Modern Applied Statistics with S

Support-vector networks

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The Nature of Statistical Learning Theory

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The Elements of Statistical Learning

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Multilayer feedforward networks are universal approximators

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Finite Mixture Models

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Mixed-Effects Models in S and S-PLUS

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A note on a general definition of the coefficient of determination

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On a Least Squares Adjustment of a Sampled Frequency Table When the Expected Marginal Totals are Known

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Metagenomic biomarker discovery and explanation

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mice: Multivariate Imputation by Chained Equations in R

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pROC: an open-source package for R and S+ to analyze and compare ROC curves

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Generalized linear mixed models: a practical guide for ecology and evolution

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Building Predictive Models in R Using the caret Package

Kuhn, M. (2008) · Journal of Statistical Software · doi:10.18637/jss.v028.i05 · Cited by 8,863

Predicting effective microRNA target sites in mammalian mRNAs

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SoilGrids250m: Global gridded soil information based on machine learning

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MatchIt : Nonparametric Preprocessing for Parametric Causal Inference

Ho, D. E., Imai, K., King, G., & Stuart, E. A. (2011) · Journal of Statistical Software · doi:10.18637/jss.v042.i08 · Cited by 4,385

Methods to account for spatial autocorrelation in the analysis of species distributional data: a review.

F. Dormann, C., M. McPherson, J., B. Araújo, M., Bivand, R., Bolliger, J., Carl, G., G. Davies, R., Hirzel, A., Jetz, W., Daniel Kissling, W., et al. (2007) · Ecography · doi:10.1111/j.2007.0906-7590.05171.x · Cited by 3,433

Text as Data: The Promise and Pitfalls of Automatic Content Analysis Methods for Political Texts

Grimmer, J., & Stewart, B. M. (2013) · Political Analysis · doi:10.1093/pan/mps028 · Cited by 3,297

Multiple imputation of discrete and continuous data by fully conditional specification

van Buuren, S. (2007) · Statistical Methods in Medical Research · doi:10.1177/0962280206074463 · Cited by 2,827

RNA-seq: An assessment of technical reproducibility and comparison with gene expression arrays

Marioni, J. C., Mason, C. E., Mane, S. M., Stephens, M., & Gilad, Y. (2008) · Genome Research · doi:10.1101/gr.079558.108 · Cited by 2,771

The coefficient of determination R 2 and intra-class correlation coefficient from generalized linear mixed-effects models revisited and expanded

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Repeatability for Gaussian and non-Gaussian data: a practical guide for biologists.

Nakagawa, S., & Schielzeth, H. (2010) · Biological Reviews · doi:10.1111/j.1469-185x.2010.00141.x · Cited by 2,584

A dendrochronology program library in R (dplR)

Bunn, A. G. (2008) · Dendrochronologia · doi:10.1016/j.dendro.2008.01.002 · Cited by 2,073

Peeking Inside the Black Box: Visualizing Statistical Learning With Plots of Individual Conditional Expectation

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Automatic denoising of functional MRI data: Combining independent component analysis and hierarchical fusion of classifiers

Salimi-Khorshidi, G., Douaud, G., Beckmann, C. F., Glasser, M. F., Griffanti, L., & Smith, S. M. (2014) · NeuroImage · doi:10.1016/j.neuroimage.2013.11.046 · Cited by 1,770

Anthropogenic drivers of 2013–2017 trends in summer surface ozone in China

Li, K., Jacob, D. J., Liao, H., Shen, L., Zhang, Q., & Bates, K. H. (2019) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1812168116 · Cited by 1,804

Visualizing the effects of predictor variables in black box supervised learning models

Apley, D. W., & Zhu, J. (2020) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/rssb.12377 · Cited by 1,535

Biodiversity and ecosystem multifunctionality

Hector, A., & Bagchi, R. (2007) · Nature · doi:10.1038/nature05947 · Cited by 1,412

Complex archaea that bridge the gap between prokaryotes and eukaryotes

Spang, A., Saw, J. H., Jørgensen, S. L., Zaremba-Niedzwiedzka, K., Martijn, J., Lind, A. E., van Eijk, R., Schleper, C., Guy, L., & Ettema, T. J. G. (2015) · Nature · doi:10.1038/nature14447 · Cited by 1,324

A Kenward-Roger Approximation and Parametric Bootstrap Methods for Tests in Linear Mixed Models - The R Package pbkrtest

Halekoh, U., & Højsgaard, S. (2014) · Journal of Statistical Software · doi:10.18637/jss.v059.i09 · Cited by 1,324

The human transcriptome across tissues and individuals

Melé, M., Ferreira, P. G., Reverter, F., DeLuca, D. S., Monlong, J., Sammeth, M., Young, T. R., Goldmann, J. M., Pervouchine, D. D., Sullivan, T. J., et al. (2015) · Science · doi:10.1126/science.aaa0355 · Cited by 1,304

Individual patient data meta-analysis of controlled attenuation parameter (CAP) technology for assessing steatosis

Karlas, T., Petroff, D., Sasso, M., Fan, J.-G., Mi, Y.-Q., de Lédinghen, V., Kumar, M., Lupsor-Platon, M., Han, K.-H., Cardoso, A. C., et al. (2017) · Journal of Hepatology · doi:10.1016/j.jhep.2016.12.022 · Cited by 1,275

SoilGrids1km — Global Soil Information Based on Automated Mapping

Hengl, T., de Jesus, J. M., MacMillan, R. A., Batjes, N. H., Heuvelink, G. B. M., Ribeiro, E., Samuel-Rosa, A., Kempen, B., Leenaars, J. G. B., Walsh, M. G., et al. (2014) · PLoS ONE · doi:10.1371/journal.pone.0105992 · Cited by 1,268

Phytools 2.0: an updated R ecosystem for phylogenetic comparative methods (and other things)

Revell, L. J. (2024) · PeerJ · doi:10.7717/peerj.16505 · Cited by 1,244

Dealing with outliers and offsets in radiocarbon dating

Bronk Ramsey, C. (2009) · Radiocarbon · doi:10.1017/s0033822200034093 · Cited by 1,204

Cancer-associated adipocytes exhibit an activated phenotype and contribute to breast cancer invasion

Dirat, B., Bochet, L., Dabek, M., Daviaud, D., Dauvillier, S., Majed, B., Wang, Y. Y., Meulle, A., Salles, B., Le Gonidec, S., et al. (2011) · Cancer Research · doi:10.1158/0008-5472.can-10-3323 · Cited by 1,134

Using observation-level random effects to model overdispersion in count data in ecology and evolution

Harrison, X. A. (2014) · PeerJ · doi:10.7717/peerj.616 · Cited by 1,150

Multi-site assessment of the precision and reproducibility of multiple reaction monitoring–based measurements of proteins in plasma

Addona, T. A., Abbatiello, S. E., Schilling, B., Skates, S. J., Mani, D. R., Bunk, D. M., Spiegelman, C. H., Zimmerman, L. J., Ham, A.-J. L., Keshishian, H., et al. (2009) · Nature Biotechnology · doi:10.1038/nbt.1546 · Cited by 1,037

Conclusions beyond support: overconfident estimates in mixed models

Schielzeth, H., & Forstmeier, W. (2009) · Behavioral Ecology · doi:10.1093/beheco/arn145 · Cited by 952

Tumor Mutational Burden as a Predictive Biomarker in Solid Tumors

Sha, D., Jin, Z., Budczies, J., Kluck, K., Stenzinger, A., & Sinicrope, F. A. (2020) · Cancer Discovery · doi:10.1158/2159-8290.cd-20-0522 · Cited by 859

An evaluation of methods for modelling species distributions

Segurado, P., & Araújo, M. B. (2004) · Journal of Biogeography · doi:10.1111/j.1365-2699.2004.01076.x · Cited by 875

A de novo paradigm for mental retardation.

Vissers, L. E. L. M., de Ligt, J., Gilissen, C., Janssen, I., Steehouwer, M., de Vries, P., van Lier, B., Arts, P., Wieskamp, N., del Rosario, M., et al. (2010) · Nature Genetics · doi:10.1038/ng.712 · Cited by 852

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

Single-cell ChIP-seq reveals cell subpopulations defined by chromatin state

Rotem, A., Ram, O., Shoresh, N., Sperling, R. A., Goren, A., Weitz, D. A., & Bernstein, B. E. (2015) · Nature Biotechnology · doi:10.1038/nbt.3383 · Cited by 854

Biodiversity enhances ecosystem multifunctionality across trophic levels and habitats

Lefcheck, J. S., Byrnes, J. E. K., Isbell, F., Gamfeldt, L., Griffin, J. N., Eisenhauer, N., Hensel, M. J. S., Hector, A., Cardinale, B. J., & Duffy, J. E. (2015) · Nature Communications · doi:10.1038/ncomms7936 · Cited by 840

Analyzing ordinal data with metric models: What could possibly go wrong?

Liddell, T. M., & Kruschke, J. K. (2018) · Journal of Experimental Social Psychology · doi:10.1016/j.jesp.2018.08.009 · Cited by 805

Changes in fire and climate in the eastern Iberian Peninsula (Mediterranean basin).

Pausas, J. G. (2004) · Climatic Change · doi:10.1023/b:clim.0000018508.94901.9c · Cited by 798

Climate change and deepening of the North Sea fish assemblage: a biotic indicator of warming seas

Dulvy, N. K., Rogers, S. I., Jennings, S., Stelzenmüller, V., Dye, S. R., & Skjoldal, H. R. (2008) · Journal of Applied Ecology · doi:10.1111/j.1365-2664.2008.01488.x · Cited by 782

Asynchronous carbon sink saturation in African and Amazonian tropical forests

Hubau, W., Lewis, S. L., Phillips, O. L., Affum-Baffoe, K., Beeckman, H., Cuní-Sanchez, A., Daniels, A. K., Ewango, C. E. N., Fauset, S., Mukinzi, J. M., et al. (2020) · Nature · doi:10.1038/s41586-020-2035-0 · Cited by 782

Predictive 5-year survivorship model of cystic fibrosis

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Intestinal microbiome is related to lifetime antibiotic use in Finnish pre-school children

Korpela, K., Salonen, A., Virta, L. J., Kekkonen, R. A., Forslund, K., Bork, P., & de Vos, W. M. (2016) · Nature Communications · doi:10.1038/ncomms10410 · Cited by 698

Changes in fire regimes since the Last Glacial Maximum: an assessment based on a global synthesis and analysis of charcoal data

Power, M. J., Marlon, J., Ortiz, N., Bartlein, P. J., Harrison, S. P., Mayle, F. E., Ballouche, A., Bradshaw, R. H. W., Carcaillet, C., Cordova, C., et al. (2008) · Climate Dynamics · doi:10.1007/s00382-007-0334-x · Cited by 717

Beyond Western, Educated, Industrial, Rich, and Democratic (WEIRD) Psychology: Measuring and Mapping Scales of Cultural and Psychological Distance

Muthukrishna, M., Bell, A. V., Henrich, J., Curtin, C. M., Gedranovich, A., McInerney, J., & Thue, B. (2020) · Psychological Science · doi:10.1177/0956797620916782 · Cited by 704

Longitudinal Mapping of Cortical Thickness and Clinical Outcome in Children and Adolescents With Attention-Deficit/Hyperactivity Disorder

Shaw, P., Lerch, J., Greenstein, D., Sharp, W., Clasen, L., Evans, A., Giedd, J., Castellanos, F. X., & Rapoport, J. (2006) · Archives of General Psychiatry · doi:10.1001/archpsyc.63.5.540 · Cited by 703

Ecology Drives the Worldwide Distribution of Human Diseases

Guernier, V., Hochberg, M. E., & Guégan, J.-F. (2004) · PLoS Biology · doi:10.1371/journal.pbio.0020141 · Cited by 693

The determination of standard metabolic rate in fishes

Chabot, D., Steffensen, J. F., & Farrell, A. P. (2016) · Journal of Fish Biology · doi:10.1111/jfb.12845 · Cited by 667

Machine learning and its applications to biology

Tarca, A. L., Carey, V. J., Chen, X.-W., Romero, R., & Drăghici, S. (2007) · PLoS Computational Biology · doi:10.1371/journal.pcbi.0030116 · Cited by 656

The uncertain nature of absences and their importance in species distribution modelling

Lobo, J. M., Jiménez‐Valverde, A., & Hortal, J. (2010) · Ecography · doi:10.1111/j.1600-0587.2009.06039.x · Cited by 656

The tiny eukaryote Ostreococcus provides genomic insights into the paradox of plankton speciation

Palenik, B., Grimwood, J., Aerts, A., Rouzé, P., Salamov, A., Putnam, N., Dupont, C., Jorgensen, R., Derelle, E., Rombauts, S., et al. (2007) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0611046104 · Cited by 625

Evaluating multiple classifiers for stock price direction prediction

Ballings, M., Van den Poel, D., Hespeels, N., & Gryp, R. (2015) · Expert Systems with Applications · doi:10.1016/j.eswa.2015.05.013 · Cited by 611

Single-cell sequencing of human midbrain reveals glial activation and a Parkinson-specific neuronal state

Smajić, S., Prada-Medina, C. A., Landoulsi, Z., Ghelfi, J., Delcambre, S., Dietrich, C., Jarazo, J., Henck, J., Balachandran, S., Pachchek, S., et al. (2022) · Brain · doi:10.1093/brain/awab446 · Cited by 590

Morality in everyday life

Hofmann, W., Wisneski, D. C., Brandt, M. J., & Skitka, L. J. (2014) · Science · doi:10.1126/science.1251560 · Cited by 575

Characterization of ancient and modern genomes by SNP detection and phylogenomic and metagenomic analysis using PALEOMIX

Schubert, M., Ermini, L., Sarkissian, C. D., Jónsson, H., Ginolhac, A., Schaefer, R., Martin, M. D., Fernández, R., Kircher, M., McCue, M., et al. (2014) · Nature Protocols · doi:10.1038/nprot.2014.063 · Cited by 562

Getting Under the Hood: How and for Whom Does Increasing Course Structure Work?

Eddy, S. L., & Hogan, K. A. (2014) · CBE—Life Sciences Education · doi:10.1187/cbe.14-03-0050 · Cited by 553

Accuracy and calibration of commercial oligonucleotide and custom cDNA microarrays

Yuen, T. (2002) · Nucleic Acids Research · doi:10.1093/nar/30.10.e48 · Cited by 520

The Problem with Having Two Watches: Assessment of Fit When RMSEA and CFI Disagree

Lai, K., & Green, S. B. (2016) · Multivariate Behavioral Research · doi:10.1080/00273171.2015.1134306 · Cited by 514

Intra-urban societal vulnerability to extreme heat: The role of heat exposure and the built environment, socioeconomics, and neighborhood stability

Uejio, C. K., Wilhelmi, O. V., Golden, J. S., Mills, D. M., Gulino, S. P., & Samenow, J. P. (2011) · Health & Place · doi:10.1016/j.healthplace.2010.12.005 · Cited by 504

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

Genome sequencing analysis identifies new loci associated with Lewy body dementia and provides insights into its genetic architecture

Chia, R., Sabir, M. S., Bandres-Ciga, S., Saez-Atienzar, S., Reynolds, R. H., Gustavsson, E., Walton, R. L., Ahmed, S., Viollet, C., Ding, J., et al. (2021) · Nature Genetics · doi:10.1038/s41588-021-00785-3 · Cited by 438

Which is a better predictor of plant traits: temperature or precipitation?

Moles, A. T., Perkins, S. E., Laffan, S. W., Flores‐Moreno, H., Awasthy, M., Tindall, M. L., Sack, L., Pitman, A., Kattge, J., Aarssen, L. W., et al. (2014) · Journal of Vegetation Science · doi:10.1111/jvs.12190 · Cited by 481

The evolving role of preprints in the dissemination of COVID-19 research and their impact on the science communication landscape

Fraser, N., Brierley, L., Dey, G., Polka, J. K., Pálfy, M., Nanni, F., & Coates, J. A. (2021) · PLOS Biology · doi:10.1371/journal.pbio.3000959 · Cited by 555

Cliff´s Delta Calculator: A non-parametric effect size program for two groups of observations

Macbeth, G., Razumiejczyk, E., & Ledesma, R. D. (2010) · Universitas Psychologica · doi:10.11144/javeriana.upsy10-2.cdcp · Cited by 442

Using control genes to correct for unwanted variation in microarray data

Gagnon-Bartsch, J. A., & Speed, T. P. (2012) · Biostatistics · doi:10.1093/biostatistics/kxr034 · Cited by 442

Weight consistency specifies regularities of macaque cortical networks

Markov, N. T., Misery, P., Falchier, A., Lamy, C., Vezoli, J., Quilodran, R., Gariel, M. A., Giroud, P., Ercsey-Ravasz, M., Pilaz, L. J., et al. (2011) · Cerebral Cortex · doi:10.1093/cercor/bhq201 · Cited by 386

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

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

The Relative Contributions of Aging, Health, and Lifestyle Factors to Serum Testosterone Decline in Men

Travison, T. G., Araujo, A. B., Kupelian, V., O’Donnell, A. B., & McKinlay, J. B. (2007) · The Journal of Clinical Endocrinology & Metabolism · doi:10.1210/jc.2006-1859 · Cited by 410

Identification of Individuals With Insulin Resistance Using Routine Clinical Measurements

Stern, S. E., Williams, K., Ferrannini, E., DeFronzo, R. A., Bogardus, C., & Stern, M. P. (2005) · Diabetes · doi:10.2337/diabetes.54.2.333 · Cited by 384

Predicting Six-Month Mortality for Patients Who Are on Maintenance Hemodialysis

Cohen, L. M., Ruthazer, R., Moss, A. H., & Germain, M. J. (2010) · Clinical Journal of the American Society of Nephrology · doi:10.2215/cjn.03860609 · Cited by 391

Traditional versus integrative behavioral couple therapy for significantly and chronically distressed married couples.

Christensen, A., Atkins, D. C., Berns, S., Wheeler, J., Baucom, D. H., & Simpson, L. E. (2004) · Journal of Consulting and Clinical Psychology · doi:10.1037/0022-006x.72.2.176 · Cited by 380

Peripheral blood proteins predict mortality in idiopathic pulmonary fibrosis

Richards, T. J., Kaminski, N., Baribaud, F., Flavin, S., Brodmerkel, C., Horowitz, D., Li, K., Choi, J., Vuga, L. J., Lindell, K. O., et al. (2012) · American Journal of Respiratory and Critical Care Medicine · doi:10.1164/rccm.201101-0058oc · Cited by 381

Nebulosa recovers single-cell gene expression signals by kernel density estimation

Alquicira-Hernandez, J., & Powell, J. E. (2021) · Bioinformatics · doi:10.1093/bioinformatics/btab003 · Cited by 381

A systematic evaluation of normalization methods in quantitative label-free proteomics

Välikangas, T., Suomi, T., & Elo, L. L. (2016) · Briefings in Bioinformatics · doi:10.1093/bib/bbw095 · Cited by 336

An artificial neural network to estimate physical activity energy expenditure and identify physical activity type from an accelerometer

Staudenmayer, J., Pober, D., Crouter, S., Bassett, D., & Freedson, P. (2009) · Journal of Applied Physiology · doi:10.1152/japplphysiol.00465.2009 · Cited by 361

Peripheral blood mononuclear cell gene expression profiles predict poor outcome in idiopathic pulmonary fibrosis

Herazo-Maya, J. D., Noth, I., Duncan, S. R., Kim, S., Ma, S.-F., Tseng, G. C., Feingold, E., Juan-Guardela, B. M., Richards, T. J., Lussier, Y., et al. (2013) · Science Translational Medicine · doi:10.1126/scitranslmed.3005964 · Cited by 325

Cohesin-based chromatin interactions enable regulated gene expression within preexisting architectural compartments

Seitan, V. C., Faure, A. J., Zhan, Y., McCord, R. P., Lajoie, B. R., Ing-Simmons, E., Lenhard, B., Giorgetti, L., Heard, E., Fisher, A. G., et al. (2013) · Genome Research · doi:10.1101/gr.161620.113 · Cited by 318

Genome-Wide Association Meta-analysis of Neuropathologic Features of Alzheimer's Disease and Related Dementias

Beecham, G. W., Hamilton, K., Naj, A. C., Martin, E. R., Huentelman, M., Myers, A. J., Corneveaux, J. J., Hardy, J., Vonsattel, J.-P., Younkin, S. G., et al. (2014) · PLoS Genetics · doi:10.1371/journal.pgen.1004606 · Cited by 343

Socioeconomic Status (SES) and Children's Intelligence (IQ): In a UK-Representative Sample SES Moderates the Environmental, Not Genetic, Effect on IQ

Hanscombe, K. B., Trzaskowski, M., Haworth, C. M. A., Davis, O. S. P., Dale, P. S., & Plomin, R. (2012) · PLoS ONE · doi:10.1371/journal.pone.0030320 · Cited by 343

Deficient Hippocampal Neuron Expression of Proteasome, Ubiquitin, and Mitochondrial Genes in Multiple Schizophrenia Cohorts

Altar, C. A., Jurata, L. W., Charles, V., Lemire, A., Liu, P., Bukhman, Y., Young, T. A., Bullard, J., Yokoe, H., Webster, M. J., et al. (2005) · Biological Psychiatry · doi:10.1016/j.biopsych.2005.03.031 · Cited by 318

The citation advantage of linking publications to research data

Colavizza, G., Hrynaszkiewicz, I., Staden, I., Whitaker, K., & McGillivray, B. (2020) · PLOS ONE · doi:10.1371/journal.pone.0230416 · Cited by 335

Automated classification of bird and amphibian calls using machine learning: A comparison of methods

Acevedo, M. A., Corrada-Bravo, C. J., Corrada-Bravo, H., Villanueva-Rivera, L. J., & Aide, T. M. (2009) · Ecological Informatics · doi:10.1016/j.ecoinf.2009.06.005 · Cited by 317

Distinct subtypes of polycystic ovary syndrome with novel genetic associations: An unsupervised, phenotypic clustering analysis

Dapas, M., Lin, F. T. J., Nadkarni, G. N., Sisk, R., Legro, R. S., Urbanek, M., Hayes, M. G., & Dunaif, A. (2020) · PLOS Medicine · doi:10.1371/journal.pmed.1003132 · Cited by 297

Cognitive-existential group psychotherapy for women with primary breast cancer: a randomised controlled trial

Kissane, D. W., Bloch, S., Smith, G. C., Miach, P., Clarke, D. M., Ikin, J., Love, A., Ranieri, N., & McKenzie, D. (2003) · Psycho-Oncology · doi:10.1002/pon.683 · Cited by 306

Missing data in a multi-item instrument were best handled by multiple imputation at the item score level

Eekhout, I., de Vet, H. C. W., Twisk, J. W. R., Brand, J. P. L., de Boer, M. R., & Heymans, M. W. (2014) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2013.09.009 · Cited by 302

Predicting spatial patterns of fire on a southern California landscape

Syphard, A. D., Radeloff, V. C., Keuler, N. S., Taylor, R. S., Hawbaker, T. J., Stewart, S. I., & Clayton, M. K. (2008) · International Journal of Wildland Fire · doi:10.1071/wf07087 · Cited by 288

How to pre-process Raman spectra for reliable and stable models?

Bocklitz, T., Walter, A., Hartmann, K., Rösch, P., & Popp, J. (2011) · Analytica Chimica Acta · doi:10.1016/j.aca.2011.06.043 · Cited by 282

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Effective delivery of large genes to the retina by dual AAV vectors

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A systematic evaluation of single cell RNA-seq analysis pipelines

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Early bilingualism enhances mechanisms of false‐belief reasoning

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Ecological effects of ocean acidification and habitat complexity on reef-associated macroinvertebrate communities

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Land Management Practices Associated with House Loss in Wildfires

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