R: A Language and Environment for Statistical Computing

Evaluation of Methods for the Extraction and Purification of DNA from the Human Microbiome

Yuan, S., Cohen, D. B., Ravel, J., Abdo, Z., & Forney, L. J. (2012) · PLoS ONE · doi:10.1371/journal.pone.0033865 · Cited by 511

Global shifts in mammalian population trends reveal key predictors of virus spillover risk

Johnson, C. K., Hitchens, P. L., Pandit, P. S., Rushmore, J., Evans, T. S., Young, C. C. W., & Doyle, M. M. (2020) · Proceedings of the Royal Society B: Biological Sciences · doi:10.1098/rspb.2019.2736 · Cited by 511

Decreased soil microbial biomass and nitrogen mineralisation with eucalyptus biochar addition to a coarse textured soil

Dempster, D. N., Gleeson, D. B., Solaiman, Z. M., Jones, D. L., & Murphy, D. V. (2012) · Plant and Soil · doi:10.1007/s11104-011-1067-5 · Cited by 509

Climate Change and Mental Health: A Scoping Review

Charlson, F., Ali, S., Benmarhnia, T., Pearl, M., Massazza, A., Augustinavicius, J., & Scott, J. G. (2021) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph18094486 · Cited by 507

Common European birds are declining rapidly while less abundant species' numbers are rising

Inger, R., Gregory, R., Duffy, J. P., Stott, I., Voříšek, P., & Gaston, K. J. (2015) · Ecology Letters · doi:10.1111/ele.12387 · Cited by 489

Chronotypes in the US–influence of age and sex

Fischer, D., Lombardi, D. A., Marucci-Wellman, H., & Roenneberg, T. (2017) · PLOS ONE · doi:10.1371/journal.pone.0178782 · Cited by 502

Worldwide evidence of a unimodal relationship between productivity and plant species richness

Fraser, L. H., Pither, J., Jentsch, A., Sternberg, M., Zobel, M., Askarizadeh, D., Bartha, S., Beierkuhnlein, C., Bennett, J. A., Bittel, A., et al. (2015) · Science · doi:10.1126/science.aab3916 · Cited by 501

Disrupted Modularity and Local Connectivity of Brain Functional Networks in Childhood-Onset Schizophrenia

Alexander-Bloch, A. F., Gogtay, N., Meunier, D., Birn, R., Clasen, L., Lalonde, F., Lenroot, R., Giedd, J., & Bullmore, E. T. (2010) · Frontiers in Systems Neuroscience · doi:10.3389/fnsys.2010.00147 · Cited by 480

Representativeness of Eddy-Covariance flux footprints for areas surrounding AmeriFlux sites

Chu, H., Luo, X., Ouyang, Z., Chan, W. S., Dengel, S., Biraud, S. C., Torn, M. S., Metzger, S., Kumar, J., Arain, M. A., et al. (2021) · Agricultural and Forest Meteorology · doi:10.1016/j.agrformet.2021.108350 · Cited by 497

Establishing a common metric for depressive symptoms: linking the BDI-II, CES-D, and PHQ-9 to PROMIS depression

Choi, S. W., Schalet, B., Cook, K. F., & Cella, D. (2014) · Psychological Assessment · doi:10.1037/a0035768 · Cited by 480

Genome-wide epigenetic regulation by early-life trauma

Labonté, B., Suderman, M., Maussion, G., Navaro, L., Yerko, V., Mahar, I., Bureau, A., Mechawar, N., Szyf, M., Meaney, M. J., et al. (2012) · Archives of General Psychiatry · doi:10.1001/archgenpsychiatry.2011.2287 · Cited by 488

Autophagy is critical for pancreatic tumor growth and progression in tumors with p53 alterations

Yang, A., Rajeshkumar, N. V., Wang, X., Yabuuchi, S., Alexander, B. M., Chu, G. C., Von Hoff, D. D., Maitra, A., & Kimmelman, A. C. (2014) · Cancer Discovery · doi:10.1158/2159-8290.cd-14-0362 · Cited by 486

Measuring the Meltdown: Drivers of Global Amphibian Extinction and Decline

Sodhi, N. S., Bickford, D., Diesmos, A. C., Lee, T. M., Koh, L. P., Brook, B. W., Sekercioglu, C. H., & Bradshaw, C. J. A. (2008) · PLoS ONE · doi:10.1371/journal.pone.0001636 · Cited by 492

Almost All Antipsychotics Result in Weight Gain: A Meta-Analysis

Bak, M., Fransen, A., Janssen, J., van Os, J., & Drukker, M. (2014) · PLoS ONE · doi:10.1371/journal.pone.0094112 · Cited by 490

The impact of therapy-related acute myeloid leukemia (AML) on outcome in 2853 adult patients with newly diagnosed AML

Kayser, S., Döhner, K., Krauter, J., Köhne, C.-H., Horst, H. A., Held, G., von Lilienfeld-Toal, M., Wilhelm, S., Kündgen, A., Götze, K., et al. (2011) · Blood · doi:10.1182/blood-2010-08-301713 · Cited by 487

DNA methylome profiling of human tissues identifies global and tissue-specific methylation patterns

Lokk, K., Modhukur, V., Rajashekar, B., Märtens, K., Mägi, R., Kolde, R., Koltšina, M., Nilsson, T. K., Vilo, J., Salumets, A., et al. (2014) · Genome Biology · doi:10.1186/gb-2014-15-4-r54 · Cited by 444

A recent bottleneck of Y chromosome diversity coincides with a global change in culture

Karmin, M., Saag, L., Vicente, M., Sayres, M. A. W., Järve, M., Talas, U. G., Rootsi, S., Ilumäe, A.-M., Mägi, R., Mitt, M., et al. (2015) · Genome Research · doi:10.1101/gr.186684.114 · Cited by 485

In situ effects of biochar on aggregation, water retention and porosity in light-textured tropical soils

Obia, A., Mulder, J., Martinsen, V., Cornelissen, G., & Børresen, T. (2016) · Soil and Tillage Research · doi:10.1016/j.still.2015.08.002 · Cited by 488

The Availability of Research Data Declines Rapidly with Article Age

Vines, T. H., Albert, A. Y. K., Andrew, R. L., Débarre, F., Bock, D. G., Franklin, M. T., Gilbert, K. J., Moore, J.-S., Renaut, S., & Rennison, D. J. (2014) · Current Biology · doi:10.1016/j.cub.2013.11.014 · Cited by 482

A randomized trial to assess catheter ablation versus rate control in the management of persistent atrial fibrillation in heart failure

Jones, D. G., Haldar, S. K., Hussain, W., Sharma, R., Francis, D. P., Rahman-Haley, S. L., McDonagh, T. A., Underwood, S. R., Markides, V., & Wong, T. (2013) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2013.01.069 · Cited by 481

Cardiorespiratory fitness as a predictor of intestinal microbial diversity and distinct metagenomic functions

Estaki, M., Pither, J., Baumeister, P., Little, J. P., Gill, S. K., Ghosh, S., Ahmadi-Vand, Z., Marsden, K. R., & Gibson, D. L. (2016) · Microbiome · doi:10.1186/s40168-016-0189-7 · Cited by 476

Preferred interpersonal distances: A global comparison

Sorokowska, A., Sorokowski, P., Hilpert, P., Cantarero, K., Frackowiak, T., Ahmadi, K., Alghraibeh, A. M., Aryeetey, R., Bertoni, A., Bettache, K., et al. (2017) · Journal of Cross-Cultural Psychology · doi:10.1177/0022022117698039 · Cited by 468

Patterns of implicit and explicit attitudes: I. Long-term change and stability from 2007 to 2016

Charlesworth, T. E. S., & Banaji, M. R. (2019) · Psychological Science · doi:10.1177/0956797618813087 · Cited by 466

Temporal Variation in Heat–Mortality Associations: A Multicountry Study

Gasparrini, A., Guo, Y., Hashizume, M., Kinney, P. L., Petkova, E. P., Lavigne, E., Zanobetti, A., Schwartz, J. D., Tobias, A., Leone, M., et al. (2015) · Environmental Health Perspectives · doi:10.1289/ehp.1409070 · Cited by 470

Internet interventions for mental health in university students: A systematic review and meta‐analysis

Harrer, M., Adam, S. H., Baumeister, H., Cuijpers, P., Karyotaki, E., Auerbach, R. P., Kessler, R. C., Bruffaerts, R., Berking, M., & Ebert, D. D. (2019) · International Journal of Methods in Psychiatric Research · doi:10.1002/mpr.1759 · Cited by 445

CSF A β 42/A β 40 and A β 42/A β 38 ratios: better diagnostic markers of Alzheimer disease

Janelidze, S., Zetterberg, H., Mattsson, N., Palmqvist, S., Vanderstichele, H., Lindberg, O., van Westen, D., Stomrud, E., Minthon, L., Blennow, K., et al. (2016) · Annals of Clinical and Translational Neurology · doi:10.1002/acn3.274 · Cited by 456

The role of fear avoidance beliefs as a prognostic factor for outcome in patients with nonspecific low back pain: A systematic review

Wertli, M. M., Rasmussen-Barr, E., Weiser, S., Bachmann, L. M., & Brunner, F. (2014) · The Spine Journal · doi:10.1016/j.spinee.2013.09.036 · Cited by 479

Firth's logistic regression with rare events: accurate effect estimates and predictions?

Puhr, R., Heinze, G., Nold, M., Lusa, L., & Geroldinger, A. (2017) · Statistics in Medicine · doi:10.1002/sim.7273 · Cited by 483

A universal scaling relationship between body mass and proximal limb bone dimensions in quadrupedal terrestrial tetrapods

Campione, N. E., & Evans, D. C. (2012) · BMC Biology · doi:10.1186/1741-7007-10-60 · Cited by 475

The Effects of Changes in Utilization and Technological Advancements of Cross-Sectional Imaging on Radiologist Workload

McDonald, R. J., Schwartz, K. M., Eckel, L. J., Diehn, F. E., Hunt, C. H., Bartholmai, B. J., Erickson, B. J., & Kallmes, D. F. (2015) · Academic Radiology · doi:10.1016/j.acra.2015.05.007 · Cited by 460

Using Principal Components and Factor Analysis in Animal Behaviour Research: Caveats and Guidelines

Budaev, S. V. (2010) · Ethology · doi:10.1111/j.1439-0310.2010.01758.x · Cited by 460

A graphical tool for locating inconsistency in network meta-analyses

Krahn, U., Binder, H., & König, J. (2013) · BMC Medical Research Methodology · doi:10.1186/1471-2288-13-35 · Cited by 458

Susceptibility genes for age-related maculopathy on chromosome 10q26

Jakobsdottir, J., Conley, Y. P., Weeks, D. E., Mah, T. S., Ferrell, R. E., & Gorin, M. B. (2005) · The American Journal of Human Genetics · doi:10.1086/444437 · Cited by 470

Self-regulated learning training programs enhance university students’ academic performance, self-regulated learning strategies, and motivation: A meta-analysis

Theobald, M. (2021) · Contemporary Educational Psychology · doi:10.1016/j.cedpsych.2021.101976 · Cited by 478

Effects of conventional and biodegradable microplastics on a marine ecosystem engineer (Arenicola marina) and sediment nutrient cycling

Green, D. S., Boots, B., Sigwart, J., Jiang, S., & Rocha, C. (2016) · Environmental Pollution · doi:10.1016/j.envpol.2015.10.010 · Cited by 463

A quantitative index of land-use intensity in grasslands: Integrating mowing, grazing and fertilization

Blüthgen, N., Dormann, C. F., Prati, D., Klaus, V. H., Kleinebecker, T., Hölzel, N., Alt, F., Boch, S., Gockel, S., Hemp, A., et al. (2012) · Basic and Applied Ecology · doi:10.1016/j.baae.2012.04.001 · Cited by 465

Making sense of recommendations

Yeomans, M., Shah, A., Mullainathan, S., & Kleinberg, J. (2019) · Journal of Behavioral Decision Making · doi:10.1002/bdm.2118 · Cited by 459

Comparing bioinformatic pipelines for microbial 16S rRNA amplicon sequencing

Prodan, A., Tremaroli, V., Brolin, H., Zwinderman, A. H., Nieuwdorp, M., & Levin, E. (2020) · PLOS ONE · doi:10.1371/journal.pone.0227434 · Cited by 462

Interpreting prevotella and bacteroides as biomarkers of diet and lifestyle

Gorvitovskaia, A., Holmes, S. P., & Huse, S. M. (2016) · Microbiome · doi:10.1186/s40168-016-0160-7 · Cited by 461

A unified framework of longitudinal models to examine reciprocal relations.

Usami, S., Murayama, K., & Hamaker, E. L. (2019) · Psychological Methods · doi:10.1037/met0000210 · Cited by 437

Extensive Heterogeneity and Intrinsic Variation in Spatial Genome Organization

Finn, E. H., Pegoraro, G., Brandão, H. B., Valton, A.-L., Oomen, M. E., Dekker, J., Mirny, L., & Misteli, T. (2019) · Cell · doi:10.1016/j.cell.2019.01.020 · Cited by 459

Monitoring Change in Vertebrate Abundance: the Living Planet Index

COLLEN, B., LOH, J., WHITMEE, S., McRAE, L., AMIN, R., & BAILLIE, J. E. M. (2009) · Conservation Biology · doi:10.1111/j.1523-1739.2008.01117.x · Cited by 441

CalR: A Web-Based Analysis Tool for Indirect Calorimetry Experiments

Mina, A. I., LeClair, R. A., LeClair, K. B., Cohen, D. E., Lantier, L., & Banks, A. S. (2018) · Cell Metabolism · doi:10.1016/j.cmet.2018.06.019 · Cited by 458

Moderate alcohol consumption as risk factor for adverse brain outcomes and cognitive decline: longitudinal cohort study

Topiwala, A., Allan, C. L., Valkanova, V., Zsoldos, E., Filippini, N., Sexton, C., Mahmood, A., Fooks, P., Singh-Manoux, A., Mackay, C. E., et al. (2017) · BMJ · doi:10.1136/bmj.j2353 · Cited by 455

Broad Epigenetic Signature of Maternal Care in the Brain of Adult Rats

McGowan, P. O., Suderman, M., Sasaki, A., Huang, T. C. T., Hallett, M., Meaney, M. J., & Szyf, M. (2011) · PLoS ONE · doi:10.1371/journal.pone.0014739 · Cited by 452

Teacher stress and burnout in urban middle schools: Associations with job demands, resources, and effective classroom practices

Bottiani, J. H., Duran, C. A. K., Pas, E. T., & Bradshaw, C. P. (2019) · Journal of School Psychology · doi:10.1016/j.jsp.2019.10.002 · Cited by 454

Use of allele scores as instrumental variables for Mendelian randomization

Burgess, S., & Thompson, S. G. (2013) · International Journal of Epidemiology · doi:10.1093/ije/dyt093 · Cited by 452

Translation and Validation of Body Image Instruments: Challenges, Good Practice Guidelines, and Reporting Recommendations for Test Adaptation

Swami, V., & Barron, D. (2019) · Body Image · doi:10.1016/j.bodyim.2018.08.014 · Cited by 454

Total-Evidence Dating under the Fossilized Birth–Death Process

Zhang, C., Stadler, T., Klopfstein, S., Heath, T. A., & Ronquist, F. (2016) · Systematic Biology · doi:10.1093/sysbio/syv080 · Cited by 419

AGA Institute Rapid Review of the Gastrointestinal and Liver Manifestations of COVID-19, Meta-Analysis of International Data, and Recommendations for the Consultative Management of Patients with COVID-19

Sultan, S., Altayar, O., Siddique, S. M., Davitkov, P., Feuerstein, J. D., Lim, J. K., Falck-Ytter, Y., & El-Serag, H. B. (2020) · Gastroenterology · doi:10.1053/j.gastro.2020.05.001 · Cited by 437

Defining the features and duration of antibody responses to SARS-CoV-2 infection associated with disease severity and outcome

Röltgen, K., Powell, A. E., Wirz, O. F., Stevens, B. A., Hogan, C. A., Najeeb, J., Hunter, M., Wang, H., Sahoo, M. K., Huang, C., et al. (2020) · Science Immunology · doi:10.1126/sciimmunol.abe0240 · Cited by 452

Validation and Psychometric Evaluation of the Italian Version of the Fear of COVID-19 Scale

Soraci, P., Ferrari, A., Abbiati, F. A., Del Fante, E., De Pace, R., Urso, A., & Griffiths, M. D. (2022) · International Journal of Mental Health and Addiction · doi:10.1007/s11469-020-00277-1 · Cited by 427

Analysis of a SARS-CoV-2-Infected Individual Reveals Development of Potent Neutralizing Antibodies with Limited Somatic Mutation

Seydoux, E., Homad, L. J., MacCamy, A. J., Parks, K. R., Hurlburt, N. K., Jennewein, M. F., Akins, N. R., Stuart, A. B., Wan, Y.-H., Feng, J., et al. (2020) · Immunity · doi:10.1016/j.immuni.2020.06.001 · Cited by 364

Maternal residence near agricultural pesticide applications and autism spectrum disorders among children in the California central valley

Roberts, E. M., English, P. B., Grether, J. K., Windham, G. C., Somberg, L., & Wolff, C. (2007) · Environmental Health Perspectives · doi:10.1289/ehp.10168 · Cited by 442

Admixture in Latin America: Geographic Structure, Phenotypic Diversity and Self-Perception of Ancestry Based on 7,342 Individuals

Ruiz-Linares, A., Adhikari, K., Acuña-Alonzo, V., Quinto-Sanchez, M., Jaramillo, C., Arias, W., Fuentes, M., Pizarro, M., Everardo, P., de Avila, F., et al. (2014) · PLoS Genetics · doi:10.1371/journal.pgen.1004572 · Cited by 442

Forest bolsters bird abundance, pest control and coffee yield

Karp, D. S., Mendenhall, C. D., Sandí, R. F., Chaumont, N., Ehrlich, P. R., Hadly, E. A., & Daily, G. C. (2013) · Ecology Letters · doi:10.1111/ele.12173 · Cited by 432

Association between 7 years of intensive treatment of type 1 diabetes and long-term mortality

Orchard, T. J., Nathan, D. M., Zinman, B., Cleary, P., Brillon, D., Backlund, J.-Y. C., & Lachin, J. M. (2015) · JAMA · doi:10.1001/jama.2014.16107 · Cited by 437

Preliminary Results of an Open Label Study of Heart Rate Variability Biofeedback for the Treatment of Major Depression

Karavidas, M. K., Lehrer, P. M., Vaschillo, E., Vaschillo, B., Marin, H., Buyske, S., Malinovsky, I., Radvanski, D., & Hassett, A. (2007) · Applied Psychophysiology and Biofeedback · doi:10.1007/s10484-006-9029-z · Cited by 440

Modeling zero-inflated count data with glmmTMB

Brooks, M. E., Kristensen, K., van Benthem, K. J., Magnusson, A., Berg, C. W., Nielsen, A., Skaug, H. J., Mächler, M., & Bolker, B. M. (2017) · doi:10.1101/132753 · Cited by 440

Early risk and protective factors for problem gambling: a systematic review and meta-analysis of longitudinal studies

Dowling, N. A., Merkouris, S. S., Greenwood, C. J., Oldenhof, E., Toumbourou, J. W., & Youssef, G. J. (2017) · Clinical Psychology Review · doi:10.1016/j.cpr.2016.10.008 · Cited by 439

The Revised Child Anxiety and Depression Scale-Short Version: Scale reduction via exploratory bifactor modeling of the broad anxiety factor.

Ebesutani, C., Reise, S. P., Chorpita, B. F., Ale, C., Regan, J., Young, J., Higa-McMillan, C., & Weisz, J. R. (2012) · Psychological Assessment · doi:10.1037/a0027283 · Cited by 439

R Package gdistance : Distances and Routes on Geographical Grids

van Etten, J. (2017) · Journal of Statistical Software · doi:10.18637/jss.v076.i13 · Cited by 439

Emotion regulation strategies in daily life: mindfulness, cognitive reappraisal and emotion suppression

Brockman, R., Ciarrochi, J., Parker, P., & Kashdan, T. (2017) · Cognitive Behaviour Therapy · doi:10.1080/16506073.2016.1218926 · Cited by 427

Screening for pre-eclampsia by maternal factors and biomarkers at 11–13 weeks’ gestation

Tan, M. Y., Syngelaki, A., Poon, L. C., Rolnik, D. L., O'Gorman, N., Delgado, J. L., Akolekar, R., Konstantinidou, L., Tsavdaridou, M., Galeva, S., et al. (2018) · Ultrasound in Obstetrics & Gynecology · doi:10.1002/uog.19112 · Cited by 438

Meta-analysis Confirms CR1, CLU, and PICALM as Alzheimer Disease Risk Loci and Reveals Interactions With APOE Genotypes

Jun, G. (2010) · Archives of Neurology · doi:10.1001/archneurol.2010.201 · Cited by 422

The Impact of Peer Assessment on Academic Performance: A Meta-analysis of Control Group Studies

Double, K. S., McGrane, J. A., & Hopfenbeck, T. N. (2020) · Educational Psychology Review · doi:10.1007/s10648-019-09510-3 · Cited by 446

Modulating functional connectivity patterns and topological functional organization of the human brain with transcranial direct current stimulation

Polanía, R., Nitsche, M. A., & Paulus, W. (2011) · Human Brain Mapping · doi:10.1002/hbm.21104 · Cited by 434

A Chemoproteomic Approach to Query the Degradable Kinome Using a Multi-kinase Degrader

Huang, H.-T., Dobrovolsky, D., Paulk, J., Yang, G., Weisberg, E. L., Doctor, Z. M., Buckley, D. L., Cho, J.-H., Ko, E., Jang, J., et al. (2018) · Cell Chemical Biology · doi:10.1016/j.chembiol.2017.10.005 · Cited by 437

B‐cell diversity decreases in old age and is correlated with poor health status

Gibson, K. L., Wu, Y., Barnett, Y., Duggan, O., Vaughan, R., Kondeatis, E., Nilsson, B., Wikby, A., Kipling, D., & Dunn‐Walters, D. K. (2009) · Aging Cell · doi:10.1111/j.1474-9726.2008.00443.x · Cited by 433

Climate change and wildfire in California

Westerling, A. L., & Bryant, B. P. (2008) · Climatic Change · doi:10.1007/s10584-007-9363-z · Cited by 432

Peripheral Cytokine Levels and Response to Antidepressant Treatment in Depression: A Systematic Review and Meta-Analysis

Liu, J. J., Wei, Y. B., Strawbridge, R., Bao, Y., Chang, S., Shi, L., Que, J., Gadad, B. S., Trivedi, M. H., Kelsoe, J. R., et al. (2020) · Molecular Psychiatry · doi:10.1038/s41380-019-0474-5 · Cited by 429

Genome‐wide mapping of plasma protein QTLs identifies putatively causal genes and pathways for cardiovascular disease

Yao, C., Chen, G., Song, C., Keefe, J., Mendelson, M., Huan, T., Sun, B. B., Laser, A., Maranville, J. C., Wu, H., et al. (2018) · Nature Communications · doi:10.1038/s41467-018-05512-x · Cited by 431

Microplastic addition alters the microbial community structure and stimulates soil carbon dioxide emissions in vegetable-growing soil

Gao, B., Yao, H., Li, Y., & Zhu, Y. (2020) · Environmental Toxicology and Chemistry · doi:10.1002/etc.4916 · Cited by 430

Physical activity and incident type 2 diabetes mellitus: a systematic review and dose-response meta-analysis of prospective cohort studies.

Smith, A. D., Crippa, A., Woodcock, J., & Brage, S. (2016) · Diabetologia · doi:10.1007/s00125-016-4079-0 · Cited by 430

Introduction matters: Manipulating trust in automation and reliance in automated driving

Körber, M., Baseler, E., & Bengler, K. (2018) · Applied Ergonomics · doi:10.1016/j.apergo.2017.07.006 · Cited by 418

Metabolomics in Early Alzheimer's Disease: Identification of Altered Plasma Sphingolipidome Using Shotgun Lipidomics

Han, X., Rozen, S., Boyle, S. H., Hellegers, C., Cheng, H., Burke, J. R., Welsh-Bohmer, K. A., Doraiswamy, P. M., & Kaddurah-Daouk, R. (2011) · PLoS ONE · doi:10.1371/journal.pone.0021643 · Cited by 429

Metabolite profiling stratifies pancreatic ductal adenocarcinomas into subtypes with distinct sensitivities to metabolic inhibitors

Daemen, A., Peterson, D., Sahu, N., McCord, R., Du, X., Liu, B., Kowanetz, K., Hong, R., Moffat, J., Gao, M., et al. (2015) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1501605112 · Cited by 398

A comparative study of machine learning classifiers for modeling travel mode choice

Hagenauer, J., & Helbich, M. (2017) · Expert Systems with Applications · doi:10.1016/j.eswa.2017.01.057 · Cited by 424

A multidimensional systems biology analysis of cellular senescence in aging and disease

Avelar, R. A., Ortega, J. G., Tacutu, R., Tyler, E. J., Bennett, D., Binetti, P., Budovsky, A., Chatsirisupachai, K., Johnson, E., Murray, A., et al. (2020) · Genome Biology · doi:10.1186/s13059-020-01990-9 · Cited by 399

Universal and blocking primer mismatches limit the use of high‐throughput DNA sequencing for the quantitative metabarcoding of arthropods

Piñol, J., Mir, G., Gomez‐Polo, P., & Agustí, N. (2015) · Molecular Ecology Resources · doi:10.1111/1755-0998.12355 · Cited by 423

Open MEE : Intuitive, open‐source software for meta‐analysis in ecology and evolutionary biology

Wallace, B. C., Lajeunesse, M. J., Dietz, G., Dahabreh, I. J., Trikalinos, T. A., Schmid, C. H., & Gurevitch, J. (2017) · Methods in Ecology and Evolution · doi:10.1111/2041-210x.12708 · Cited by 421

Viral zoonotic risk is homogenous among taxonomic orders of mammalian and avian reservoir hosts

Mollentze, N., & Streicker, D. G. (2020) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1919176117 · Cited by 387

Controlling Bias and inflation in epigenome- and transcriptome-wide association studies using the empirical null distribution

the BIOS Consortium, van Iterson, M., van Zwet, E. W., & Heijmans, B. T. (2017) · Genome biology · doi:10.1186/s13059-016-1131-9 · Cited by 421

Resting energy expenditure, calorie and protein consumption in critically ill patients: a retrospective cohort study

Zusman, O., Theilla, M., Cohen, J., Kagan, I., Bendavid, I., & Singer, P. (2016) · Critical Care · doi:10.1186/s13054-016-1538-4 · Cited by 421

Adjusting for Publication Bias in Meta-Analysis: An Evaluation of Selection Methods and Some Cautionary Notes

McShane, B. B., Böckenholt, U., & Hansen, K. T. (2016) · Perspectives on Psychological Science · doi:10.1177/1745691616662243 · Cited by 398

The Human Nasal Microbiota and Staphylococcus aureus Carriage

Frank, D. N., Feazel, L. M., Bessesen, M. T., Price, C. S., Janoff, E. N., & Pace, N. R. (2010) · PLoS ONE · doi:10.1371/journal.pone.0010598 · Cited by 422

Neuropathic pain prevalence following spinal cord injury: A systematic review and meta‐analysis

Burke, D., Fullen, B. M., Stokes, D., & Lennon, O. (2017) · European Journal of Pain · doi:10.1002/ejp.905 · Cited by 402

Predictability of biomass burning in response to climate changes

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Methane yield phenotypes linked to differential gene expression in the sheep rumen microbiome

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