ROCR: visualizing classifier performance in R

GSVA: gene set variation analysis for microarray and RNA-Seq data

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

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The precision-recall plot is more informative than the ROC plot when evaluating binary classifiers on imbalanced datasets

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SoilGrids250m: Global gridded soil information based on machine learning

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DoubletFinder: Doublet Detection in Single-Cell RNA Sequencing Data Using Artificial Nearest Neighbors

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REVEL: An Ensemble Method for Predicting the Pathogenicity of Rare Missense Variants

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Multivariable association discovery in population-scale meta-omics studies

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Extinction risk and conservation of the world’s sharks and rays

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Single-cell chromatin state analysis with Signac

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A data-driven approach to preprocessing Illumina 450K methylation array data

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Predicting non-small cell lung cancer prognosis by fully automated microscopic pathology image features

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GUIDANCE2: accurate detection of unreliable alignment regions accounting for the uncertainty of multiple parameters

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SWAN: Subset-quantile Within Array Normalization for Illumina Infinium HumanMethylation450 BeadChips

Maksimovic, J., Gordon, L., & Oshlack, A. (2012) · Genome Biology · doi:10.1186/gb-2012-13-6-r44 · Cited by 826

Biological interpretation of genome-wide association studies using predicted gene functions

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Polymorphisms Associated with Cholesterol and Risk of Cardiovascular Events

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Untitled work

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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

Temporal Response of the Human Virome to Immunosuppression and Antiviral Therapy

De Vlaminck, I., Khush, K. K., Strehl, C., Kohli, B., Luikart, H., Neff, N. F., Okamoto, J., Snyder, T. M., Cornfield, D. N., Nicolls, M. R., et al. (2013) · Cell · doi:10.1016/j.cell.2013.10.034 · Cited by 471

Diagnosis-Independent Alzheimer Disease Biomarker Signature in Cognitively Normal Elderly People

De Meyer, G. (2010) · Archives of Neurology · doi:10.1001/archneurol.2010.179 · Cited by 462

PresenceAbsence : An R Package for Presence Absence Analysis

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YAP1 Exerts Its Transcriptional Control via TEAD-Mediated Activation of Enhancers

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A Pilot Study of Circulating miRNAs as Potential Biomarkers of Early Stage Breast Cancer

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Mortality prediction in intensive care units with the Super ICU Learner Algorithm (SICULA): a population-based study.

Pirracchio, R., Petersen, M. L., Carone, M., Rigon, M. R., Chevret, S., & van der Laan, M. J. (2015) · The Lancet Respiratory Medicine · doi:10.1016/s2213-2600(14)70239-5 · Cited by 374

Classification Criteria for Distinguishing Cortisol Responders From Nonresponders to Psychosocial Stress: Evaluation of Salivary Cortisol Pulse Detection in Panel Designs

Miller, R., Plessow, F., Kirschbaum, C., & Stalder, T. (2013) · Psychosomatic Medicine · doi:10.1097/psy.0000000000000002 · Cited by 376

In vivo high-throughput profiling of CRISPR-Cpf1 activity

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The genetic interpretation of area under the ROC curve in genomic profiling

Wray, N. R., Yang, J., Goddard, M. E., & Visscher, P. M. (2010) · PLoS Genetics · doi:10.1371/journal.pgen.1000864 · Cited by 334

Human pluripotent stem cell-derived neural constructs for predicting neural toxicity

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AF2Complex predicts direct physical interactions in multimeric proteins with deep learning

Gao, M., Nakajima An, D., Parks, J. M., & Skolnick, J. (2022) · Nature Communications · doi:10.1038/s41467-022-29394-2 · Cited by 252

Validation of a Machine Learning Model That Outperforms Clinical Risk Scoring Systems for Upper Gastrointestinal Bleeding

Shung, D. L., Au, B., Taylor, R. A., Tay, J. K., Laursen, S. B., Stanley, A. J., Dalton, H. R., Ngu, J., Schultz, M., & Laine, L. (2020) · Gastroenterology · doi:10.1053/j.gastro.2019.09.009 · Cited by 227

Prediction of missense mutation functionality depends on both the algorithm and sequence alignment employed

Hicks, S., Wheeler, D. A., Plon, S. E., & Kimmel, M. (2011) · Human Mutation · doi:10.1002/humu.21490 · Cited by 223

Ultraconserved Enhancers Are Required for Normal Development

Dickel, D. E., Ypsilanti, A. R., Pla, R., Zhu, Y., Barozzi, I., Mannion, B. J., Khin, Y. S., Fukuda-Yuzawa, Y., Plajzer-Frick, I., Pickle, C. S., et al. (2018) · Cell · doi:10.1016/j.cell.2017.12.017 · Cited by 190

Immune gene signatures for predicting durable clinical benefit of anti-PD-1 immunotherapy in patients with non-small cell lung cancer

Hwang, S., Kwon, A.-Y., Jeong, J.-Y., Kim, S., Kang, H., Park, J., Kim, J.-H., Han, O. J., Lim, S. M., & An, H. J. (2020) · Scientific Reports · doi:10.1038/s41598-019-57218-9 · Cited by 215

Identification of a three-biomarker panel in urine for early detection of pancreatic adenocarcinoma

Radon, T. P., Massat, N. J., Jones, R., Alrawashdeh, W., Dumartin, L., Ennis, D., Duffy, S. W., Kocher, H. M., Pereira, S. P., Guarner (posthumous), L., et al. (2015) · Clinical Cancer Research · doi:10.1158/1078-0432.ccr-14-2467 · Cited by 203

Continuous Automated Model EvaluatiOn (CAMEO) complementing the critical assessment of structure prediction in CASP12

Haas, J., Barbato, A., Behringer, D., Studer, G., Roth, S., Bertoni, M., Mostaguir, K., Gumienny, R., & Schwede, T. (2018) · Proteins: Structure, Function, and Bioinformatics · doi:10.1002/prot.25431 · Cited by 168

Comparison of the Predictive Performance and Interpretability of Random Forest and Linear Models on Benchmark Data Sets

Marchese Robinson, R. L., Palczewska, A., Palczewski, J., & Kidley, N. (2017) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.6b00753 · Cited by 167

Clinical factors predicting treatment resistant depression: affirmative results from the European multicenter study

Kautzky, A., Dold, M., Bartova, L., Spies, M., Kranz, G. S., Souery, D., Montgomery, S., Mendlewicz, J., Zohar, J., Fabbri, C., et al. (2019) · Acta Psychiatrica Scandinavica · doi:10.1111/acps.12959 · Cited by 148

ROC curves for clinical prediction models part 1. ROC plots showed no added value above the AUC when evaluating the performance of clinical prediction models

Verbakel, J. Y., Steyerberg, E. W., Uno, H., De Cock, B., Wynants, L., Collins, G. S., & Van Calster, B. (2020) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2020.01.028 · Cited by 138

Sex estimation using sexually dimorphic amelogenin protein fragments in human enamel

Parker, G. J., Yip, J. M., Eerkens, J. W., Salemi, M., Durbin-Johnson, B., Kiesow, C., Haas, R., Buikstra, J. E., Klaus, H., Regan, L. A., et al. (2019) · Journal of Archaeological Science · doi:10.1016/j.jas.2018.08.011 · Cited by 138

Serum neurofilament light chain levels are related to small vessel disease burden

Duering, M., Konieczny, M. J., Tiedt, S., Baykara, E., Tuladhar, A. M., Leijsen, E. V., Lyrer, P., Engelter, S. T., Gesierich, B., Achmüller, M., et al. (2018) · Journal of Stroke · doi:10.5853/jos.2017.02565 · Cited by 132

Caveats and pitfalls of ROC analysis in clinical microarray research (and how to avoid them)

Berrar, D., & Flach, P. (2012) · Briefings in Bioinformatics · doi:10.1093/bib/bbr008 · Cited by 136

RNABindRPlus: A Predictor that Combines Machine Learning and Sequence Homology-Based Methods to Improve the Reliability of Predicted RNA-Binding Residues in Proteins

Walia, R. R., Xue, L. C., Wilkins, K., El-Manzalawy, Y., Dobbs, D., & Honavar, V. (2014) · PLoS ONE · doi:10.1371/journal.pone.0097725 · Cited by 132

Microbiome responses to an uncontrolled short-term diet intervention in the frame of the Citizen Science Project

Klimenko, N. S., Tyakht, A. V., Popenko, A. S., Vasiliev, A. S., Altukhov, I. A., Ischenko, D. S., Shashkova, T. I., Efimova, D. A., Nikogosov, D. A., Osipenko, D. A., et al. (2018) · Nutrients · doi:10.3390/nu10050576 · Cited by 123

Detection of selective sweeps in structured populations: a comparison of recent methods

Vatsiou, A. I., Bazin, E., & Gaggiotti, O. E. (2016) · Molecular Ecology · doi:10.1111/mec.13360 · Cited by 111

In vivo Analysis of Hippocampal Subfield Atrophy in Mild Cognitive Impairment via Semi-Automatic Segmentation of T2-Weighted MRI

Pluta, J., Yushkevich, P., Das, S., & Wolk, D. (2012) · Journal of Alzheimer's Disease · doi:10.3233/jad-2012-111931 · Cited by 107

Generally applicable transcriptome-wide analysis of translation using anota2seq

Oertlin, C., Lorent, J., Murie, C., Furic, L., Topisirovic, I., & Larsson, O. (2019) · Nucleic Acids Research · doi:10.1093/nar/gkz223 · Cited by 107

LOGICOIL—multi-state prediction of coiled-coil oligomeric state

Vincent, T. L., Green, P. J., & Woolfson, D. N. (2013) · Bioinformatics · doi:10.1093/bioinformatics/bts648 · Cited by 103

On Sharing Quantitative Trait GWAS Results in an Era of Multiple-omics Data and the Limits of Genomic Privacy

Im, H. K., Gamazon, E. R., Nicolae, D. L., & Cox, N. J. (2012) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2012.02.008 · Cited by 103

The interaction between cytosine methylation and processes of DNA replication and repair shape the mutational landscape of cancer genomes

Poulos, R. C., Olivier, J., & Wong, J. W. H. (2017) · Nucleic Acids Research · doi:10.1093/nar/gkx463 · Cited by 93

RNA‐seq Data: Challenges in and Recommendations for Experimental Design and Analysis

Williams, A. G., Thomas, S., Wyman, S. K., & Holloway, A. K. (2014) · Current Protocols in Human Genetics · doi:10.1002/0471142905.hg1113s83 · Cited by 93

HiNT: a computational method for detecting copy number variations and translocations from Hi-C data

Wang, S., Lee, S., Chu, C., Jain, D., Kerpedjiev, P., Nelson, G. M., Walsh, J. M., Alver, B. H., & Park, P. J. (2020) · Genome Biology · doi:10.1186/s13059-020-01986-5 · Cited by 99

Statistical Shape Modeling of the Left Ventricle: Myocardial Infarct Classification Challenge

Suinesiaputra, A., Dhooge, J., Duchateau, N., Ehrhardt, J., Frangi, A. F., Gooya, A., Grau, V., Lekadir, K., Lu, A., Mukhopadhyay, A., et al. (2018) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2017.2652449 · Cited by 95

Combined evaluation of bedside clinical variables and the electrocardiogram for the differential diagnosis of paroxysmal atrioventricular reciprocating tachycardias in patients without pre-excitation

González-Torrecilla, E., Almendral, J., Arenal, A., Atienza, F., Atea, L. F., del Castillo, S., & Fernández-Avilés, F. (2009) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2009.02.059 · Cited by 89

The relative impacts of vegetation, topography and spatial arrangement on building loss to wildfires in case studies of California and Colorado

Alexandre, P. M., Stewart, S. I., Mockrin, M. H., Keuler, N. S., Syphard, A. D., Bar-Massada, A., Clayton, M. K., & Radeloff, V. C. (2016) · Landscape Ecology · doi:10.1007/s10980-015-0257-6 · Cited by 83

Use of morning serum cortisol level after transsphenoidal resection of pituitary adenoma to predict the need for long-term glucocorticoid supplementation: Clinical article

Marko, N. F., Gonugunta, V. A., Hamrahian, A. H., Usmani, A., Mayberg, M. R., & Weil, R. J. (2009) · Journal of Neurosurgery · doi:10.3171/2008.12.jns081265 · Cited by 67

The effect of ligand-based tautomer and protomer prediction on structure-based virtual screening.

Kalliokoski, T., Salo, H. S., Lahtela-Kakkonen, M., & Poso, A. (2009) · Journal of Chemical Information and Modeling · doi:10.1021/ci900364w · Cited by 66

ThalPred: a web-based prediction tool for discriminating thalassemia trait and iron deficiency anemia

Laengsri, V., Shoombuatong, W., Adirojananon, W., Nantasenamat, C., Prachayasittikul, V., & Nuchnoi, P. (2019) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-019-0929-2 · Cited by 66

The role of defensible space on the likelihood of house impact from wildfires in forested landscapes of south eastern Australia

Penman, S. H., Price, O. F., Penman, T. D., & Bradstock, R. A. (2019) · International Journal of Wildland Fire · doi:10.1071/wf18046 · Cited by 66

Society of Toxicologic Pathology Digital Pathology and Image Analysis Special Interest Group Article*: Opinion on the Application of Artificial Intelligence and Machine Learning to Digital Toxicologic Pathology

Turner, O. C., Aeffner, F., Bangari, D. S., High, W., Knight, B., Forest, T., Cossic, B., Himmel, L. E., Rudmann, D. G., Bawa, B., et al. (2020) · Toxicologic Pathology · doi:10.1177/0192623319881401 · Cited by 64

C-Reactive Protein: A Differential Biomarker For Major Depressive Disorder and Bipolar II Disorder

Chang, H. H., Wang, T.-Y., Lee, I. H., Lee, S.-Y., Chen, K. C., Huang, S.-Y., Yang, Y. K., Lu, R.-B., & Chen, P. S. (2017) · The World Journal of Biological Psychiatry · doi:10.3109/15622975.2016.1155746 · Cited by 61

Playing 20 Questions with the Mind: Collaborative Problem Solving by Humans Using a Brain-to-Brain Interface

Stocco, A., Prat, C. S., Losey, D. M., Cronin, J. A., Wu, J., Abernethy, J. A., & Rao, R. P. N. (2015) · PLOS ONE · doi:10.1371/journal.pone.0137303 · Cited by 57

In‐depth evaluation of machine learning methods for semi‐automating article screening in a systematic review of mechanistic literature

Kebede, M. M., Le Cornet, C., & Fortner, R. T. (2023) · Research Synthesis Methods · doi:10.1002/jrsm.1589 · Cited by 50

Liebowitz Social Anxiety Scale (LSAS): Optimal cut points for remission and response in a German sample

von Glischinski, M., Willutzki, U., Stangier, U., Hiller, W., Hoyer, J., Leibing, E., Leichsenring, F., & Hirschfeld, G. (2018) · Clinical Psychology & Psychotherapy · doi:10.1002/cpp.2179 · Cited by 54

Cross-Comparison of Human iPSC Motor Neuron Models of Familial and Sporadic ALS Reveals Early and Convergent Transcriptomic Disease Signatures

Ho, R., Workman, M. J., Mathkar, P., Wu, K., Kim, K. J., O’Rourke, J. G., Kellogg, M., Montel, V., Banuelos, M. G., Arogundade, O. A., et al. (2021) · Cell Systems · doi:10.1016/j.cels.2020.10.010 · Cited by 62

SCORER 2.0: an algorithm for distinguishing parallel dimeric and trimeric coiled-coil sequences

Armstrong, C. T., Vincent, T. L., Green, P. J., & Woolfson, D. N. (2011) · Bioinformatics · doi:10.1093/bioinformatics/btr299 · Cited by 49

Uncoupling evolutionary changes in DNA sequence, transcription factor occupancy and enhancer activity

Khoueiry, P., Girardot, C., Ciglar, L., Peng, P.-C., Gustafson, E. H., Sinha, S., & Furlong, E. E. (2017) · eLife · doi:10.7554/elife.28440 · Cited by 44

Personalized medicine: genetic variation and loss of physiologic complexity are associated with mortality in 644 trauma patients

Norris, P. R., Canter, J. A., Jenkins, J. M., Moore, J. H., Williams, A. E., & Morris, J. A. (2009) · Annals of Surgery · doi:10.1097/sla.0b013e3181b8fb1f · Cited by 40

Conceptualizing vacation dedication

Ring, A., Yanamandram, V., Gretzel, U., & Dolnicar, S. (2020) · Current Issues in Tourism · doi:10.1080/13683500.2019.1678572 · Cited by 34

Applying fire connectivity and centrality measures to mitigate the cheatgrass-fire cycle in the arid West, USA

Gray, M. E., & Dickson, B. G. (2016) · Landscape Ecology · doi:10.1007/s10980-016-0353-2 · Cited by 31

How difficult is inference of mammalian causal gene regulatory networks?

Djordjevic, D., Yang, A., Zadoorian, A., Rungrugeecharoen, K., & Ho, J. W. K. (2014) · PLoS ONE · doi:10.1371/journal.pone.0111661 · Cited by 29

Multiscale consensus habitat modeling for landscape level conservation prioritization

Poor, E. E., Scheick, B. K., & Mullinax, J. M. (2020) · Scientific Reports · doi:10.1038/s41598-020-74716-3 · Cited by 27

Navigating a 2D Virtual World Using Direct Brain Stimulation

Losey, D. M., Stocco, A., Abernethy, J. A., & Rao, R. P. N. (2016) · Frontiers in Robotics and AI · doi:10.3389/frobt.2016.00072 · Cited by 19

A Novel Type of Blood Biomarker: Distinct Changes of Cytokine-Induced STAT Phosphorylation in Blood T Cells Between Colorectal Cancer Patients and Healthy Individuals

Yun, J. W., Lee, S., Kim, H. M., Chun, S., Engleman, E. G., Kim, H. C., & Kang, E.-S. (2019) · Cancers · doi:10.3390/cancers11081157 · Cited by 18

Somatic cancer driver mutations are enriched and associated with inflammatory states in Alzheimer’s disease microglia

Huang, A. Y., Zhou, Z., Talukdar, M., Miller, M. B., Chhouk, B., Enyenihi, L., Rosen, I., Stronge, E., Zhao, B., Kim, D., et al. (2024) · doi:10.1101/2024.01.03.574078 · Cited by 16

Somatic cancer variants enriched in Alzheimer’s disease microglia-like cells drive inflammatory and proliferative states

Huang, A. Y., Zhou, Z., Talukdar, M., Enyenihi, L., Miller, M. B., Chhouk, B., Rosen, I., Zheng, M., Zhou, M., Yang, A., et al. (2026) · Cell · doi:10.1016/j.cell.2026.03.040 · Cited by 15

Fine-scale habitat characteristics influence the winter habitat use of swamp deer (Rucervus duvaucelii) in Shuklaphanta National Park, Nepal

Dhami, B., Bhusal, A., Neupane, B., Kc, N., Lamichhane, S., Bhattarai, D., & Shrestha, B. (2023) · Global Ecology and Conservation · doi:10.1016/j.gecco.2023.e02648 · Cited by 11

Machine learning classification of new firearm injury encounters in the St Louis region: 2010-2020

Ancona, R. M., Cooper, B. P., Foraker, R., Kaser, T., Adeoye, O., & Mueller, K. L. (2024) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocae173 · Cited by 7

Evaluation of serum diagnosis of pancreatic cancer by using surface-enhanced laser desorption/ionization time-of-flight mass spectrometry

GAO, H., ZHENG, Z., YUE, Z., LIU, F., ZHOU, L., & ZHAO, X. (2012) · International Journal of Molecular Medicine · doi:10.3892/ijmm.2012.1113 · Cited by 7