The NumPy Array: A Structure for Efficient Numerical Computation

SciPy 1.0: fundamental algorithms for scientific computing in Python

Virtanen, P., Gommers, R., Oliphant, T. E., Haberland, M., Reddy, T., Cournapeau, D., Burovski, E., Peterson, P., Weckesser, W., Bright, J., et al. (2020) · Nature Methods · doi:10.1038/s41592-019-0686-2 · Cited by 38,430

Array programming with NumPy

Harris, C. R., Millman, K. J., van der Walt, S. J., Gommers, R., Virtanen, P., Cournapeau, D., Wieser, E., Taylor, J., Berg, S., Smith, N. J., et al. (2020) · Nature · doi:10.1038/s41586-020-2649-2 · Cited by 23,826

HTSeq—a Python framework to work with high-throughput sequencing data

Anders, S., Pyl, P. T., & Huber, W. (2015) · Bioinformatics · doi:10.1093/bioinformatics/btu638 · Cited by 21,464

Astropy: A community Python package for astronomy

The Astropy Collaboration, Robitaille, T. P., Tollerud, E. J., Greenfield, P., Droettboom, M., Bray, E., Aldcroft, T., Davis, M., Ginsburg, A., Price-Whelan, A. M., et al. (2013) · Astronomy & Astrophysics · doi:10.1051/0004-6361/201322068 · Cited by 14,620

SCANPY: large-scale single-cell gene expression data analysis

Wolf, F. A., Angerer, P., & Theis, F. J. (2018) · Genome Biology · doi:10.1186/s13059-017-1382-0 · Cited by 8,881

UCSF ChimeraX: Meeting modern challenges in visualization and analysis

Goddard, T. D., Huang, C. C., Meng, E. C., Pettersen, E. F., Couch, G. S., Morris, J. H., & Ferrin, T. E. (2018) · Protein Science · doi:10.1002/pro.3235 · Cited by 5,267

NanoPack: visualizing and processing long-read sequencing data

De Coster, W., D’Hert, S., Schultz, D. T., Cruts, M., & Van Broeckhoven, C. (2018) · Bioinformatics · doi:10.1093/bioinformatics/bty149 · Cited by 3,168

Albumentations: fast and flexible image augmentations

Buslaev, A., Iglovikov, V. I., Khvedchenya, E., Parinov, A., Druzhinin, M., & Kalinin, A. A. (2020) · Information · doi:10.3390/info11020125 · Cited by 2,714

Probabilistic programming in Python using PyMC3

Salvatier, J., Wiecki, T. V., & Fonnesbeck, C. (2016) · PeerJ Computer Science · doi:10.7717/peerj-cs.55 · Cited by 2,544

Cellpose 2.0: how to train your own model

Pachitariu, M., & Stringer, C. (2022) · Nature Methods · doi:10.1038/s41592-022-01663-4 · Cited by 1,381

Dipy, a library for the analysis of diffusion MRI data

Garyfallidis, E., Brett, M., Amirbekian, B., Rokem, A., van der Walt, S., Descoteaux, M., Nimmo-Smith, I., & Contributors, D. (2014) · Frontiers in Neuroinformatics · doi:10.3389/fninf.2014.00008 · Cited by 1,371

Deep learning for cellular image analysis

Moen, E., Bannon, D., Kudo, T., Graf, W., Covert, M., & Van Valen, D. (2019) · Nature Methods · doi:10.1038/s41592-019-0403-1 · Cited by 1,225

Proteomics of SARS-CoV-2-infected host cells reveals therapy targets

Bojkova, D., Klann, K., Koch, B., Widera, M., Krause, D., Ciesek, S., Cinatl, J., & Münch, C. (2020) · Nature · doi:10.1038/s41586-020-2332-7 · Cited by 1,010

Mutation effects predicted from sequence co-variation

Hopf, T. A., Ingraham, J. B., Poelwijk, F. J., Schärfe, C. P. I., Springer, M., Sander, C., & Marks, D. S. (2017) · Nature Biotechnology · doi:10.1038/nbt.3769 · Cited by 775

In Silico Labeling: Predicting Fluorescent Labels in Unlabeled Images

Christiansen, E. M., Yang, S. J., Ando, D. M., Javaherian, A., Skibinski, G., Lipnick, S., Mount, E., O’Neil, A., Shah, K., Lee, A. K., et al. (2018) · Cell · doi:10.1016/j.cell.2018.03.040 · Cited by 635

Cell Types of the Human Retina and Its Organoids at Single-Cell Resolution

Cowan, C. S., Renner, M., De Gennaro, M., Gross-Scherf, B., Goldblum, D., Hou, Y., Munz, M., Rodrigues, T. M., Krol, J., Szikra, T., et al. (2020) · Cell · doi:10.1016/j.cell.2020.08.013 · Cited by 654

Deep Learning Automates the Quantitative Analysis of Individual Cells in Live-Cell Imaging Experiments

Van Valen, D. A., Kudo, T., Lane, K. M., Macklin, D. N., Quach, N. T., DeFelice, M. M., Maayan, I., Tanouchi, Y., Ashley, E. A., & Covert, M. W. (2016) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1005177 · Cited by 557

Machine learning molecular dynamics for the simulation of infrared spectra

Gastegger, M., Behler, J., & Marquetand, P. (2017) · Chemical Science · doi:10.1039/c7sc02267k · Cited by 556

neuromaps: structural and functional interpretation of brain maps

Markello, R. D., Hansen, J. Y., Liu, Z.-Q., Bazinet, V., Shafiei, G., Suárez, L. E., Blostein, N., Seidlitz, J., Baillet, S., Satterthwaite, T. D., et al. (2022) · Nature Methods · doi:10.1038/s41592-022-01625-w · Cited by 479

Nanopore long-read RNAseq reveals widespread transcriptional variation among the surface receptors of individual B cells

Byrne, A., Beaudin, A. E., Olsen, H. E., Jain, M., Cole, C., Palmer, T., DuBois, R. M., Forsberg, E. C., Akeson, M., & Vollmers, C. (2017) · Nature Communications · doi:10.1038/ncomms16027 · Cited by 379

Different Neuronal Activity Patterns Induce Different Gene Expression Programs

Tyssowski, K. M., DeStefino, N. R., Cho, J.-H., Dunn, C. J., Poston, R. G., Carty, C. E., Jones, R. D., Chang, S. M., Romeo, P., Wurzelmann, M. K., et al. (2018) · Neuron · doi:10.1016/j.neuron.2018.04.001 · Cited by 390

Chaperone-mediated autophagy prevents collapse of the neuronal metastable proteome

Bourdenx, M., Martín-Segura, A., Scrivo, A., Rodriguez-Navarro, J. A., Kaushik, S., Tasset, I., Diaz, A., Storm, N. J., Xin, Q., Juste, Y. R., et al. (2021) · Cell · doi:10.1016/j.cell.2021.03.048 · Cited by 337

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

TopologyNet: Topology based deep convolutional and multi-task neural networks for biomolecular property predictions

Cang, Z., & Wei, G.-W. (2017) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1005690 · Cited by 330

Cellular and Molecular Probing of Intact Human Organs

Zhao, S., Todorov, M. I., Cai, R., -Maskari, R. A., Steinke, H., Kemter, E., Mai, H., Rong, Z., Warmer, M., Stanic, K., et al. (2020) · Cell · doi:10.1016/j.cell.2020.01.030 · Cited by 292

An Unsupervised-Learning-Based Approach for Automated Defect Inspection on Textured Surfaces

Mei, S., Yang, H., & Yin, Z. (2018) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2018.2795178 · Cited by 308

The origin of accreted stellar halo populations in the Milky Way using APOGEE, Gaia , and the EAGLE simulations

Mackereth, J. T., Schiavon, R. P., Pfeffer, J., Hayes, C. R., Bovy, J., Anguiano, B., Prieto, C. A., Hasselquist, S., Holtzman, J., Johnson, J. A., et al. (2019) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/sty2955 · Cited by 285

Visualizing the Hidden Activity of Artificial Neural Networks

Rauber, P. E., Fadel, S. G., Falcao, A. X., & Telea, A. C. (2017) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2016.2598838 · Cited by 280

The Jellyfish Cassiopea Exhibits a Sleep-like State

Nath, R. D., Bedbrook, C. N., Abrams, M. J., Basinger, T., Bois, J. S., Prober, D. A., Sternberg, P. W., Gradinaru, V., & Goentoro, L. (2017) · Current Biology · doi:10.1016/j.cub.2017.08.014 · Cited by 260

MRI visual rating scales in the diagnosis of dementia: evaluation in 184 post-mortem confirmed cases

Harper, L., Fumagalli, G. G., Barkhof, F., Scheltens, P., O’Brien, J. T., Bouwman, F., Burton, E. J., Rohrer, J. D., Fox, N. C., Ridgway, G. R., et al. (2016) · Brain · doi:10.1093/brain/aww005 · Cited by 264

Proteome-wide analysis of chaperone-mediated autophagy targeting motifs

Kirchner, P., Bourdenx, M., Madrigal-Matute, J., Tiano, S., Diaz, A., Bartholdy, B. A., Will, B., & Cuervo, A. M. (2019) · PLOS Biology · doi:10.1371/journal.pbio.3000301 · Cited by 257

Measurements of the E -mode polarization and temperature- E -mode correlation of the CMB from SPT-3G 2018 data

Dutcher, D., Balkenhol, L., Ade, P. A. R., Ahmed, Z., Anderes, E., Anderson, A. J., Archipley, M., Avva, J. S., Aylor, K., Barry, P. S., et al. (2021) · Physical Review D · doi:10.1103/physrevd.104.022003 · Cited by 249

Kraken reveals itself – the merger history of the Milky Way reconstructed with the E-MOSAICS simulations

Kruijssen, J. M. D., Pfeffer, J. L., Chevance, M., Bonaca, A., Trujillo-Gomez, S., Bastian, N., Reina-Campos, M., Crain, R. A., & Hughes, M. E. (2020) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/staa2452 · Cited by 252

Ancestral Circuits for the Coordinated Modulation of Brain State

Lovett-Barron, M., Andalman, A. S., Allen, W. E., Vesuna, S., Kauvar, I., Burns, V. M., & Deisseroth, K. (2017) · Cell · doi:10.1016/j.cell.2017.10.021 · Cited by 212

eleanor: An Open-source Tool for Extracting Light Curves from the TESS Full-frame Images

Feinstein, A. D., Montet, B. T., Foreman-Mackey, D., Bedell, M. E., Saunders, N., Bean, J. L., Christiansen, J. L., Hedges, C., Luger, R., Scolnic, D., et al. (2019) · Publications of the Astronomical Society of the Pacific · doi:10.1088/1538-3873/ab291c · Cited by 266

modlAMP: Python for antimicrobial peptides

Müller, A. T., Gabernet, G., Hiss, J. A., & Schneider, G. (2017) · Bioinformatics · doi:10.1093/bioinformatics/btx285 · Cited by 215

Gene regulatory network reconstruction using single-cell RNA sequencing of barcoded genotypes in diverse environments

Jackson, C. A., Castro, D. M., Saldi, G.-A., Bonneau, R., & Gresham, D. (2020) · eLife · doi:10.7554/elife.51254 · Cited by 174

Suppressed Sensory Response to Predictable Object Stimuli throughout the Ventral Visual Stream

Richter, D., Ekman, M., & de Lange, F. P. (2018) · The Journal of Neuroscience · doi:10.1523/jneurosci.3421-17.2018 · Cited by 150

Making AI Forget You: Data Deletion in Machine Learning

Ginart, A., Guan, M. Y., Valiant, G., & Zou, J. (2019) · arXiv · doi:10.48550/arxiv.1907.05012 · Cited by 175

Extensive loss of cell-cycle and DNA repair genes in an ancient lineage of bipolar budding yeasts

Steenwyk, J. L., Opulente, D. A., Kominek, J., Shen, X.-X., Zhou, X., Labella, A. L., Bradley, N. P., Eichman, B. F., Čadež, N., Libkind, D., et al. (2019) · PLoS Biology · doi:10.1371/journal.pbio.3000255 · Cited by 162

Describing and measuring the pathway to suicide attempts: A preliminary study

Millner, A. J., Lee, M. D., & Nock, M. K. (2017) · Suicide and Life-Threatening Behavior · doi:10.1111/sltb.12284 · Cited by 161

Circuitscape in Julia: High Performance Connectivity Modelling to Support Conservation Decisions

(2020) · JuliaCon Proceedings · doi:10.21105/jcon.00058 · Cited by 163

Large-scale network analysis reveals the sequence space architecture of antibody repertoires

Miho, E., Roškar, R., Greiff, V., & Reddy, S. T. (2019) · Nature Communications · doi:10.1038/s41467-019-09278-8 · Cited by 138

Anxiety level recognition for virtual reality therapy system using physiological signals

Šalkevicius, J., Damaševičius, R., Maskeliunas, R., & Laukienė, I. (2019) · Electronics · doi:10.3390/electronics8091039 · Cited by 135

Data Clustering using Memristor Networks

Choi, S., Sheridan, P., & Lu, W. D. (2015) · Scientific Reports · doi:10.1038/srep10492 · Cited by 132

Environmental Impact of Buildings─What Matters?

Heeren, N., Mutel, C. L., Steubing, B., Ostermeyer, Y., Wallbaum, H., & Hellweg, S. (2015) · Environmental Science & Technology · doi:10.1021/acs.est.5b01735 · Cited by 128

Intrinsic map dynamics exploration for uncharted effective free-energy landscapes

Chiavazzo, E., Covino, R., Coifman, R. R., Gear, C. W., Georgiou, A. S., Hummer, G., & Kevrekidis, I. G. (2017) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1621481114 · Cited by 125

PyPhi: A toolbox for integrated information theory

Mayner, W. G. P., Marshall, W., Albantakis, L., Findlay, G., Marchman, R., & Tononi, G. (2018) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1006343 · Cited by 118

Precise cancer detection via the combination of functionalized SERS surfaces and convolutional neural network with independent inputs

Erzina, M., Trelin, A., Guselnikova, O., Dvorankova, B., Strnadova, K., Perminova, A., Ulbrich, P., Mares, D., Jerabek, V., Elashnikov, R., et al. (2020) · Sensors and Actuators B: Chemical · doi:10.1016/j.snb.2020.127660 · Cited by 116

Circuitscape in julia: empowering dynamic approaches to connectivity assessment

Hall, K. R., Anantharaman, R., Landau, V. A., Clark, M., Dickson, B. G., Jones, A., Platt, J., Edelman, A., & Shah, V. B. (2021) · Land · doi:10.3390/land10030301 · Cited by 115

Individual stellar haloes of massive galaxies measured to 100 kpc at 0.3 < z < 0.5 using Hyper Suprime-Cam

Huang, S., Leauthaud, A., Greene, J. E., Bundy, K., Lin, Y.-T., Tanaka, M., Miyazaki, S., & Komiyama, Y. (2018) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stx3200 · Cited by 113

Solubility-Weighted Index: fast and accurate prediction of protein solubility

Bhandari, B. K., Gardner, P. P., & Lim, C. S. (2020) · Bioinformatics · doi:10.1093/bioinformatics/btaa578 · Cited by 97

Using Gaia DR2 to constrain local dark matter density and thin dark disk

Buch, J., Leung, S. C. (., & Fan, J. (2019) · Journal of Cosmology and Astroparticle Physics · doi:10.1088/1475-7516/2019/04/026 · Cited by 110

Deep learning-enabled multi-organ segmentation in whole-body mouse scans

Schoppe, O., Pan, C., Coronel, J., Mai, H., Rong, Z., Todorov, M. I., Müskes, A., Navarro, F., Li, H., Ertürk, A., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-19449-7 · Cited by 104

Whither AutoML? Understanding the Role of Automation in Machine Learning Workflows

Xin, D., Wu, E. Y., Lee, D. J.-L., Salehi, N., & Parameswaran, A. (2021) · Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3411764.3445306 · Cited by 110

An uncertainty principle for star formation – II. A new method for characterizing the cloud-scale physics of star formation and feedback across cosmic history

Kruijssen, J. M. D., Schruba, A., Hygate, A. P. S., Hu, C.-Y., Haydon, D. T., & Longmore, S. N. (2018) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/sty1128 · Cited by 103

Detection of the LMC-induced sloshing of the Galactic halo

Erkal, D., Deason, A. J., Belokurov, V., Xue, X.-X., Koposov, S. E., Bird, S. A., Liu, C., Simion, I. T., Yang, C., Zhang, L., et al. (2021) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stab1828 · Cited by 101

Divergent functional isoforms drive niche specialisation for nutrient acquisition and use in rumen microbiome

Rubino, F., Carberry, C., Waters, S. M., Kenny, D., McCabe, M. S., & Creevey, C. J. (2017) · The ISME Journal · doi:10.1038/ismej.2016.172 · Cited by 101

A modular computational framework for medical digital twins

Masison, J., Beezley, J., Mei, Y., Ribeiro, H., Knapp, A. C., Sordo Vieira, L., Adhikari, B., Scindia, Y., Grauer, M., Helba, B., et al. (2021) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2024287118 · Cited by 98

Evaluation of Data Sharing After Implementation of the International Committee of Medical Journal Editors Data Sharing Statement Requirement

Danchev, V., Min, Y., Borghi, J., Baiocchi, M., & Ioannidis, J. P. A. (2021) · JAMA Network Open · doi:10.1001/jamanetworkopen.2020.33972 · Cited by 94

Hippocampal replay of experience at real-world speeds

Denovellis, E. L., Gillespie, A. K., Coulter, M. E., Sosa, M., Chung, J. E., Eden, U. T., & Frank, L. M. (2021) · eLife · doi:10.7554/elife.64505 · Cited by 89

Automotive big data: Applications, workloads and infrastructures

Luckow, A., Kennedy, K., Manhardt, F., Djerekarov, E., Vorster, B., & Apon, A. (2015) · 2015 IEEE International Conference on Big Data (Big Data) · doi:10.1109/bigdata.2015.7363874 · Cited by 92

Cerebellar involvement in an evidence-accumulation decision-making task

Deverett, B., Koay, S. A., Oostland, M., & Wang, S. S.-H. (2018) · eLife · doi:10.7554/elife.36781 · Cited by 85

Constraints on ΛCDM extensions from the SPT-3G 2018 EE and TE power spectra

Balkenhol, L., Dutcher, D., Ade, P. A. R., Ahmed, Z., Anderes, E., Anderson, A. J., Archipley, M., Avva, J. S., Aylor, K., Barry, P. S., et al. (2021) · Physical Review D · doi:10.1103/physrevd.104.083509 · Cited by 84

Classifiers and their Metrics Quantified

Brown, J. B. (2018) · Molecular Informatics · doi:10.1002/minf.201700127 · Cited by 82

Dual midbrain and forebrain origins of thalamic inhibitory interneurons

Jager, P., Moore, G., Calpin, P., Durmishi, X., Salgarella, I., Menage, L., Kita, Y., Wang, Y., Kim, D. W., Blackshaw, S., et al. (2021) · eLife · doi:10.7554/elife.59272 · Cited by 61

Generalized Density-Functional Tight-Binding Repulsive Potentials from Unsupervised Machine Learning

Kranz, J. J., Kubillus, M., Ramakrishnan, R., von Lilienfeld, O. A., & Elstner, M. (2018) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.7b00933 · Cited by 63

Real-time computer-aided diagnosis of focal pancreatic masses from endoscopic ultrasound imaging based on a hybrid convolutional and long short-term memory neural network model

Udriștoiu, A. L., Cazacu, I. M., Gruionu, L. G., Gruionu, G., Iacob, A. V., Burtea, D. E., Ungureanu, B. S., Costache, M. I., Constantin, A., Popescu, C. F., et al. (2021) · PLOS ONE · doi:10.1371/journal.pone.0251701 · Cited by 63

Quantitative phosphoproteomics uncovers dysregulated kinase networks in Alzheimer’s disease

Morshed, N., Lee, M. J., Rodriguez, F. H., Lauffenburger, D. A., Mastroeni, D., & White, F. M. (2021) · Nature Aging · doi:10.1038/s43587-021-00071-1 · Cited by 58

OpenSoundscape: An open‐source bioacoustics analysis package for Python

Lapp, S., Rhinehart, T., Freeland‐Haynes, L., Khilnani, J., Syunkova, A., & Kitzes, J. (2023) · Methods in Ecology and Evolution · doi:10.1111/2041-210x.14196 · Cited by 57

Hyperactive CDK2 Activity in Basal-like Breast Cancer Imposes a Genome Integrity Liability that Can Be Exploited by Targeting DNA Polymerase ε

Sviderskiy, V. O., Blumenberg, L., Gorodetsky, E., Karakousi, T. R., Hirsh, N., Alvarez, S. W., Terzi, E. M., Kaparos, E., Whiten, G. C., Ssebyala, S., et al. (2020) · Molecular Cell · doi:10.1016/j.molcel.2020.10.016 · Cited by 56

Large-Vessel Vasculopathy in Children With Sickle Cell Disease: A Magnetic Resonance Imaging Study of Infarct Topography and Focal Atrophy

Guilliams, K. P., Fields, M. E., Ragan, D. K., Chen, Y., Eldeniz, C., Hulbert, M. L., Binkley, M. M., Rhodes, J. N., Shimony, J. S., McKinstry, R. C., et al. (2017) · Pediatric Neurology · doi:10.1016/j.pediatrneurol.2016.11.005 · Cited by 51

Searching for Changing-state AGNs in Massive Data Sets. I. Applying Deep Learning and Anomaly-detection Techniques to Find AGNs with Anomalous Variability Behaviors

Sánchez-Sáez, P., Lira, H., Martí, L., Sánchez-Pi, N., Arredondo, J., Bauer, F. E., Bayo, A., Cabrera-Vives, G., Donoso-Oliva, C., Estévez, P. A., et al. (2021) · The Astronomical Journal · doi:10.3847/1538-3881/ac1426 · Cited by 47

Un-gating and allosteric modulation of a pentameric ligand-gated ion channel captured by molecular dynamics

Martin, N. E., Malik, S., Calimet, N., Changeux, J.-P., & Cecchini, M. (2017) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1005784 · Cited by 38

Breathing Biofeedback for Police Officers in a Stressful Virtual Environment: Challenges and Opportunities

Brammer, J. C., van Peer, J. M., Michela, A., van Rooij, M. M. J. W., Oostenveld, R., Klumpers, F., Dorrestijn, W., Granic, I., & Roelofs, K. (2021) · Frontiers in Psychology · doi:10.3389/fpsyg.2021.586553 · Cited by 38

MindLink-Eumpy: An Open-Source Python Toolbox for Multimodal Emotion Recognition

Li, R., Liang, Y., Liu, X., Wang, B., Huang, W., Cai, Z., Ye, Y., Qiu, L., & Pan, J. (2021) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2021.621493 · Cited by 36

Development of a nationally representative set of combined building energy and indoor air quality models for U.S. residences

Fazli, T., & Stephens, B. (2018) · Building and Environment · doi:10.1016/j.buildenv.2018.03.047 · Cited by 35

CPU versus GPU: which can perform matrix computation faster—performance comparison for basic linear algebra subprograms

Li, F., Ye, Y., Tian, Z., & Zhang, X. (2019) · Neural Computing and Applications · doi:10.1007/s00521-018-3354-z · Cited by 29

Probing the Southern Fermi Bubble in Ultraviolet Absorption Using Distant AGNs

Karim, M. T., Fox, A. J., Jenkins, E. B., Bordoloi, R., Wakker, B. P., Savage, B. D., Lockman, F. J., Crawford, S. M., Jorgenson, R. A., & Bland-Hawthorn, J. (2018) · The Astrophysical Journal · doi:10.3847/1538-4357/aac167 · Cited by 29

Implicit Cognitions as a Behavioral Marker of Suicide Attempts in Adolescents

Millner, A. J., Augenstein, T. M., Visser, K. H., Gallagher, K., Vergara, G. A., D’Angelo, E. J., & Nock, M. K. (2019) · Archives of Suicide Research · doi:10.1080/13811118.2017.1421488 · Cited by 29

Comparison of the 3D structures of mouse and human α-synuclein fibrils by solid-state NMR and STEM

Hwang, S., Fricke, P., Zinke, M., Giller, K., Wall, J. S., Riedel, D., Becker, S., & Lange, A. (2019) · Journal of Structural Biology · doi:10.1016/j.jsb.2018.04.003 · Cited by 22

Exploratory Data Analysis of Cell and Mitochondrial High-Fat, High-Sugar Toxicity on Human HepG2 Cells

Amorim, R., Simões, I., Veloso, C., Carvalho, A., Simões, R., Pereira, F., Thiel, T., Normann, A., Morais, C., Jurado, A., et al. (2021) · Nutrients · doi:10.3390/nu13051723 · Cited by 21

Modeling methyl-sensitive transcription factor motifs with an expanded epigenetic alphabet

Viner, C., Ishak, C. A., Johnson, J., Walker, N. J., Shi, H., Sjöberg-Herrera, M. K., Shen, S. Y., Lardo, S. M., Adams, D. J., Ferguson-Smith, A. C., et al. (2016) · doi:10.1101/043794 · Cited by 18

Assessing the robustness of the Galactic rotation curve inferred from the Jeans equations using Gaia DR3 and cosmological simulations

Koop, O., Antoja, T., Helmi, A., Callingham, T. M., & Laporte, C. F. P. (2024) · Astronomy & Astrophysics · doi:10.1051/0004-6361/202450911 · Cited by 14

The Structure and Stability of Extended, Inclined Circumplanetary Disc or Ring Systems

Speedie, J., & Zanazzi, J. J. (2020) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/staa2068 · Cited by 10

GAVIP: a platform for Gaia data analysis

Vagg, D., O'Callaghan, D., Ó hÓgáin, F., McBreen, S., Hanlon, L., Lynn, D., & O'Mullane, W. (2016) · SPIE Proceedings · doi:10.1117/12.2233619 · Cited by 5