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

Hastie, T., Friedman, J., & Tibshirani, R. (2001) · Springer Series in Statistics · doi:10.1007/978-0-387-21606-5 · Cited by 24,162

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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Land Management Practices Associated with House Loss in Wildfires

Gibbons, P., van Bommel, L., Gill, A. M., Cary, G. J., Driscoll, D. A., Bradstock, R. A., Knight, E., Moritz, M. A., Stephens, S. L., & Lindenmayer, D. B. (2012) · PLoS ONE · doi:10.1371/journal.pone.0029212 · Cited by 237

Efficacy of Smartphone Applications for Smoking Cessation: A Randomized Clinical Trial

Bricker, J. B., Watson, N. L., Mull, K. E., Sullivan, B. M., & Heffner, J. L. (2020) · JAMA Internal Medicine · doi:10.1001/jamainternmed.2020.4055 · Cited by 207

Characterizing the molecular regulation of inhibitory immune checkpoints with multimodal single-cell screens

Papalexi, E., Mimitou, E. P., Butler, A. W., Foster, S., Bracken, B., Mauck, W. M., Wessels, H.-H., Hao, Y., Yeung, B. Z., Smibert, P., et al. (2021) · Nature Genetics · doi:10.1038/s41588-021-00778-2 · Cited by 237

Vasovagal syncope in medical students and their first-degree relatives

Serletis, A., Rose, S., Sheldon, A. G., & Sheldon, R. S. (2006) · European Heart Journal · doi:10.1093/eurheartj/ehl147 · Cited by 229

Intestinal Microbiota in Healthy U.S. Young Children and Adults—A High Throughput Microarray Analysis

Ringel-Kulka, T., Cheng, J., Ringel, Y., Salojärvi, J., Carroll, I., Palva, A., de Vos, W. M., & Satokari, R. (2013) · PLoS ONE · doi:10.1371/journal.pone.0064315 · Cited by 230

Separating measurement and expression models clarifies confusion in single-cell RNA sequencing analysis

Sarkar, A., & Stephens, M. (2021) · Nature Genetics · doi:10.1038/s41588-021-00873-4 · Cited by 224

Verifying the Solution from a Nonlinear Solver: A Case Study

McCullough, B. D., & Vinod, H. D. (2003) · American Economic Review · doi:10.1257/000282803322157133 · Cited by 220

A catastrophic tropical drought kills hydraulically vulnerable tree species

Powers, J. S., Vargas G., G., Brodribb, T. J., Schwartz, N. B., Pérez‐Aviles, D., Smith‐Martin, C. M., Becknell, J. M., Aureli, F., Blanco, R., Calderón‐Morales, E., et al. (2020) · Global Change Biology · doi:10.1111/gcb.15037 · Cited by 216

Comparisons of methods for multiple hypothesis testing in neuropsychological research

Blakesley, R. E., Mazumdar, S., Dew, M. A., Houck, P. R., Tang, G., Reynolds, C. F., & Butters, M. A. (2009) · Neuropsychology · doi:10.1037/a0012850 · Cited by 206

Integrating gene annotation with orthology inference at scale

Kirilenko, B. M., Munegowda, C., Osipova, E., Jebb, D., Sharma, V., Blumer, M., Morales, A. E., Ahmed, A.-W., Kontopoulos, D.-G., Hilgers, L., et al. (2023) · Science · doi:10.1126/science.abn3107 · Cited by 209

Modeling continuous response variables using ordinal regression

Liu, Q., Shepherd, B. E., Li, C., & Harrell, F. E. (2017) · Statistics in Medicine · doi:10.1002/sim.7433 · Cited by 208

The antipsychotic olanzapine interacts with the gut microbiome to cause weight gain in mouse

Morgan, A. P., Crowley, J. J., Nonneman, R. J., Quackenbush, C. R., Miller, C. N., Ryan, A. K., Bogue, M. A., Paredes, S. H., Yourstone, S., Carroll, I. M., et al. (2014) · PLoS ONE · doi:10.1371/journal.pone.0115225 · Cited by 197

Improving propensity score estimators’ robustness to model misspecification using super learner.

Pirracchio, R., Petersen, M. L., & van der Laan, M. (2015) · American Journal of Epidemiology · doi:10.1093/aje/kwu253 · Cited by 186

Gut permeability is related to body weight, fatty liver disease, and insulin resistance in obese individuals undergoing weight reduction

Damms-Machado, A., Louis, S., Schnitzer, A., Volynets, V., Rings, A., Basrai, M., & Bischoff, S. C. (2017) · The American Journal of Clinical Nutrition · doi:10.3945/ajcn.116.131110 · Cited by 195

Mortality Risk Score Prediction in an Elderly Population Using Machine Learning

Rose, S. (2013) · American Journal of Epidemiology · doi:10.1093/aje/kws241 · Cited by 193

Mobile phone-based unobtrusive ecological momentary assessment of day-to-day mood: an explorative study

Asselbergs, J., Ruwaard, J., Ejdys, M., Schrader, N., Sijbrandij, M., & Riper, H. (2016) · Journal of Medical Internet Research · doi:10.2196/jmir.5505 · Cited by 192

Transmission of West Nile and five other temperate mosquito-borne viruses peaks at temperatures between 23°C and 26°C

Shocket, M. S., Verwillow, A. B., Numazu, M. G., Slamani, H., Cohen, J. M., El Moustaid, F., Rohr, J., Johnson, L. R., & Mordecai, E. A. (2020) · eLife · doi:10.7554/elife.58511 · Cited by 192

BMI as a Modifiable Risk Factor for Type 2 Diabetes: Refining and Understanding Causal Estimates Using Mendelian Randomization

Corbin, L. J., Richmond, R. C., Wade, K. H., Burgess, S., Bowden, J., Smith, G. D., & Timpson, N. J. (2016) · Diabetes · doi:10.2337/db16-0418 · Cited by 184

Ecological time lags and the journey towards conservation success

Watts, K., Whytock, R. C., Park, K. J., Fuentes-Montemayor, E., Macgregor, N. A., Duffield, S., & McGowan, P. J. K. (2020) · Nature Ecology & Evolution · doi:10.1038/s41559-019-1087-8 · Cited by 185

CSF biomarkers cutoffs: The importance of coincident neuropathological diseases

Toledo, J. B., Brettschneider, J., Grossman, M., Arnold, S. E., Hu, W. T., Xie, S. X., Lee, V. M.-Y., Shaw, L. M., & Trojanowski, J. Q. (2012) · Acta Neuropathologica · doi:10.1007/s00401-012-0983-7 · Cited by 182

White matter diffusion alterations precede symptom onset in autosomal dominant Alzheimer’s disease

Araque Caballero, M. Á., Suárez-Calvet, M., Duering, M., Franzmeier, N., Benzinger, T., Fagan, A. M., Bateman, R. J., Jack, C. R., Levin, J., Dichgans, M., et al. (2018) · Brain · doi:10.1093/brain/awy229 · Cited by 184

Iterative stepwise regression imputation using standard and robust methods

Templ, M., Kowarik, A., & Filzmoser, P. (2011) · Computational Statistics & Data Analysis · doi:10.1016/j.csda.2011.04.012 · Cited by 169

Vitamin K Treatment Reduces Undercarboxylated Osteocalcin but Does Not Alter Bone Turnover, Density, or Geometry in Healthy Postmenopausal North American Women

Binkley, N., Harke, J., Krueger, D., Engelke, J., Vallarta-Ast, N., Gemar, D., Checovich, M., Chappell, R., & Suttie, J. (2009) · Journal of Bone and Mineral Research · doi:10.1359/jbmr.081254 · Cited by 163

The nonlinear development of emotion differentiation: granular emotional experience is low in adolescence

Nook, E. C., Sasse, S. F., Lambert, H. K., McLaughlin, K. A., & Somerville, L. H. (2018) · Psychological Science · doi:10.1177/0956797618773357 · Cited by 160

Intestinal and lung parasites in owned dogs and cats from central Italy

Riggio, F., Mannella, R., Ariti, G., & Perrucci, S. (2013) · Veterinary Parasitology · doi:10.1016/j.vetpar.2012.11.026 · Cited by 162

Duodenal microbiota composition and mucosal homeostasis in pediatric celiac disease

Cheng, J., Kalliomäki, M., Heilig, H. G., Palva, A., Lähteenoja, H., de Vos, W. M., Salojärvi, J., & Satokari, R. (2013) · BMC Gastroenterology · doi:10.1186/1471-230x-13-113 · Cited by 152

The economic and cognitive costs of annoying display advertisements

Goldstein, D. G., Suri, S., McAfee, R. P., Ekstrand-Abueg, M., & Diaz, F. (2014) · Journal of Marketing Research · doi:10.1509/jmr.13.0439 · Cited by 151

A combined risk score enhances prediction of type 1 diabetes among susceptible children

Ferrat, L. A., Vehik, K., Sharp, S. A., Lernmark, Å., Rewers, M. J., She, J.-X., Ziegler, A.-G., Toppari, J., Akolkar, B., Krischer, J. P., et al. (2020) · Nature Medicine · doi:10.1038/s41591-020-0930-4 · Cited by 148

Open digital mapping as a cost-effective method for mapping peat thickness and assessing the carbon stock of tropical peatlands

Rudiyanto, Minasny, B., Setiawan, B. I., Saptomo, S. K., & McBratney, A. B. (2018) · Geoderma · doi:10.1016/j.geoderma.2017.10.018 · Cited by 143

Transcriptional consequences of 16p11.2 deletion and duplication in mouse cortex and multiplex autism families

Blumenthal, I., Ragavendran, A., Erdin, S., Klei, L., Sugathan, A., Guide, J. R., Manavalan, P., Zhou, J. Q., Wheeler, V. C., Levin, J. Z., et al. (2014) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2014.05.004 · Cited by 145

Logistic regression has similar performance to optimised machine learning algorithms in a clinical setting: application to the discrimination between type 1 and type 2 diabetes in young adults

Lynam, A. L., Dennis, J. M., Owen, K. R., Oram, R. A., Jones, A. G., Shields, B. M., & Ferrat, L. A. (2020) · Diagnostic and Prognostic Research · doi:10.1186/s41512-020-00075-2 · Cited by 142

Behavioral change towards reduced intensity physical activity is disproportionately prevalent among adults with serious health issues or Self-Perception of high risk during the UK COVID-19 Lockdown

Rogers, N. T., Waterlow, N. R., Brindle, H., Enria, L., Eggo, R. M., Lees, S., & Roberts, C. H. (2020) · Frontiers in Public Health · doi:10.3389/fpubh.2020.575091 · Cited by 140

Mosaicism, Modules, and the Evolution of Birds: Results from a Bayesian Approach to the Study of Morphological Evolution Using Discrete Character Data

Clarke, J. A., & Middleton, K. M. (2008) · Systematic Biology · doi:10.1080/10635150802022231 · Cited by 137

A Social Capital Perspective on the Mentoring of Undergraduate Life Science Researchers: An Empirical Study of Undergraduate–Postgraduate–Faculty Triads

Aikens, M. L., Sadselia, S., Watkins, K., Evans, M., Eby, L. T., & Dolan, E. L. (2016) · CBE—Life Sciences Education · doi:10.1187/cbe.15-10-0208 · Cited by 139

Addressing age-related bias in sentiment analysis,

Diaz, M., Johnson, I., Lazar, A., Piper, A. M., & Gergle, D. (2018) · Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3173574.3173986 · Cited by 138

Guided Imagery And Progressive Muscle Relaxation as a Cluster of Symptoms Management Intervention in Patients Receiving Chemotherapy: A Randomized Control Trial

Charalambous, A., Giannakopoulou, M., Bozas, E., Marcou, Y., Kitsios, P., & Paikousis, L. (2016) · PLOS ONE · doi:10.1371/journal.pone.0156911 · Cited by 135

Anthropogenic stressors and riverine fish extinctions

Dias, M. S., Tedesco, P. A., Hugueny, B., Jézéquel, C., Beauchard, O., Brosse, S., & Oberdorff, T. (2017) · Ecological Indicators · doi:10.1016/j.ecolind.2017.03.053 · Cited by 131

Contrasting trends of butterfly species preferring semi-natural grasslands, field margins and forest edges in northern Europe

Kuussaari, M., Heliölä, J., Pöyry, J., & Saarinen, K. (2007) · Journal of Insect Conservation · doi:10.1007/s10841-006-9052-7 · Cited by 131

Estuarine influence on survival rates of coho (Oncorhynchus kisutch) and chinook salmon (Oncorhynchus tshawytscha) released from hatcheries on the U.S. Pacific coast

Magnusson, A., & Hilborn, R. (2003) · Estuaries · doi:10.1007/bf02803366 · Cited by 129

On the Use of General Control Samples for Genome-wide Association Studies: Genetic Matching Highlights Causal Variants

Luca, D., Ringquist, S., Klei, L., Lee, A. B., Gieger, C., Wichmann, H.-E., Schreiber, S., Krawczak, M., Lu, Y., Styche, A., et al. (2008) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2007.11.003 · Cited by 128

Signatures of Selection Reveal Candidate Genes Involved in Economic Traits and Cold Acclimation in Five Swedish Cattle Breeds

Ghoreishifar, S. M., Eriksson, S., Johansson, A. M., Khansefid, M., Moghaddaszadeh-Ahrabi, S., Parna, N., Davoudi, P., & Javanmard, A. (2020) · Genetics Selection Evolution · doi:10.1186/s12711-020-00571-5 · Cited by 126

Exposure to the Russian Internet Research Agency foreign influence campaign on Twitter in the 2016 US election and its relationship to attitudes and voting behavior

Eady, G., Paskhalis, T., Zilinsky, J., Bonneau, R., Nagler, J., & Tucker, J. A. (2023) · Nature Communications · doi:10.1038/s41467-022-35576-9 · Cited by 125

Association of aggressive behaviours with psychiatric disorders, age, sex and degree of intellectual disability: a large-scale survey

Tsiouris, J. A., Kim, S. Y., Brown, W. T., & Cohen, I. L. (2011) · Journal of Intellectual Disability Research · doi:10.1111/j.1365-2788.2011.01418.x · Cited by 124

A comparison of InVivoStat with other statistical software packages for analysis of data generated from animal experiments

Clark, R. A., Shoaib, M., Hewitt, K. N., Stanford, S. C., & Bate, S. T. (2012) · Journal of Psychopharmacology · doi:10.1177/0269881111420313 · Cited by 122

An approach for normalization and quality control for NanoString RNA expression data

Bhattacharya, A., Hamilton, A. M., Furberg, H., Pietzak, E., Purdue, M. P., Troester, M. A., Hoadley, K. A., & Love, M. I. (2021) · Briefings in Bioinformatics · doi:10.1093/bib/bbaa163 · Cited by 122

Functional coordination of alternative splicing in the mammalian central nervous system

Fagnani, M., Barash, Y., Ip, J. Y., Misquitta, C., Pan, Q., Saltzman, A. L., Shai, O., Lee, L., Rozenhek, A., Mohammad, N., et al. (2007) · Genome Biology · doi:10.1186/gb-2007-8-6-r108 · Cited by 120

Stochastic earned value analysis using Monte Carlo simulation and statistical learning techniques

Acebes, F., Pereda, M., Poza, D., Pajares, J., & Galán, J. M. (2015) · International Journal of Project Management · doi:10.1016/j.ijproman.2015.06.012 · Cited by 120

Large‐scale, long‐term trends in British river macroinvertebrates

Vaughan, I. P., & Ormerod, S. J. (2012) · Global Change Biology · doi:10.1111/j.1365-2486.2012.02662.x · Cited by 119

Classification of intended phoneme production from chronic intracortical microelectrode recordings in speech-motor cortex

Brumberg (2011) · Frontiers in Neuroscience · doi:10.3389/fnins.2011.00065 · Cited by 114

Aberrant activation of TCL1A promotes stem cell expansion in clonal haematopoiesis

Weinstock, J. S., Gopakumar, J., Burugula, B. B., Uddin, M. M., Jahn, N., Belk, J. A., Bouzid, H., Daniel, B., Miao, Z., Ly, N., et al. (2023) · Nature · doi:10.1038/s41586-023-05806-1 · Cited by 118

Trophic disruption: A meta-analysis of how habitat fragmentation affects resource consumption in terrestrial arthropod systems

Martinson, H. M., & Fagan, W. F. (2014) · Ecology Letters · doi:10.1111/ele.12305 · Cited by 117

Robust regression and posterior predictive simulation increase power to detect early bursts of trait evolution

Slater, G. J., & Pennell, M. W. (2014) · Systematic Biology · doi:10.1093/sysbio/syt066 · Cited by 117

Distributed neural representations of phonological features during speech perception

Arsenault, J. S., & Buchsbaum, B. R. (2015) · The Journal of Neuroscience · doi:10.1523/jneurosci.2454-14.2015 · Cited by 111

Functional diversity of leaf nitrogen concentrations drives grassland carbon fluxes

Milcu, A., Roscher, C., Gessler, A., Bachmann, D., Gockele, A., Guderle, M., Landais, D., Piel, C., Escape, C., Devidal, S., et al. (2014) · Ecology Letters · doi:10.1111/ele.12243 · Cited by 110

Randomized controlled trial of a web-delivered personalized normative feedback intervention to reduce alcohol-related risky sexual behavior among college students

Lewis, M. A., Patrick, M. E., Litt, D. M., Atkins, D. C., Kim, T., Blayney, J. A., Norris, J., George, W. H., & Larimer, M. E. (2014) · Journal of Consulting and Clinical Psychology · doi:10.1037/a0035550 · Cited by 108

Making friends and communicating on Facebook: Implications for the access to social capital

Bohn, A., Buchta, C., Hornik, K., & Mair, P. (2014) · Social Networks · doi:10.1016/j.socnet.2013.11.003 · Cited by 106

Global patterns of fisheries conflict: Forty years of data

Spijkers, J., Singh, G., Blasiak, R., Morrison, T. H., Le Billon, P., & Österblom, H. (2019) · Global Environmental Change · doi:10.1016/j.gloenvcha.2019.05.005 · Cited by 105

Meta-analysis of genome-wide DNA methylation identifies shared associations across neurodegenerative disorders

study, T. A. I. B. A. L., Nabais, M. F., Initiative, T. A. D. N., Laws, S. M., Lin, T., Vallerga, C. L., Armstrong, N. J., Blair, I. P., Kwok, J. B., Mather, K. A., et al. (2021) · Genome Biology · doi:10.1186/s13059-021-02275-5 · Cited by 99

Improved dual AAV vectors with reduced expression of truncated proteins are safe and effective in the retina of a mouse model of Stargardt disease

Trapani, I., Toriello, E., de Simone, S., Colella, P., Iodice, C., Polishchuk, E. V., Sommella, A., Colecchi, L., Rossi, S., Simonelli, F., et al. (2015) · Human Molecular Genetics · doi:10.1093/hmg/ddv386 · Cited by 94

Using learning analytics to understand student perceptions of peer feedback

Misiejuk, K., Wasson, B., & Egelandsdal, K. (2021) · Computers in Human Behavior · doi:10.1016/j.chb.2020.106658 · Cited by 98

Climate Change and Lithium Mining Influence Flamingo Abundance in the Lithium Triangle

Gutiérrez, J. S., Moore, J. N., Donnelly, J. P., Dorador, C., Navedo, J. G., & Senner, N. R. (2022) · Proceedings of the Royal Society B: Biological Sciences · doi:10.1098/rspb.2021.2388 · Cited by 96

Short- and long-read metagenomics of urban and rural South African gut microbiomes reveal a transitional composition and undescribed taxa

Tamburini, F. B., Maghini, D., Oduaran, O. H., Brewster, R., Hulley, M. R., Sahibdeen, V., Norris, S. A., Tollman, S., Kahn, K., Wagner, R. G., et al. (2022) · Nature Communications · doi:10.1038/s41467-021-27917-x · Cited by 88

Thermal sensitivity of occupants in different building typologies: The Griffiths Constant is a Variable

Rupp, R. F., Kim, J., Ghisi, E., & de Dear, R. (2019) · Energy and Buildings · doi:10.1016/j.enbuild.2019.07.048 · Cited by 92

Improving quit rates of web‐delivered interventions for smoking cessation: full‐scale randomized trial of WebQuit.org versus Smokefree.gov

Bricker, J. B., Mull, K. E., McClure, J. B., Watson, N. L., & Heffner, J. L. (2018) · Addiction · doi:10.1111/add.14127 · Cited by 91

Fire severity and tree regeneration following bark beetle outbreaks: The role of outbreak stage and burning conditions

Harvey, B. J., Donato, D. C., Romme, W. H., & Turner, M. G. (2014) · Ecological Applications · doi:10.1890/13-1851.1 · Cited by 91

Cell cycle-resolved chromatin proteomics reveals the extent of mitotic preservation of the genomic regulatory landscape

Ginno, P. A., Burger, L., Seebacher, J., Iesmantavicius, V., & Schübeler, D. (2018) · Nature Communications · doi:10.1038/s41467-018-06007-5 · Cited by 91

Comparative influence of ocean conditions on yellowfin and Atlantic bluefin tuna catch from longlines in the Gulf of Mexico

Teo, S. L. H., & Block, B. A. (2010) · PLoS ONE · doi:10.1371/journal.pone.0010756 · Cited by 91

The Cumulative Mass Profile of the Milky Way as Determined by Globular Cluster Kinematics from Gaia DR2

Eadie, G., & Jurić, M. (2019) · The Astrophysical Journal · doi:10.3847/1538-4357/ab0f97 · Cited by 90

Correlates of naturalization and occupancy of introduced ornamentals in Germany

Hanspach, J., Kühn, I., Pyšek, P., Boos, E., & Klotz, S. (2008) · Perspectives in Plant Ecology, Evolution and Systematics · doi:10.1016/j.ppees.2008.05.001 · Cited by 88

Rationalizing Three-Dimensional Activity Landscapes and the Influence of Molecular Representations on Landscape Topology and the Formation of Activity Cliffs

Peltason, L., Iyer, P., & Bajorath, J. (2010) · Journal of Chemical Information and Modeling · doi:10.1021/ci100091e · Cited by 85

The fingerprints of misinformation: how deceptive content differs from reliable sources in terms of cognitive effort and appeal to emotions

Carrasco-Farré, C. (2022) · Humanities and Social Sciences Communications · doi:10.1057/s41599-022-01174-9 · Cited by 84

Respiratory responses during affective picture viewing

Gomez, P., Stahel, W. A., & Danuser, B. (2004) · Biological Psychology · doi:10.1016/j.biopsycho.2004.03.013 · Cited by 84

Physical Activity Recognition from Accelerometer Data Using a Multi-Scale Ensemble Method

Zheng, Y., Won, W.-K., Guan, X., & Trost, S. (2013) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v27i2.18997 · Cited by 84

Heterogeneity in Blood Pressure Response to 4 Antihypertensive Drugs: A Randomized Clinical Trial

Sundström, J., Lind, L., Nowrouzi, S., Hagström, E., Held, C., Lytsy, P., Neal, B., Marttala, K., & Östlund, O. (2023) · JAMA · doi:10.1001/jama.2023.3322 · Cited by 84

Rapid-Throughput Skeletal Phenotyping of 100 Knockout Mice Identifies 9 New Genes That Determine Bone Strength

Bassett, J. H. D., Gogakos, A., White, J. K., Evans, H., Jacques, R. M., van der Spek, A. H., Sanger Mouse Genetics Project, Ramirez-Solis, R., Ryder, E., Sunter, D., et al. (2012) · PLoS Genetics · doi:10.1371/journal.pgen.1002858 · Cited by 82

Age, gender, personality, ideological attitudes and individual differences in a person's news spectrum: how many and who might be prone to “filter bubbles” and “echo chambers” online?

Sindermann, C., Elhai, J. D., Moshagen, M., & Montag, C. (2020) · Heliyon · doi:10.1016/j.heliyon.2020.e03214 · Cited by 78

Selection and gene flow on a diminishing cline of melanic peppered moths

Saccheri, I. J., Rousset, F., Watts, P. C., Brakefield, P. M., & Cook, L. M. (2008) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0803785105 · Cited by 76

Patterns of Geographic Expansion of Aedes aegypti in the Peruvian Amazon

Guagliardo, S. A., Barboza, J. L., Morrison, A. C., Astete, H., Vazquez-Prokopec, G., & Kitron, U. (2014) · PLoS Neglected Tropical Diseases · doi:10.1371/journal.pntd.0003033 · Cited by 77

Molecular basis of proton sensing by G protein-coupled receptors

Howard, M. K., Hoppe, N., Huang, X.-P., Mitrovic, D., Billesbølle, C. B., Macdonald, C. B., Mehrotra, E., Rockefeller Grimes, P., Trinidad, D. D., Delemotte, L., et al. (2025) · Cell · doi:10.1016/j.cell.2024.11.036 · Cited by 77

Hybrid multiple-site mass closure and source apportionment of PM2.5 and aerosol acidity at major cities in the Po Valley

Masiol, M., Squizzato, S., Formenton, G., Khan, M. B., Hopke, P. K., Nenes, A., Pandis, S. N., Tositti, L., Benetello, F., Visin, F., et al. (2020) · Science of The Total Environment · doi:10.1016/j.scitotenv.2019.135287 · Cited by 77

Predictors of utilization of a novel smoking cessation smartphone app

Zeng, E. Y., Vilardaga, R., Heffner, J. L., Mull, K. E., & Bricker, J. B. (2015) · Telemedicine and e-Health · doi:10.1089/tmj.2014.0232 · Cited by 71

Classification of Major Construction Materials in Construction Environments Using Ensemble Classifiers

Son, H., Kim, C., Hwang, N., Kim, C., & Kang, Y. (2014) · Advanced Engineering Informatics · doi:10.1016/j.aei.2013.10.001 · Cited by 74

MicroRNA expression distinguishes SCLC from NSCLC lung tumor cells and suggests a possible pathological relationship between SCLCs and NSCLCs

Du, L., Schageman, J. J., Irnov, Girard, L., Hammond, S. M., Minna, J. D., Gazdar, A. F., & Pertsemlidis, A. (2010) · Journal of Experimental & Clinical Cancer Research · doi:10.1186/1756-9966-29-75 · Cited by 73

Current role of social benefits in ecosystem service assessments

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White-Sand Savannas Expand at the Core of the Amazon After Forest Wildfires

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