief during the COVID-19 public health emergency

Allington, D., Duffy, B., Wessely, S., Dhavan, N., & Rubin, J. (2021) · Psychological Medicine · doi:10.1017/s003329172000224x · Cited by 904

Sustained microglial depletion with CSF1R inhibitor impairs parenchymal plaque development in an Alzheimer’s disease model

Spangenberg, E., Severson, P. L., Hohsfield, L. A., Crapser, J., Zhang, J., Burton, E. A., Zhang, Y., Spevak, W., Lin, J., Phan, N. Y., et al. (2019) · Nature Communications · doi:10.1038/s41467-019-11674-z · Cited by 939

The conservation status of the world’s reptiles

Böhm, M., Collen, B., Baillie, J. E. M., Bowles, P., Chanson, J., Cox, N., Hammerson, G., Hoffmann, M., Livingstone, S. R., Ram, M., et al. (2013) · Biological Conservation · doi:10.1016/j.biocon.2012.07.015 · Cited by 914

A Total-Evidence Approach to Dating with Fossils, Applied to the Early Radiation of the Hymenoptera

Ronquist, F., Klopfstein, S., Vilhelmsen, L., Schulmeister, S., Murray, D. L., & Rasnitsyn, A. P. (2012) · Systematic Biology · doi:10.1093/sysbio/sys058 · Cited by 930

Temporal dynamics of soil organic carbon after land-use change in the temperate zone - carbon response functions as a model approach: SOIL ORGANIC CARBON AND LAND-USE CHANGE

POEPLAU, C., DON, A., VESTERDAL, L., LEIFELD, J., VAN WESEMAEL, B., SCHUMACHER, J., & GENSIOR, A. (2011) · Global Change Biology · doi:10.1111/j.1365-2486.2011.02408.x · Cited by 918

Psychometric properties of the EQ-5D-5L: a systematic review of the literature

Feng, Y.-S., Kohlmann, T., Janssen, M. F., & Buchholz, I. (2021) · Quality of Life Research · doi:10.1007/s11136-020-02688-y · Cited by 917

Sharing Detailed Research Data Is Associated with Increased Citation Rate

Piwowar, H. A., Day, R. S., & Fridsma, D. B. (2007) · PLoS ONE · doi:10.1371/journal.pone.0000308 · Cited by 881

The Charlson comorbidity index is adapted to predict costs of chronic disease in primary care patients

Charlson, M. E., Charlson, R. E., Peterson, J. C., Marinopoulos, S. S., Briggs, W. M., & Hollenberg, J. P. (2008) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2008.01.006 · Cited by 894

Global alcohol exposure between 1990 and 2017 and forecasts until 2030: a modelling study

Manthey, J., Shield, K. D., Rylett, M., Hasan, O. S. M., Probst, C., & Rehm, J. (2019) · The Lancet · doi:10.1016/s0140-6736(18)32744-2 · Cited by 856

Happiness is greater in natural environments

MacKerron, G., & Mourato, S. (2013) · Global Environmental Change · doi:10.1016/j.gloenvcha.2013.03.010 · Cited by 899

Distribution of plastic polymer types in the marine environment; A meta-analysis

Erni-Cassola, G., Zadjelovic, V., Gibson, M. I., & Christie-Oleza, J. A. (2019) · Journal of Hazardous Materials · doi:10.1016/j.jhazmat.2019.02.067 · Cited by 874

International standards for fetal growth based on serial ultrasound measurements: the Fetal Growth Longitudinal Study of the INTERGROWTH-21st Project

Papageorghiou, A. T., Ohuma, E. O., Altman, D. G., Todros, T., Ismail, L. C., Lambert, A., Jaffer, Y. A., Bertino, E., Gravett, M. G., Purwar, M., et al. (2014) · The Lancet · doi:10.1016/s0140-6736(14)61490-2 · Cited by 870

A comparison of methods for differential expression analysis of RNA-seq data

Soneson, C., & Delorenzi, M. (2013) · BMC Bioinformatics · doi:10.1186/1471-2105-14-91 · Cited by 887

DNA methylation in newborns and maternal smoking in pregnancy: genome-wide consortium meta-analysis

Joubert, B. R., Felix, J. F., Yousefi, P., Bakulski, K. M., Just, A. C., Breton, C., Reese, S. E., Markunas, C. A., Richmond, R. C., Xu, C.-J., et al. (2016) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2016.02.019 · Cited by 886

Zoonotic host diversity increases in human-dominated ecosystems

Gibb, R., Redding, D. W., Chin, K. Q., Donnelly, C. A., Blackburn, T. M., Newbold, T., & Jones, K. E. (2020) · Nature · doi:10.1038/s41586-020-2562-8 · Cited by 880

Reduced H3K27me3 and DNA Hypomethylation Are Major Drivers of Gene Expression in K27M Mutant Pediatric High-Grade Gliomas

Bender, S., Tang, Y., Lindroth, A. M., Hovestadt, V., Jones, D. T. W., Kool, M., Zapatka, M., Northcott, P. A., Sturm, D., Wang, W., et al. (2013) · Cancer Cell · doi:10.1016/j.ccr.2013.10.006 · Cited by 818

Probiotics for the Prevention and Treatment of Antibiotic-Associated Diarrhea: A Systematic Review and Meta-analysis

Newberry, S. J. (2012) · JAMA · doi:10.1001/jama.2012.3507 · Cited by 870

Risk of mortality in patients infected with SARS-CoV-2 variant of concern 202012/1: matched cohort study

Challen, R., Brooks-Pollock, E., Read, J. M., Dyson, L., Tsaneva-Atanasova, K., & Danon, L. (2021) · BMJ · doi:10.1136/bmj.n579 · Cited by 776

Measurement Schmeasurement: Questionable Measurement Practices and How to Avoid Them

Flake, J. K., & Fried, E. I. (2020) · Advances in Methods and Practices in Psychological Science · doi:10.1177/2515245920952393 · Cited by 969

Anatomy of hierarchy: Feedforward and feedback pathways in macaque visual cortex

Markov, N. T., Vezoli, J., Chameau, P., Falchier, A., Quilodran, R., Huissoud, C., Lamy, C., Misery, P., Giroud, P., Ullman, S., et al. (2014) · Journal of Comparative Neurology · doi:10.1002/cne.23458 · Cited by 759

Stress-related and basic determinants of hair cortisol in humans: a meta-analysis

Stalder, T., Steudte-Schmiedgen, S., Alexander, N., Klucken, T., Vater, A., Wichmann, S., Kirschbaum, C., & Miller, R. (2017) · Psychoneuroendocrinology · doi:10.1016/j.psyneuen.2016.12.017 · Cited by 855

Lost, but Found with Nile Red: A Novel Method for Detecting and Quantifying Small Microplastics (1 mm to 20 μm) in Environmental Samples

Erni-Cassola, G., Gibson, M. I., Thompson, R. C., & Christie-Oleza, J. A. (2017) · Environmental Science & Technology · doi:10.1021/acs.est.7b04512 · Cited by 848

Assessing the Impact of Transgenerational Epigenetic Variation on Complex Traits

Johannes, F., Porcher, E., Teixeira, F. K., Saliba-Colombani, V., Simon, M., Agier, N., Bulski, A., Albuisson, J., Heredia, F., Audigier, P., et al. (2009) · PLoS Genetics · doi:10.1371/journal.pgen.1000530 · Cited by 814

Land‐use intensity and the effects of organic farming on biodiversity: a hierarchical meta‐analysis

Tuck, S. L., Winqvist, C., Mota, F., Ahnström, J., Turnbull, L. A., & Bengtsson, J. (2014) · Journal of Applied Ecology · doi:10.1111/1365-2664.12219 · Cited by 846

Career adaptability: a meta-analysis of relationships with measures of adaptivity, adapting responses, and adaptation results

Rudolph, C. W., Lavigne, K. N., & Zacher, H. (2017) · Journal of Vocational Behavior · doi:10.1016/j.jvb.2016.09.002 · Cited by 837

Meta‐analyses of human gut microbes associated with obesity and IBD

Walters, W. A., Xu, Z., & Knight, R. (2014) · FEBS Letters · doi:10.1016/j.febslet.2014.09.039 · Cited by 836

The nuisance of nuisance regression: spectral misspecification in a common approach to resting-state fMRI preprocessing reintroduces noise and obscures functional connectivity

Hallquist, M. N., Hwang, K., & Luna, B. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.05.116 · Cited by 753

CATALISE: A Multinational and Multidisciplinary Delphi Consensus Study. Identifying Language Impairments in Children

Bishop, D. V. M., Snowling, M. J., Thompson, P. A., Greenhalgh, T., & CATALISE consortium (2016) · PLOS ONE · doi:10.1371/journal.pone.0158753 · Cited by 831

A meta-analysis of risk factors for post-traumatic stress disorder in children and adolescents

Trickey, D., Siddaway, A. P., Meiser-Stedman, R., Serpell, L., & Field, A. P. (2012) · Clinical Psychology Review · doi:10.1016/j.cpr.2011.12.001 · Cited by 828

Predictive performance of presence‐only species distribution models: a benchmark study with reproducible code

Valavi, R., Guillera‐Arroita, G., Lahoz‐Monfort, J. J., & Elith, J. (2022) · Ecological Monographs · doi:10.1002/ecm.1486 · Cited by 830

Global, 30-m resolution continuous fields of tree cover: Landsat-based rescaling of MODIS vegetation continuous fields with lidar-based estimates of error

Sexton, J. O., Song, X.-P., Feng, M., Noojipady, P., Anand, A., Huang, C., Kim, D.-H., Collins, K. M., Channan, S., DiMiceli, C., et al. (2013) · International Journal of Digital Earth · doi:10.1080/17538947.2013.786146 · Cited by 829

Sexual assault victimization and psychopathology: A review and meta-analysis

Dworkin, E. R., Menon, S. V., Bystrynski, J., & Allen, N. E. (2017) · Clinical Psychology Review · doi:10.1016/j.cpr.2017.06.002 · Cited by 825

Influence of biochars on nitrous oxide emission and nitrogen leaching from two contrasting soils

Singh, B. P., Hatton, B. J., Singh, B., Cowie, A. L., & Kathuria, A. (2010) · Journal of Environmental Quality · doi:10.2134/jeq2009.0138 · Cited by 818

Effect Size Guidelines, Sample Size Calculations, and Statistical Power in Gerontology

Brydges, C. R. (2019) · Innovation in Aging · doi:10.1093/geroni/igz036 · Cited by 826

Can DNA-Based Ecosystem Assessments Quantify Species Abundance? Testing Primer Bias and Biomass—Sequence Relationships with an Innovative Metabarcoding Protocol

Elbrecht, V., & Leese, F. (2015) · PLOS ONE · doi:10.1371/journal.pone.0130324 · Cited by 765

Neural Circuit-Specialized Astrocytes: Transcriptomic, Proteomic, Morphological, and Functional Evidence

Chai, H., Diaz-Castro, B., Shigetomi, E., Monte, E., Octeau, J. C., Yu, X., Cohn, W., Rajendran, P. S., Vondriska, T. M., Whitelegge, J. P., et al. (2017) · Neuron · doi:10.1016/j.neuron.2017.06.029 · Cited by 769

Small sample adjustments for robust variance estimation with meta-regression.

Tipton, E. (2015) · Psychological Methods · doi:10.1037/met0000011 · Cited by 810

Modern modelling techniques are data hungry: a simulation study for predicting dichotomous endpoints

van der Ploeg, T., Austin, P. C., & Steyerberg, E. W. (2014) · BMC Medical Research Methodology · doi:10.1186/1471-2288-14-137 · Cited by 809

Machine learning for the prediction of sepsis: a systematic review and meta-analysis of diagnostic test accuracy

Fleuren, L. M., Klausch, T. L. T., Zwager, C. L., Schoonmade, L. J., Guo, T., Roggeveen, L. F., Swart, E. L., Girbes, A. R. J., Thoral, P., Ercole, A., et al. (2020) · Intensive Care Medicine · doi:10.1007/s00134-019-05872-y · Cited by 755

Sleep Fragmentation and the Risk of Incident Alzheimer's Disease and Cognitive Decline in Older Persons

Lim, A. S. P., Kowgier, M., Yu, L., Buchman, A. S., & Bennett, D. A. (2013) · Sleep · doi:10.5665/sleep.2802 · Cited by 793

Plasticity in thermal tolerance has limited potential to buffer ectotherms from global warming

Gunderson, A. R., & Stillman, J. H. (2015) · Proceedings of the Royal Society B: Biological Sciences · doi:10.1098/rspb.2015.0401 · Cited by 792

Medical students' attitude towards artificial intelligence: a multicentre survey

Pinto dos Santos, D., Giese, D., Brodehl, S., Chon, S. H., Staab, W., Kleinert, R., Maintz, D., & Baeßler, B. (2019) · European Radiology · doi:10.1007/s00330-018-5601-1 · Cited by 754

Some Methods of Propensity‐Score Matching had Superior Performance to Others: Results of an Empirical Investigation and Monte Carlo simulations

Austin, P. C. (2009) · Biometrical Journal · doi:10.1002/bimj.200810488 · Cited by 769

A tutorial on a practical Bayesian alternative to null-hypothesis significance testing

Masson, M. E. J. (2011) · Behavior Research Methods · doi:10.3758/s13428-010-0049-5 · Cited by 752

Initial validation of the general attitudes towards Artificial Intelligence Scale

Schepman, A., & Rodway, P. (2020) · Computers in Human Behavior Reports · doi:10.1016/j.chbr.2020.100014 · Cited by 785

Roux-en-Y Gastric Bypass and Vertical Banded Gastroplasty Induce Long-Term Changes on the Human Gut Microbiome Contributing to Fat Mass Regulation

Tremaroli, V., Karlsson, F., Werling, M., Ståhlman, M., Kovatcheva-Datchary, P., Olbers, T., Fändriks, L., le Roux, C. W., Nielsen, J., & Bäckhed, F. (2015) · Cell Metabolism · doi:10.1016/j.cmet.2015.07.009 · Cited by 757

Sample size requirements to estimate key design parameters from external pilot randomised controlled trials: a simulation study

Teare, M. D., Dimairo, M., Shephard, N., Hayman, A., Whitehead, A., & Walters, S. J. (2014) · Trials · doi:10.1186/1745-6215-15-264 · Cited by 773

Predictors of amygdala activation during the processing of emotional stimuli: A meta-analysis of 385 PET and fMRI studies

Costafreda, S. G., Brammer, M. J., David, A. S., & Fu, C. H. Y. (2008) · Brain Research Reviews · doi:10.1016/j.brainresrev.2007.10.012 · Cited by 772

Multivariate meta-analysis for non-linear and other multi-parameter associations

Gasparrini, A., Armstrong, B., & Kenward, M. G. (2012) · Statistics in Medicine · doi:10.1002/sim.5471 · Cited by 772

Selecting optimal partitioning schemes for phylogenomic datasets

Lanfear, R., Calcott, B., Kainer, D., Mayer, C., & Stamatakis, A. (2014) · BMC Evolutionary Biology · doi:10.1186/1471-2148-14-82 · Cited by 762

Genomic Hallmarks and Structural Variation in Metastatic Prostate Cancer

Quigley, D. A., Dang, H. X., Zhao, S. G., Lloyd, P., Aggarwal, R., Alumkal, J. J., Foye, A., Kothari, V., Perry, M. D., Bailey, A. M., et al. (2018) · Cell · doi:10.1016/j.cell.2018.06.039 · Cited by 733

Intestinal label-retaining cells are secretory precursors expressing Lgr5

Buczacki, S. J. A., Zecchini, H. I., Nicholson, A. M., Russell, R., Vermeulen, L., Kemp, R., & Winton, D. J. (2013) · Nature · doi:10.1038/nature11965 · Cited by 761

Mindfulness Interventions with Youth: A Meta-Analysis

Zoogman, S., Goldberg, S. B., Hoyt, W. T., & Miller, L. (2015) · Mindfulness · doi:10.1007/s12671-013-0260-4 · Cited by 760

Enhanced sampling techniques in molecular dynamics simulations of biological systems

Bernardi, R. C., Melo, M. C. R., & Schulten, K. (2015) · Biochimica et Biophysica Acta (BBA) - General Subjects · doi:10.1016/j.bbagen.2014.10.019 · Cited by 757

A reaction norm model for genomic selection using high-dimensional genomic and environmental data

Jarquín, D., Crossa, J., Lacaze, X., Du Cheyron, P., Daucourt, J., Lorgeou, J., Piraux, F., Guerreiro, L., Pérez, P., Calus, M., et al. (2014) · Theoretical and Applied Genetics · doi:10.1007/s00122-013-2243-1 · Cited by 687

Network meta‐analysis, electrical networks and graph theory

Rücker, G. (2012) · Research Synthesis Methods · doi:10.1002/jrsm.1058 · Cited by 730

A critical review of the impacts of cover crops on nitrogen leaching, net greenhouse gas balance and crop productivity

Abdalla, M., Hastings, A., Cheng, K., Yue, Q., Chadwick, D., Espenberg, M., Truu, J., Rees, R. M., & Smith, P. (2019) · Global Change Biology · doi:10.1111/gcb.14644 · Cited by 747

Effect of Mindfulness-Based Stress Reduction vs Cognitive Behavioral Therapy or Usual Care on Back Pain and Functional Limitations in Adults With Chronic Low Back Pain: A Randomized Clinical Trial

Cherkin, D. C., Sherman, K. J., Balderson, B. H., Cook, A. J., Anderson, M. L., Hawkes, R. J., Hansen, K. E., & Turner, J. A. (2016) · JAMA · doi:10.1001/jama.2016.2323 · Cited by 729

Deep neural networks, gradient-boosted trees, random forests: Statistical arbitrage on the S&P 500

Krauss, C., Do, X. A., & Huck, N. (2017) · European Journal of Operational Research · doi:10.1016/j.ejor.2016.10.031 · Cited by 750

Ordinal Regression Models in Psychology: A Tutorial

Bürkner, P.-C., & Vuorre, M. (2019) · Advances in Methods and Practices in Psychological Science · doi:10.1177/2515245918823199 · Cited by 689

CAMP R3 : a database on sequences, structures and signatures of antimicrobial peptides: Table 1.

Waghu, F. H., Barai, R. S., Gurung, P., & Idicula-Thomas, S. (2016) · Nucleic Acids Research · doi:10.1093/nar/gkv1051 · Cited by 715

Unexpected role of interferon-γ in regulating neuronal connectivity and social behaviour

Filiano, A. J., Xu, Y., Tustison, N. J., Marsh, R. L., Baker, W., Smirnov, I., Overall, C. C., Gadani, S. P., Turner, S. D., Weng, Z., et al. (2016) · Nature · doi:10.1038/nature18626 · Cited by 697

Quantifying the impact of physical distance measures on the transmission of COVID-19 in the UK

CMMID COVID-19 working group, Jarvis, C. I., Van Zandvoort, K., Gimma, A., Prem, K., Klepac, P., Rubin, G. J., & Edmunds, W. J. (2020) · BMC Medicine · doi:10.1186/s12916-020-01597-8 · Cited by 720

Cognitive impairment by antibiotic-induced gut dysbiosis: analysis of gut microbiota-brain communication

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