seaborn: statistical data visualization

Matplotlib: a 2D graphics environment

Hunter, J. D. (2007) · Computing in Science & Engineering · doi:10.1109/mcse.2007.55 · Cited by 39,249

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

Exploratory Data Analysis

David, F. N., & Tukey, J. W. (1977) · Biometrics · doi:10.2307/2529486 · Cited by 12,707

Data Structures for Statistical Computing in Python

McKinney, W. (2010) · Proceedings of the Python in Science Conferences · doi:10.25080/majora-92bf1922-00a · Cited by 12,469

Inference by Eye: Confidence Intervals and How to Read Pictures of Data.

Cumming, G., & Finch, S. (2005) · American Psychologist · doi:10.1037/0003-066x.60.2.170 · Cited by 1,449 · cited 1×

Tidy Data

Wickham, H. (2014) · Journal of Statistical Software · doi:10.18637/jss.v059.i10 · Cited by 939

Distributed representation of context by intrinsic subnetworks in prefrontal cortex

Waskom, M., & Wagner, A. D. (2017) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1615269114 · Cited by 29

Forecasting small area populations with long short-term memory networks

Grossman, I., Wilson, T., & Temple, J. (2023) · Socio-Economic Planning Sciences · doi:10.1016/j.seps.2023.101658 · Cited by 27

Discovering Attention-Based Genetic Algorithms via Meta-Black-Box Optimization

Lange, R., Schaul, T., Chen, Y., Lu, C. X., Zahavy, T., Dalibard, V., & Flennerhag, S. (2023) · Annual Conference on Genetic and Evolutionary Computation · doi:10.1145/3583131.3590496 · Cited by 49

Investigating the visible phase-curve variability of 55 Cnc e

Valdés, E. A. M., Morris, B. M., Demory, B.-O., Brandeker, A., Kitzmann, D., Benz, W., Deline, A., Florén, H.-G., Sousa, S. G., Bourrier, V., et al. (2023) · Astronomy and Astrophysics · doi:10.1051/0004-6361/202346050 · Cited by 23

A genetically encoded sensor for visualizing leukotriene B4 gradients in vivo

Tamás, S. X., Roux, B. T., Vámosi, B., Dehne, F., Török, A., Fazekas, L., & Enyedi, B. (2023) · Nature Communications · doi:10.1038/s41467-023-40326-6 · Cited by 23

Multielement Z-tag imaging by X-ray fluorescence microscopy for next-generation multiplex imaging

Strotton, M. C., Hosogane, T., Michiel, M. D., Moch, H., Varga, Z., & Bodenmiller, B. (2023) · Nature Methods · doi:10.1038/s41592-023-01977-x · Cited by 23

Random, de novo, and conserved proteins: How structure and disorder predictors perform differently

Middendorf, L., & Eicholt, L. A. (2024) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.26652 · Cited by 24

Computed entropy spectra for grain boundary segregation in polycrystals

Tuchinda, N., & Schuh, C. A. (2024) · npj Computational Materials · doi:10.1038/s41524-024-01260-3 · Cited by 26

U6 snRNA m6A modification is required for accurate and efficient splicing of C. elegans and human pre-mRNAs

Shen, A., Hencel, K., Parker, M. T., Scott, R., Skukan, R., Adesina, A. S., Metheringham, C. L., Miska, E. A., Nam, Y., Haerty, W., et al. (2024) · Nucleic Acids Research · doi:10.1093/nar/gkae447 · Cited by 22

Offline hippocampal reactivation during dentate spikes supports flexible memory

McHugh, S. B., Lopes‐dos‐Santos, V., Castelli, M., Gava, G. P., Thompson, S. E., Tam, S. K. E., Hartwich, K., Perry, B. A. L., Toth, R. A., Denison, T., et al. (2024) · Neuron · doi:10.1016/j.neuron.2024.08.022 · Cited by 26

MicroRNA expression in the heart of Xenopus laevis facilitates metabolic adaptation to dehydration

Hawkins, L. J., & Storey, K. B. (2020) · Genomics · doi:10.1016/j.ygeno.2020.04.003 · Cited by 23

The role of oxygenated species in the growth of graphene, fullerenes and carbonaceous particles

León, G., Martin, J. W., Bringley, E. J., Akroyd, J., & Kraft, M. (2021) · Carbon · doi:10.1016/j.carbon.2021.05.052 · Cited by 23

2020 BioImage Analysis Survey: Community experiences and needs for the future

Jamali, N., Dobson, E. T. A., Eliceiri, K. W., Carpenter, A. E., & Cimini, B. A. (2021) · Biological Imaging · doi:10.1017/s2633903x21000039 · Cited by 23

Discovery of archaeal Fusexins homologous to eukaryotic HAP2/GCS1 gamete fusion proteins

Moi, D., Nishio, S., Li, X., Valansi, C., Langleib, M., Brukman, N. G., Flyak, K., Dessimoz, C., de Sanctis, D., Tunyasuvunakool, K., et al. (2021) · doi:10.1101/2021.10.13.464100 · Cited by 23

Sarracen: a Python package for analysis and visualization of smoothed particle hydrodynamics data

Harris, A., & Tricco, T. S. (2023) · The Journal of Open Source Software · doi:10.21105/joss.05263 · Cited by 23

A cell atlas of the adult female Aedes aegypti midgut revealed by single-cell RNA sequencing

Wang, S., Huang, Y., Wang, F., Han, Q., Ren, N., Wang, X., Cui, Y., Yuan, Z., & Xia, H. (2024) · Scientific Data · doi:10.1038/s41597-024-03432-8 · Cited by 22

Functionally diverse microbial communities show resilience in response to a record-breaking rain event

Walker, J. R., Woods, A. C., Pierce, M. K., Steichen, J. L., Quigg, A. S., Kaiser, K., & Labonté, J. (2022) · ISME Communications · doi:10.1038/s43705-022-00162-z · Cited by 24

A Century of Evolution in Spine Surgery Publications: A Bibliometric Analysis of the Field From 1900 to 2023

Kurland, D. B., Cheung, A. T. M., Kim, N. C., Ashayeri, K., Hidalgo, T., Frempong-Boadu, A. K., Oermann, E. K., & Kondziolka, D. S. (2023) · Neurosurgery · doi:10.1227/neu.0000000000002648 · Cited by 22

Comparative life cycle impact assessment of offshore support vessels powered by alternative fuels for sustainable offshore wind operations using machine learning

Ahmed, S., Li, T., Li, S. Y., & Chen, R. (2023) · Journal of Ocean Engineering and Science · doi:10.1016/j.joes.2023.10.005 · Cited by 22

Tackling data challenges in forecasting effluent characteristics of wastewater treatment plants

Roohi, A. M., Nazif, S., & Ramazi, P. (2024) · Journal of Environmental Management · doi:10.1016/j.jenvman.2024.120324 · Cited by 22

Converging and evolving immuno-genomic routes toward immune escape in breast cancer

Blanco-Heredia, J., Souza, C. A., Trincado, J. L., González‐Cao, M., Gonçalves-Ribeiro, S., Gil, S. R., Pravdyvets, D., Cedeño, S., Callari, M., Marra, A., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-45292-1 · Cited by 22

Genomic and epidemiological evidence for the emergence of a L. infantum/L. donovani hybrid with unusual epidemiology in northern Italy

Bruno, F., Castelli, G., Li, B., Reale, S., Carra, E., Vitale, F., Scibetta, S., Calzolari, M., Varani, S., Ortalli, M., et al. (2024) · mBio · doi:10.1128/mbio.00995-24 · Cited by 22

Predictive interpretable analytics models for forecasting healthcare costs using open healthcare data

Rao, A. R., Jain, R., Singh, M., & Garg, R. (2024) · Healthcare Analytics · doi:10.1016/j.health.2024.100351 · Cited by 28

Probing fermionic asymmetric dark matter cores using global neutron star properties

Rutherford, N., Prescod-Weinstein, C., & Watts, A. L. (2025) · Physical review. D/Physical review. D. · doi:10.1103/physrevd.111.123034 · Cited by 22

Theory-guided machine learning for thermal modeling of in-situ automated fiber placement of thermoplastic composites

Fontes, A., Zobeiry, N., & Shadmehri, F. (2024) · Composites Science and Technology · doi:10.1016/j.compscitech.2024.110987 · Cited by 22

A Bayesian active learning platform for scalable combination drug screens

Tosh, C., Tec, M., White, J. B., Quinn, J. F., Sanchez, G. I., Calder, P., Kung, A. L., Cruz, F. S. D., & Tansey, W. (2025) · Nature Communications · doi:10.1038/s41467-024-55287-7 · Cited by 31

Multimodal fusion diagnosis of the Alzheimer’s disease via lightweight CNN-LSTM model using magnetic resonance imaging (MRI)

Haq, E. U., Qin, Y., Zhou, Y., Xu, H., & Haq, R. U. (2025) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2025.107545 · Cited by 27

Investigating the Performance of Oxford Nanopore Long-Read Sequencing with Respect to Illumina Microarrays and Short-Read Sequencing

Santos, R., Lee, H. A., Williams, A., Baffour-Kyei, A., Lee, S. H., Troakes, C., Al‐Chalabi, A., Breen, G. D., & Iacoangeli, A. (2025) · International Journal of Molecular Sciences · doi:10.3390/ijms26104492 · Cited by 28

Epigenomic diagnosis and prognosis of Acute Myeloid Leukemia

Marchi, F., Shastri, V. M., Marrero, R. J., Nguyen, N., Öttl, A., Schade, A.-K., Landwehr, M., Krali, O., Nordlund, J., Ghavami, M., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-62005-4 · Cited by 28

Methanogenesis inhibition remodels microbial fermentation and stimulates acetogenesis in ruminants

Ni, G., Wang, M., Walker, N. D., Muetzel, S., Schmidt, O., Fischer, A., Stemmler, R. T., Leung, P. M., Zhang, X., Li, Q., et al. (2025) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2514823122 · Cited by 27

Calcifying plankton: From biomineralization to global change

Ziveri, P., Langer, G., Chaabane, S., de Vries, J., Gray, W. R., Keul, N., Hatton, I. A., Manno, C., Norris, R. D., Pallacks, S., et al. (2025) · Science · doi:10.1126/science.adq8520 · Cited by 25

Prediction of Molecular Properties Using Molecular Topographic Map

Yoshimori, A. (2021) · Molecules · doi:10.3390/molecules26154475 · Cited by 23

Data Science Toolkit: An all-in-one python library to help researchers and practitioners in implementing data science-related algorithms with less effort

Hachimi, C. E., Belaqziz, S., Khabba, S., & Chehbouni, A. (2022) · Software Impacts · doi:10.1016/j.simpa.2022.100240 · Cited by 27

Response of Eurasian Temperature to Barents–Kara Sea Ice: Evaluation by Multi‐Model Seasonal Predictions

Komatsu, K. K., Takaya, Y., Toyoda, T., & Hasumi, H. (2022) · Geophysical Research Letters · doi:10.1029/2021gl097203 · Cited by 22

Constraining scalarization in scalar-Gauss-Bonnet gravity through binary pulsars

Danchev, V. I., Doneva, D. D., & Yazadjiev, S. S. (2022) · Physical review. D/Physical review. D. · doi:10.1103/physrevd.106.124001 · Cited by 22

The extinction law in the inner 3 × 3 deg2 of the Milky Way and the red clump absolute magnitude in the inner bar-bulge

Sanders, J. L., Smith, L., González-Fernández, C., Lucas, P., & Minniti, D. (2022) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stac1367 · Cited by 22

A Dual Enrichment Strategy Provides Soil- and Digestate-Competent Nitrous Oxide-Respiring Bacteria for Mitigating Climate Forcing in Agriculture

Jonassen, K. R., Ormaasen, I., Duffner, C., Hvidsten, T. R., Bakken, L. R., Vick, S. H. W., & Vick, S. H. W. (2022) · mBio · doi:10.1128/mbio.00788-22 · Cited by 22

Thermal Budgets of Magma Storage Constrained by Diffusion Chronometry: the Cerro Galán Ignimbrite

Lubbers, J. E., Kent, A. J. R., & de Silva, S. L. (2022) · Journal of Petrology · doi:10.1093/petrology/egac048 · Cited by 22

Using Data Mining Techniques for Detecting Dependencies in the Outcoming Data of a Web-Based System

Rak, T., & Żyła, R. (2022) · Applied Sciences · doi:10.3390/app12126115 · Cited by 22

Identification of micro- and nanoplastics released from medical masks using hyperspectral imaging and deep learning

Ishmukhametov, I., Batasheva, S., & Fakhrullin, R. F. (2022) · The Analyst · doi:10.1039/d2an01139e · Cited by 22

Rethinking data-driven point spread function modeling with a differentiable optical model

Liaudat, T. I., Starck, J., Kilbinger, M., & Frugier, P.-A. (2023) · Inverse Problems · doi:10.1088/1361-6420/acb664 · Cited by 22

Adapting to noise distribution shifts in flow-based gravitational-wave inference

Wildberger, J., Dax, M., Green, S., Gair, J. R., Pürrer, M., Macke, J. H., Buonanno, A., & Schölkopf, B. (2023) · Physical review. D/Physical review. D. · doi:10.1103/physrevd.107.084046 · Cited by 22

Targeting macrophage Syk enhances responses to immune checkpoint blockade and radiotherapy in high-risk neuroblastoma

Rohila, D., Park, I. H., Pham, T. V., Jones, R. N., Tapia, E., Liu, K. X., Tamayo, P., Yu, A. L.-T., Sharabi, A. B., & Joshi, S. (2023) · Frontiers in Immunology · doi:10.3389/fimmu.2023.1148317 · Cited by 22

An exploration of machine learning models for the determination of reaction coordinates associated with conformational transitions

Naleem, N., Abreu, C. R. A., Warmuz, K., Tong, M., Kırmızıaltın, S., & Tuckerman, M. E. (2023) · The Journal of Chemical Physics · doi:10.1063/5.0147597 · Cited by 22

Modeling New Trends in Bone Regeneration, using the BERTopic Approach

Guizzardi, S., Colangelo, M. T., Mirandola, P., & Galli, C. (2023) · Regenerative Medicine · doi:10.2217/rme-2023-0096 · Cited by 23

Navigating the Chemical Space and Chemical Multiverse of a Unified Latin American Natural Product Database: LANaPDB

Gómez‐García, A., Jiménez, D. A. A., Zamora, W. J., Barazorda-Ccahuana, H. L., Chávez‐Fumagalli, M. Á., Valli, M., Andricopulo, A. D., Bolzani, V. D. S., Olmedo, D. A., Solís, P. N., et al. (2023) · Pharmaceuticals · doi:10.3390/ph16101388 · Cited by 25

Single basolateral amygdala neurons in macaques exhibit distinct connectional motifs with frontal cortex

Zeisler, Z. R., London, L., Janssen, W. G. M., Fredericks, J. M., Elorette, C., Fujimoto, A., Zhan, H., Russ, B. E., Clem, R. L., Hof, P. R., et al. (2023) · Neuron · doi:10.1016/j.neuron.2023.09.024 · Cited by 22

A comparison of clustering models for inference of T cell receptor antigen specificity

Hudson, D., Lubbock, A. L. R., Basham, M., & Koohy, H. (2024) · ImmunoInformatics · doi:10.1016/j.immuno.2024.100033 · Cited by 18

Using explainable machine learning and fitbit data to investigate predictors of adolescent obesity

Kiss, O., Baker, F. C., Pálovics, R., Dooley, E. E., Gabriel, K. P., & Nagata, J. M. (2024) · Scientific Reports · doi:10.1038/s41598-024-60811-2 · Cited by 27

Multi-scale spiking network model of human cerebral cortex

Pronold, J., van Meegen, A., Shimoura, R. O., Vollenbröker, H., Senden, M., Hilgetag, C. C., Bakker, R., & van Albada, S. J. (2024) · Cerebral Cortex · doi:10.1093/cercor/bhae409 · Cited by 26

The visual analytics of big, open public transport data – a framework and pipeline for monitoring system performance in Greater Sydney

Lock, O., Bednarz, T. P., & Pettit, C. J. (2020) · Big Earth Data · doi:10.1080/20964471.2020.1758537 · Cited by 22

A clustering solution for analyzing residential water consumption patterns

Rahim, M. S., Nguyen, K. A., Stewart, R. A., Ahmed, T., Giurco, D., & Blumenstein, M. (2021) · Knowledge-Based Systems · doi:10.1016/j.knosys.2021.107522 · Cited by 22

Making Biomedical Research Software FAIR: Actionable Step-by-step Guidelines with a User-support Tool

Patel, B. J., Soundarajan, S., Ménager, H., & Hu, Z. (2023) · Scientific Data · doi:10.1038/s41597-023-02463-x · Cited by 22

Evaluation of genetic demultiplexing of single-cell sequencing data from model species

Cardiello, J. F., Joven Araus, A., Giatrellis, S., Helsens, C., Simon, A., & Leigh, N. D. (2023) · Life Science Alliance · doi:10.26508/lsa.202301979 · Cited by 21

Trace Metals in Global Air: First Results from the GAPS and GAPS Megacities Networks

Mastin, J., Saini, A., Schuster, J. K., Harner, T., Dąbek-Złotorzyńska, E., Celo, V., & Gaga, E. O. (2023) · Environmental Science & Technology · doi:10.1021/acs.est.3c05733 · Cited by 21

Machine learning based prediction of compressive and flexural strength of recycled plastic waste aggregate concrete

Yılmaz, Y., & Nayır, S. (2024) · Structures · doi:10.1016/j.istruc.2024.107363 · Cited by 26

Teosinte-derived SynCom and precision biofertilization modulate the maize microbiome, enhancing growth, yield, and soil functionality in a Mexican field

Hernández-García, J. A., Bernal, J. S., Antony‐Babu, S., Villa‐Tanaca, L., Hernández, G., & la Vega-Camarillo, E. D. (2025) · Frontiers in Microbiology · doi:10.3389/fmicb.2025.1534327 · Cited by 22

Grain boundary segregation spectra from a generalized machine-learning potential

Tuchinda, N., & Schuh, C. A. (2025) · Scripta Materialia · doi:10.1016/j.scriptamat.2025.116682 · Cited by 21

Genomic diversity of the African malaria vector Anopheles funestus

Boddé, M., Nwezeobi, J., Korlević, P., Makunin, A., Akone‐Ella, O., Barasa, S., Mahamat, G., Hart, L., Kaindoa, E. W., Love, K., et al. (2025) · Science · doi:10.1126/science.adu3596 · Cited by 18

Cohesin guides homology search during DNA repair using loops and sister chromatid linkages

Teloni, F., Takács, Z., Mitter, M., Langer, C. C. H., Prlesi, I., Steinacker, T. L., Reuter, V. P., Mylarshchikov, D. E., & Gerlich, D. W. (2025) · Science · doi:10.1126/science.adw0566 · Cited by 28

Nuclear speckles enable processing of RNA from GC-rich isochores

Małszycki, M., Martina, L., Ilik, İ., Figueroa, D. S., Dasgupta, N., Çoşar, M. F., Kim, K.-T., Carraco, G., Fauler, B., Meierhofer, D., et al. (2026) · Cell · doi:10.1016/j.cell.2026.01.011 · Cited by 21

Genomic islands targeting dusA in Vibrio species are distantly related to Salmonella Genomic Island 1 and mobilizable by IncC conjugative plasmids

Durand, R., Deschênes, F., & Burrus, V. (2021) · PLoS Genetics · doi:10.1371/journal.pgen.1009669 · Cited by 21

Prediction of qualitative antibiofilm activity of antibiotics using supervised machine learning techniques

Shaban, T. F., & Alkawareek, M. Y. (2021) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2021.105065 · Cited by 20

Preparing to Discover the Unknown with Rubin LSST: Time Domain

Li, X., Ragosta, F., Clarkson, W. I., & Bianco, F. B. (2021) · The Astrophysical Journal Supplement Series · doi:10.3847/1538-4365/ac3bca · Cited by 21

Exploratory Data Analysis of the N-CMAPSS Dataset for Prognostics

Chatterjee, S., & Keprate, A. (2021) · doi:10.1109/ieem50564.2021.9673064 · Cited by 21

Coronal Mass Ejections and Type II Radio Emission Variability during a Magnetic Cycle on the Solar-type Star ϵ Eridani

Fionnagáin, D. Ó., Kavanagh, R. D., Vidotto, A. A., Jeffers, S. V., Petit, P. M., Marsden, S. C., Morin, J., & Golden, A. (2022) · The Astrophysical Journal · doi:10.3847/1538-4357/ac35de · Cited by 21

Threat vigilance and intrinsic amygdala connectivity

Kirk, P. A., Holmes, A. J., & Robinson, O. J. (2022) · Human Brain Mapping · doi:10.1002/hbm.25851 · Cited by 42

Deciphering Isoniazid Drug Resistance Mechanisms on Dimeric Mycobacterium tuberculosis KatG via Post-molecular Dynamics Analyses Including Combined Dynamic Residue Network Metrics

Barozi, V., Musyoka, T. M., Amamuddy, O. S., & Bishop, Ö. T. (2022) · ACS Omega · doi:10.1021/acsomega.2c01036 · Cited by 21

Radio Spectra of Luminous, Heavily Obscured WISE-NVSS Selected Quasars

Patil, P., Whittle, M., Nyland, K., Lonsdale, C. J., Lacy, M. D., Kimball, A. E., Lonsdale, C. J., Peters, W., Clarke, T. E., Efstathiou, A., et al. (2022) · The Astrophysical Journal · doi:10.3847/1538-4357/ac71b0 · Cited by 21

Co-expression analysis to identify key modules and hub genes associated with COVID-19 in platelets

Alarabi, A. B., Mohsen, A. S., Mizuguchi, K., Alshbool, F. Z., & Khasawneh, F. T. (2022) · BMC Medical Genomics · doi:10.1186/s12920-022-01222-y · Cited by 23

Property space mapping of Pseudomonas aeruginosa permeability to small molecules

Leus, I. V., Weeks, J. W., Bonifay, V., Shen, Y. L., Yang, L., Cooper, C. J., Nath, D., Duerfeldt, A. S., Smith, J. C., Parks, J. M., et al. (2022) · Scientific Reports · doi:10.1038/s41598-022-12376-1 · Cited by 21

Jet kinematics in the transversely stratified jet of 3C 84

Paraschos, G. F., Krichbaum, T. P., Kim, J.-Y., Hodgson, J. A., Oh, J., Ros, E., Zensus, J. A., Marscher, A. P., Jorstad, S. G., Gurwell, M., et al. (2022) · Astronomy and Astrophysics · doi:10.1051/0004-6361/202243343 · Cited by 21

High-content phenotyping of Parkinson's disease patient stem cell-derived midbrain dopaminergic neurons using machine learning classification

Vuidel, A., Cousin, L., Weykopf, B., Haupt, S., Hanifehlou, Z., Wiest-Daesslé, N., Segschneider, M., Lee, J., Kwon, Y., Peitz, M., et al. (2022) · Stem Cell Reports · doi:10.1016/j.stemcr.2022.09.001 · Cited by 22

A geothermal heat flow model of Africa based on random forest regression

Al‐Aghbary, M., Sobh, M., & Gerhards, C. (2022) · Frontiers in Earth Science · doi:10.3389/feart.2022.981899 · Cited by 21

Chemical order transitions within extended interfacial segregation zones in NbMoTaW

Aksoy, D., McCarthy, M., Geiger, I., Apelian, D., Hahn, H., Lavernia, E. J., Luo, J., Xin, H. L., & Rupert, T. J. (2022) · Journal of Applied Physics · doi:10.1063/5.0122502 · Cited by 21

Temporal variation of the photometric magnetic activity for the Sun and Kepler solar-like stars

Santos, Â. R. G., Mathur, S., García, R. A., Broomhall, A.-M., Egeland, R., Jiménez, A., Godoy-Rivera, D., Breton, S., Claytor, Z. R., Metcalfe, Τ. S., et al. (2023) · Astronomy and Astrophysics · doi:10.1051/0004-6361/202245430 · Cited by 21

Spectral Changes of EEG Following a 6-Week Low-Dose Oral Ketamine Treatment in Adults With Major Depressive Disorder and Chronic Suicidality

Anijärv, T. E., Can, A., Gallay, C. C., Forsyth, G., Dutton, M., Mitchell, J., Hermens, D. F., & Lagopoulos, J. (2023) · The International Journal of Neuropsychopharmacology · doi:10.1093/ijnp/pyad006 · Cited by 21

Estimating the statistical uncertainty due to spatially correlated noise in interferometric images

Tsukui, T., Iguchi, S., Mitsuhashi, I., & Tadaki, K.-I. (2023) · Journal of Astronomical Telescopes Instruments and Systems · doi:10.1117/1.jatis.9.1.018001 · Cited by 21

PCovNet+: A CNN-VAE anomaly detection framework with LSTM embeddings for smartwatch-based COVID-19 detection

Abir, F. F., Chowdhury, M. E. H., Tapotee, M. I., Mushtak, A., Khandakar, A. A., Mahmud, S., & Hasan, A. (2023) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2023.106130 · Cited by 27

Back to the Roots: Agrobacterium -Specific Phages Show Potential to Disinfect Nutrient Solution from Hydroponic Greenhouses

Fortuna, K. J., Holtappels, D., Venneman, J., Baeyen, S., Vallino, M., Verwilt, P., Rediers, H., Coninck, B. D., Maes, M., Vaerenbergh, J. V., et al. (2023) · Applied and Environmental Microbiology · doi:10.1128/aem.00215-23 · Cited by 23

Circulation of a digital community currency

Mattsson, C. E. S., Criscione, T., & Takes, F. W. (2023) · Scientific Reports · doi:10.1038/s41598-023-33184-1 · Cited by 21

Proteome Dynamics During Transition From Exponential to Stationary Phase Under Aerobic and Anaerobic Conditions in Yeast

den Ridder, M., van den Brandeler, W., Altiner, M., Daran-Lapujade, P. A. S., & Pabst, M. (2023) · Molecular & Cellular Proteomics · doi:10.1016/j.mcpro.2023.100552 · Cited by 20

Combatting the mismatch: Modeling bike-sharing rental and return machine learning classification forecast in Seoul, South Korea

Choi, S. J., Jiao, J., Lee, H. K., & Farahi, A. (2023) · Journal of Transport Geography · doi:10.1016/j.jtrangeo.2023.103587 · Cited by 21

Missense mutations in CRX homeodomain cause dominant retinopathies through two distinct mechanisms

Zheng, Y., Sun, C., Zhang, X., Ruzycki, P. A., & Chen, S. (2023) · eLife · doi:10.7554/elife.87147 · Cited by 22

An Interpretable Boosting-Based Predictive Model for Transformation Temperatures of Shape Memory Alloys

Zadeh, S. H., Behbahanian, A., Broucek, J., Fan, M., Vazquez, G., Noroozi, M., Trehern, W., Qian, X., Karaman, İ., & Arróyave, R. (2022) · Computational Materials Science · doi:10.1016/j.commatsci.2023.112225 · Cited by 21

Mitochondrial DNA methylation in metabolic associated fatty liver disease

Mposhi, A., Cortés-Mancera, F., Heegsma, J., de Meijer, V. E., van de Sluis, B., Sydor, S., Bechmann, L. P., Theys, C., Rijk, P. D., Pooter, T. D., et al. (2023) · Frontiers in Nutrition · doi:10.3389/fnut.2023.964337 · Cited by 21

Accurate tumor purity determination is critical for the analysis of homologous recombination deficiency (HRD)

Menzel, M., Endris, V., Schwab, C., Kluck, K., Neumann, O., Beck, S., Ball, M., Schaaf, C. P., Fröhling, S., Lichtner, P., et al. (2023) · Translational Oncology · doi:10.1016/j.tranon.2023.101706 · Cited by 21

Structural definition of HLA class II-presented SARS-CoV-2 epitopes reveals a mechanism to escape pre-existing CD4+ T cell immunity

Chen, Y., Mason, G. H., Scourfield, D. O., Greenshields‐Watson, A., Haigh, T. A., Sewell, A. K., Long, H. M., Gallimore, A., Rizkallah, P. J., MacLachlan, B. J., et al. (2023) · Cell Reports · doi:10.1016/j.celrep.2023.112827 · Cited by 23

Genomic characterization of the antiviral arsenal of Actinobacteria

Georjon, H., Tesson, F., Shomar, H., & Bernheim, A. G. (2023) · Microbiology · doi:10.1099/mic.0.001374 · Cited by 21

Segmenting functional tissue units across human organs using community-driven development of generalizable machine learning algorithms

Jain, Y., Godwin, L. L., Joshi, S., Mandarapu, S., Le, T. H., Lindskog, C., Lundberg, E. K., & Börner, K. (2023) · Nature Communications · doi:10.1038/s41467-023-40291-0 · Cited by 20

Deep Reinforcement Learning for Workload Prediction in Federated Cloud Environments

Ahamed, Z., Khemakhem, M. A., Eassa, F. E., Alsolami, F. J., Basuhail, A. A., & Jambi, K. M. (2023) · Sensors · doi:10.3390/s23156911 · Cited by 21

Ebike Sharing vs. Bike Sharing: Demand Prediction Using Deep Neural Networks and Random Forests

Schnieder, M. (2023) · Sustainability · doi:10.3390/su151813898 · Cited by 22

A neural code for time and space in the human brain

Schonhaut, D. R., Aghajan, Z. M., Kahana, M. J., & Fried, I. (2023) · Cell Reports · doi:10.1016/j.celrep.2023.113238 · Cited by 25

Environmental and Soil Parameters for Germination of Leaf Spot Disease in the Groundnut Plant Using IoT-Enabled Sensor System

Garg, P., Khaparde, P., Patle, K. S., Bhaliya, C., Kumar, A., Joshi, M. V., & Palaparthy, V. S. (2023) · IEEE Sensors Letters · doi:10.1109/lsens.2023.3330923 · Cited by 21

Tracing the rise of supermassive black holes

Andika, I. T., Jahnkę, K., Onoue, M., Silverman, J. D., Fitriana, I. K., Bongiorno, A., Brinch, M., Casey, C. M., Faisst, A. L., Gillman, S., et al. (2024) · Astronomy and Astrophysics · doi:10.1051/0004-6361/202349025 · Cited by 21

2,5-Diketopiperazines (DKPs): Promising Scaffolds for Anticancer Agents

Goher, S. S., Abdrabo, W. S., Veerakanellore, G. B., & Elgendy, B. (2024) · Current Pharmaceutical Design · doi:10.2174/0113816128291798240201112916 · Cited by 21

Characterization of enhancer activity in early human neurodevelopment using Massively Parallel Reporter Assay (MPRA) and forebrain organoids

Capauto, D., Wang, Y., Wu, F., Norton, S., Mariani, J., Inoue, F., Crawford, G. E., Ahituv, N., Abyzov, A. S., & Vaccarino, F. M. (2024) · Scientific Reports · doi:10.1038/s41598-024-54302-7 · Cited by 22

Dynamic antennal positioning allows honeybee followers to decode the dance

Hadjitofi, A., & Webb, B. H. (2024) · Current Biology · doi:10.1016/j.cub.2024.02.045 · Cited by 23

A comparative study on the electrocatalytic OER activity of mesoporous high-entropy oxide and medium-entropy oxide nanoparticles through SCS method

Saghir, A. V., Beidokhti, S. M., & Khaki, J. V. (2024) · Ceramics International · doi:10.1016/j.ceramint.2024.04.139 · Cited by 21

Gravitational waves carry information beyond effective spin parameters but it is hard to extract

Miller, S. J., Ko, Z., Callister, T. A., & Chatziioannou, K. (2024) · Physical review. D/Physical review. D. · doi:10.1103/physrevd.109.104036 · Cited by 21

Machine Learning Strategies to Tackle Data Challenges in Mass Spectrometry-Based Proteomics

Dens, C., Adams, C., Laukens, K., & Bittremieux, W. (2024) · Journal of the American Society for Mass Spectrometry · doi:10.1021/jasms.4c00180 · Cited by 21

Interfacing with Seaborn: A Data Visualization tool

Goyal, A., & Kritika (2024) · doi:10.13140/rg.2.2.12452.69764

plotastic: Bridging Plotting and Statistics in Python

Kuric, M., & Ebert, R. (2024) · The Journal of Open Source Software · doi:10.21105/joss.06304 · Cited by 1

Data Visualisation with Matplotlib and Seaborn

Miraz, M. H., Salleh, N. M., & Jin, H. H. (2025) · Springer eBooks · doi:10.1007/978-981-96-8291-1_4 · Cited by 1

Data Visualization with Python exploring Matplotlib, Seaborn, and Bokeh for Interactive Visualizations

Pooja (2023)

Full Lab to master Seaborn

Benabderrezak, Y. (2025) · doi:10.13140/rg.2.2.23828.16006