seaborn: statistical data visualization

Matplotlib: a 2D graphics environment

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Array programming with NumPy

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Exploratory Data Analysis

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Data Structures for Statistical Computing in Python

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

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Tidy Data

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Distributed representation of context by intrinsic subnetworks in prefrontal cortex

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Widespread monoclonal IgE antibody convergence to an immunodominant, proanaphylactic Ara h 2 epitope in peanut allergy

Croote, D., Wong, J. J. W., Pecalvel, C., Leveque, E., Casanovas, N., Kamphuis, J. B. J., Creeks, P., Romero, J., Sohail, S., Bedinger, D., et al. (2023) · Journal of Allergy and Clinical Immunology · doi:10.1016/j.jaci.2023.08.035 · Cited by 37

Benchmarking of Small Molecule Feature Representations for hERG, Nav1.5, and Cav1.2 Cardiotoxicity Prediction

Arab, I., Egghe, K., Laukens, K., Chen, K., Barakat, K., & Bittremieux, W. (2023) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.3c01301 · Cited by 37

Towards digital twins of waste sorting plants: Developing data-driven process models of industrial-scale sensor-based sorting units by combining machine learning with near-infrared-based process monitoring

Kroell, N., Maghmoumi, A., Dietl, T., Chen, X., Küppers, B., Scherling, T., Feil, A., & Greiff, K. (2023) · Resources Conservation and Recycling · doi:10.1016/j.resconrec.2023.107257 · Cited by 41

Longitudinal single-cell transcriptional dynamics throughout neurodegeneration in SCA1

Tejwani, L., Ravindra, N. G., Lee, C., Cheng, Y., Nguyen, B., Luttik, K. P., Ni, L., Zhang, S., Morrison, L. M., Gionco, J. T., et al. (2023) · Neuron · doi:10.1016/j.neuron.2023.10.039 · Cited by 37

Novel proteomics-based plasma test for early detection of multiple cancers in the general population

Budnik, B., Amirkhani, H., Forouzanfar, M. H., & Afshin, A. (2024) · BMJ Oncology · doi:10.1136/bmjonc-2023-000073 · Cited by 36

Characterizing ATP processing by the AAA+ protein p97 at the atomic level

Shein, M., Hitzenberger, M., Cheng, T. C., Rout, S. R., Leitl, K. D., Sato, Y., Zacharias, M., Sakata, E., & Schütz, A. K. (2024) · Nature Chemistry · doi:10.1038/s41557-024-01440-0 · Cited by 34

Linking ATP and allosteric sites to achieve superadditive binding with bivalent EGFR kinase inhibitors

Wittlinger, F., Ogboo, B. C., Shevchenko, E., Damghani, T., Pham, C. D., Schaeffner, I. K., Oligny, B. T., Chitnis, S. P., Beyett, T. S., Rasch, A., et al. (2024) · Communications Chemistry · doi:10.1038/s42004-024-01108-3 · Cited by 35

Transcription decouples estrogen-dependent changes in enhancer-promoter contact frequencies and spatial proximity

Acuña, L. I. G., Flyamer, I. M., Boyle, S., Friman, E. T., & Bickmore, W. A. (2024) · PLoS Genetics · doi:10.1371/journal.pgen.1011277 · Cited by 37

Transferring spectroscopic stellar labels to 217 million Gaia DR3 XP stars with SHBoost

Khalatyan, A., Anders, F., Chiappini, C., Queiroz, A. B. A., Nepal, S., Ponte, M. D., Jordi, C., Guiglion, G., Valentini, M., Elipe, G. T., et al. (2024) · Astronomy and Astrophysics · doi:10.1051/0004-6361/202451427 · Cited by 49

BiG-SCAPE 2.0 and BiG-SLiCE 2.0: scalable, accurate and interactive sequence clustering of metabolic gene clusters

Draisma, A., Loureiro, C., Louwen, N. L. L., Kautsar, S. A., Navarro-Muñoz, J. C., Doering, D. T., Mouncey, N. J., & Medema, M. H. (2026) · Nature Communications · doi:10.1038/s41467-026-68733-5 · Cited by 37

A rapid, low-cost, and highly sensitive SARS-CoV-2 diagnostic based on whole-genome sequencing

Adastra, P. A., Durand, N. C., Mitra, N., Pulido, S. G., Mahajan, R., Blackburn, A., Colaric, Z., Theisen, J. W. M., Weisz, D., Dudchenko, O., et al. (2023) · PLoS ONE · doi:10.1371/journal.pone.0294283 · Cited by 34

Tardigrades exhibit robust interlimb coordination across walking speeds and terrains

Nirody, J. A., Duran, L. A., Johnston, D., & Cohen, D. J. (2021) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2107289118 · Cited by 37

A large sub-Neptune transiting the thick-disk M4 v TOI-2406

Wells, R. D., Rackham, B. V., Schanche, N., Petrucci, R., Chew, Y. G. M., Demory, B.-O., Burgasser, A. J., Burn, R., Pozuelos, F. J., Günther, M. N., et al. (2021) · UA Campus Repository (The University of Arizona) · doi:10.1051/0004-6361/202141277 · Cited by 37

Visibility matters during wayfinding in the vertical

Gath-Morad, M., Thrash, T., Schicker, J., Hölscher, C., Helbing, D., & Aguilar, L. (2021) · Scientific Reports · doi:10.1038/s41598-021-98439-1 · Cited by 43

The CubeSpec space mission

Bowman, D. M., Vandenbussche, B. K., Sana, H., Tkachenko, A., Raskin, G., Delabie, T., Vandoren, B., Royer, P., Garcia, S., & Reeth, T. V. (2021) · Astronomy and Astrophysics · doi:10.1051/0004-6361/202142375 · Cited by 37

Distal Mutations Shape Substrate-Binding Sites during Evolution of a Metallo-Oxidase into a Laccase

Brissos, V., Borges, P. T., Núñez‐Franco, R., Lucas, M. F., Frazão, C., Monza, E., Masgrau, L., Cordeiro, T. N., & Martins, L. O. (2022) · ACS Catalysis · doi:10.1021/acscatal.2c00336 · Cited by 40

Spatially resolved Kennicutt–Schmidt relation at z ≈ 7 and its connection with the interstellar medium properties

Vallini, L., Witstok, J., Sommovigo, L., Pallottini, A., Ferrara, A., Carniani, S., Kohandel, M., Smit, R., Gallerani, S., & Gruppioni, C. (2023) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stad3150 · Cited by 37

Milky Way Satellite Census. IV. Constraints on Decaying Dark Matter from Observations of Milky Way Satellite GalaxiesPreprint

Mau, S., Nadler, E. O., Wechsler, R. H., Drlica-Wagner, A., Bechtol, K., Green, G. M., Dragan Huterer, A., Li, T. S., Mao, Y.-Y., Martínez-Vázquez, C. E., et al. (2022) · arXiv (Cornell University) · doi:10.3847/1538-4357/ac6e65 · Cited by 37

LEGWORK: A Python Package for Computing the Evolution and Detectability of Stellar-origin Gravitational-wave Sources with Space-based Detectors

Wagg, T., Breivik, K., & de Mink, S. E. (2022) · The Astrophysical Journal Supplement Series · doi:10.3847/1538-4365/ac5c52 · Cited by 37

StepMix: A Python Package for Pseudo-Likelihood Estimation of Generalized Mixture Models with External Variables

Morin, S., Legault, R., Laliberté, F., Bakk, Z., Giguère, C., de la Sablonnière, R., & Lacourse, É. (2025) · Journal of Statistical Software · doi:10.18637/jss.v113.i08 · Cited by 37

Detecting multiple retinal diseases in ultra-widefield fundus imaging and data-driven identification of informative regions with deep learning

Engelmann, J., McTrusty, A. D., MacCormick, I. J. C., Pead, E., Storkey, A., & Bernabeu, M. O. (2022) · Nature Machine Intelligence · doi:10.1038/s42256-022-00566-5 · Cited by 37

Network of hotspot interactions cluster tau amyloid folds

Mullapudi, V., Vaquer‐Alicea, J., Bommareddy, V., Vega, A. R., Ryder, B. D., White, C. L., Diamond, M. I., & Joachimiak, Ł. A. (2023) · Nature Communications · doi:10.1038/s41467-023-36572-3 · Cited by 36

Risks of Ventricular Arrhythmia and Heart Failure in Carriers of RBM20 Variants

Cannie, D. E., Protonotarios, A., Bakalakos, A., Syrris, P., Lorenzini, M., Stavola, B. L. D., Bjerregaard, L., Dybro, A. M., Hey, T. M., Hansen, F. G., et al. (2023) · Circulation Genomic and Precision Medicine · doi:10.1161/circgen.123.004059 · Cited by 36

Life cycle assessment of a marine biorefinery producing protein, bioactives and polymeric packaging material

Amponsah, L., Chuck, C. J., & Parsons, S. (2023) · The International Journal of Life Cycle Assessment · doi:10.1007/s11367-023-02239-w · Cited by 36

Developmental basis of SHH medulloblastoma heterogeneity

Gold, M. P., Ong, W., Masteller, A. M., Ghasemi, D. R., Galindo, J., Park, N. R., Huynh, N. C., Donde, A., Pister, V. I., Saurez, R. A., et al. (2024) · Nature Communications · doi:10.1038/s41467-023-44300-0 · Cited by 36

Discovery of the local counterpart of disc galaxies at z > 4: The oldest thin disc of the Milky Way usingGaia-RVS

Nepal, S., Chiappini, C., Queiroz, A. B. A., Guiglion, G., Montalbán, J., Steinmetz, M., Miglio, A., & Khalatyan, A. (2024) · Astronomy and Astrophysics · doi:10.1051/0004-6361/202449445 · Cited by 36

An explainable language model for antibody specificity prediction using curated influenza hemagglutinin antibodies

Wang, Y., Lv, H., Teo, Q. W., Lei, R., Gopal, A. B., Ouyang, W. O., Yeung, Y.-H., Tan, T. J. C., Choi, D., Shen, I. R., et al. (2024) · Immunity · doi:10.1016/j.immuni.2024.07.022 · Cited by 35

Advanced Machine Learning Techniques for Predicting Concrete Compressive Strength

Tak, M. S. N., Feng, Y., & Mahgoub, M. (2025) · Infrastructures · doi:10.3390/infrastructures10020026 · Cited by 43

Pitfalls of bacterial pan-genome analysis approaches: a case study of Mycobacterium tuberculosis and two less clonal bacterial species

Marin, M. G., Quinones‐Olvera, N., Wippel, C., Behruznia, M., Jeffrey, B. M., Harris, M., Mann, B., Rosenthal, A., Jacobson, K. R., Warren, R. M., et al. (2025) · Bioinformatics · doi:10.1093/bioinformatics/btaf219 · Cited by 36

Validation of Bioinformatic Modeling for the Zeta Potential of Vicilin, Legumin, and Commercial Pea Protein Isolate

Helmick, H. D. B., Hartanto, C., Bhunia, A. K., Liceaga, A. M., & Kokini, J. L. (2021) · Food Biophysics · doi:10.1007/s11483-021-09686-8 · Cited by 36

Photometric redshifts from SDSS images with an interpretable deep capsule network

Dey, B., Andrews, B. H., Newman, J. A., Mao, Y.-Y., Rau, M. M., & Zhou, R. (2022) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stac2105 · Cited by 43

Revolutionizing biochar synthesis for enhanced heavy metal adsorption: Harnessing machine learning and Bayesian optimization

Yang, H., Liu, X., Liu, Y., Cui, J., & Xiao, Y. (2023) · Journal of environmental chemical engineering · doi:10.1016/j.jece.2023.110593 · Cited by 40

Spateo: multidimensional spatiotemporal modeling of single-cell spatial transcriptomicsPreprint

Qiu, X., Zhu, D. Y., Yao, J., Jing, Z., Zuo, L., Wang, M., Min, K. H., Pan, H., Wang, S., Liao, S., et al. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.12.07.519417 · Cited by 37

A workflow for segmenting soil and plant X-ray computed tomography images with deep learning in Google’s Colaboratory

Rippner, D. A., Raja, P. V., Earles, J. M., Momayyezi, M., Buchko, A. C., Duong, F. V., Forrestel, E. J., Parkinson, D. Y., Shackel, K. A., Neyhart, J., et al. (2022) · Frontiers in Plant Science · doi:10.3389/fpls.2022.893140 · Cited by 36

A role for the centrosome in regulating the rate of neuronal efferocytosis by microglia in vivo

Möller, K., Brambach, M., Villani, A., Gallo, E., Gilmour, D. T., & Peri, F. (2022) · eLife · doi:10.7554/elife.82094 · Cited by 34

Effect of channel density, inverse solutions and connectivity measures on EEG resting-state networks reconstruction: A simulation study

Allouch, S., Kabbara, A., Duprez, J., Khalil, M. A., Modolo, J., & Hassan, M. (2023) · NeuroImage · doi:10.1016/j.neuroimage.2023.120006 · Cited by 39

Multicentric pilot study to standardize clinical whole exome sequencing (WES) for cancer patients

Menzel, M., Ossowski, S., Kral, S., Metzger, P., Horak, P., Marienfeld, R. B., Boerries, M., Wolter, S., Ball, M., Neumann, O., et al. (2023) · npj Precision Oncology · doi:10.1038/s41698-023-00457-x · Cited by 36

Insights from super-metal-rich stars: Is the Milky Way bar young?

Nepal, S., Chiappini, C., Guiglion, G., Steinmetz, M., Pérez-Villegas, Á., Queiroz, A. B. A., Miglio, A., Dohme, P., & Khalatyan, A. (2023) · Astronomy and Astrophysics · doi:10.1051/0004-6361/202348365 · Cited by 35

Transcriptomic-Based Microenvironment Classification Reveals Precision Medicine Strategies for Pancreatic Ductal Adenocarcinoma

George, B., Kudryashova, O. M., Kravets, A. D., Thalji, S. Z., Malarkannan, S., Kurzrock, R., Chernyavskaya, E. F., Gusakova, M., Kravchenko, D., Tychinin, D. I., et al. (2024) · Gastroenterology · doi:10.1053/j.gastro.2024.01.028 · Cited by 35

Eye movements track prioritized auditory features in selective attention to natural speech

Gehmacher, Q., Schubert, J., Schmidt, F., Hartmann, T., Reisinger, P., Rösch, S., Schwarz, K., Popov, T. G., Maria Chait, A., & Weisz, N. (2024) · Nature Communications · doi:10.1038/s41467-024-48126-2 · Cited by 34

The role of data partitioning on the performance of EEG-based deep learning models in supervised cross-subject analysis: A preliminary study

Pup, F. D., Zanola, A., Tshimanga, L. F., Bertoldo, A., Finos, L., & Atzori, M. (2025) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2025.110608 · Cited by 37

Large-scale predictions of alternative protein conformations by AlphaFold2-based sequence association

Lee, M., Schafer, J. W., Prabakaran, J., Chakravarty, D., Clore, M. F., & Porter, L. L. (2025) · Nature Communications · doi:10.1038/s41467-025-60759-5 · Cited by 38

Validating quantitative PCR assays for cfDNA detection without DNA extraction in exercising SLE patients

Neuberger, E. W. I., Brahmer, A., Ehlert, T., Kluge, K., Philippi, K. F. A., Boedecker-Lips, S. C., Weinmann‐Menke, J., & Simon, P. D. (2021) · Scientific Reports · doi:10.1038/s41598-021-92826-4 · Cited by 37

$\scriptsize\mathrm{{SQuIGG\overrightarrow{L}E}}$: Studying Quenching in Intermediate-z Galaxies—Gas, $\scriptsize\mathrm{{Angu\overrightarrow{L}ar}}$ Momentum, and Evolution

Suess, K. A., Kriek, M., Bezanson, R., Greene, J. E., Setton, D. J., Spilker, J. S., Feldmann, R., Goulding, A. D., Johnson, B. D., Leja, J., et al. (2022) · Research at the University of Copenhagen (University of Copenhagen) · doi:10.3847/1538-4357/ac404a · Cited by 35

Ground-State Properties of Metallic Solids from Ab Initio Coupled-Cluster Theory

Neufeld, V. A., Ye, H., & Berkelbach, T. C. (2022) · The Journal of Physical Chemistry Letters · doi:10.1021/acs.jpclett.2c01828 · Cited by 35

Insights on the mutational landscape of the SARS-CoV-2 Omicron variantPreprint

Miller, N. L., Clark, T., Raman, R., & Sasisekharan, R. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.12.06.471499 · Cited by 35

Autodifferentiable Spectrum Model for High-dispersion Characterization of Exoplanets and Brown Dwarfs

Kawahara, H., Kawashima, Y., Masuda, K., Crossfield, I. J. M., Pannier, E., & van den Bekerom, D. (2022) · The Astrophysical Journal Supplement Series · doi:10.3847/1538-4365/ac3b4d · Cited by 34

Retention, Fasting Patterns, and Weight Loss With an Intermittent Fasting App: Large-Scale, 52-Week Observational Study

Torres, L., Lee, J. L.-Y., Park, S., Lorenzo, R. C. D., Branam, J. P., Fraser, S. A., & Salisbury, B. A. (2022) · JMIR mhealth and uhealth · doi:10.2196/35896 · Cited by 38

Large-scale dynamics moderate impact-relevant changes to organised convective storms

Chan, S. C., Kendon, E., Fowler, H. J., Kahraman, A., Crook, J. A., Ban, N., & Prein, A. F. (2023) · Communications Earth & Environment · doi:10.1038/s43247-022-00669-2 · Cited by 37

Do persuasive designs make smartphones more addictive? - A mixed-methods study on Chinese university students

Chen, X., Hedman, A., Distler, V., & Koenig, V. (2023) · Computers in Human Behavior Reports · doi:10.1016/j.chbr.2023.100299 · Cited by 56

Hydrogel-encapsulation to enhance bacterial diagnosis of colon inflammation

Aghlara-Fotovat, S., Musteata, E., Doerfert, M., Baruch, M., Levitan, M., Tabor, J. J., & Omid Veiseh, A. (2023) · Biomaterials · doi:10.1016/j.biomaterials.2023.122246 · Cited by 35

A cross-cohort computational framework to trace tumor tissue-of-origin based on RNA sequencing

He, B., Sun, H., Bao, M., Li, H., He, J., Tian, G., & Wang, B. (2023) · Scientific Reports · doi:10.1038/s41598-023-42465-8 · Cited by 35

Deep-learning image enhancement and fibre segmentation from time-resolved computed tomography of fibre-reinforced composites

Guo, R. J., Stubbe, J. E., Zhang, Y., Schlepütz, C. M., Gomez, C. R., Mehdikhani, M., Breite, C., Swolfs, Y., & Villanueva‐Perez, P. (2023) · Composites Science and Technology · doi:10.1016/j.compscitech.2023.110278 · Cited by 34

Exploring the impact of microlensing on gravitational wave signals: Biases, population characteristics, and prospects for detection

Mishra, A., Meena, A. K., More, A., & Bose, S. (2024) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stae836 · Cited by 34

Spatial transcriptomics reveals expression gradients in developing wheat inflorescences at cellular resolution

Long, K., Lister, A., Jones, M. R. W., Adamski, N. M., Ellis, R. E., Chedid, C., Carpenter, S. J., Liu, X., Backhaus, A. E., Goldson, A. J., et al. (2025) · The Plant Cell · doi:10.1093/plcell/koaf282 · Cited by 37

Establishing a consensus for the hallmarks of cancer based on gene ontology and pathway annotations

Chen, Y., Verbeek, F. J., & Wolstencroft, K. (2021) · BMC Bioinformatics · doi:10.1186/s12859-021-04105-8 · Cited by 37

Evidence for sub-Chandrasekhar Type Ia supernovae from the last major merger

Sanders, J. L., Belokurov, V. A., & Man, K. T. F. (2021) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stab1951 · Cited by 34

Biosensors based on peptide exposure show single molecule conformations in live cells

Liu, B., Stone, O. J., Pablo, M., Herron, J. C., Nogueira, A. T., Dağliyan, O., Grimm, J. B., Lavis, L. D., Elston, T. C., & Hahn, K. M. (2021) · Cell · doi:10.1016/j.cell.2021.09.026 · Cited by 35

FcER1: A Novel Molecule Implicated in the Progression of Human Diabetic Kidney Disease

Sur, S., Nguyen, M., Boada, P., Sigdel, T. K., Sollinger, H. W., & Sarwal, M. (2021) · Frontiers in Immunology · doi:10.3389/fimmu.2021.769972 · Cited by 34

The consequences of differential origin licensing dynamics in distinct chromatin environments

Liu, M., Kedziora, K. M., Song, E. C., Purvis, J. E., & Cook, J. G. (2022) · Nucleic Acids Research · doi:10.1093/nar/gkac003 · Cited by 35

Grapevine DMR6-1 Is a Candidate Gene for Susceptibility to Downy Mildew

Pirrello, C., Malacarne, G., Moretto, M., Lenzi, L., Perazzolli, M., Zeilmaker, T., den Ackerveken, G. F. J. M. V., Pilati, S., Moser, C., & Giacomelli, L. (2022) · Biomolecules · doi:10.3390/biom12020182 · Cited by 34

A large-scale investigation of white matter microstructural associations with reading ability

Meisler, S. L., & Gabrieli, J. D. E. (2022) · NeuroImage · doi:10.1016/j.neuroimage.2022.118909 · Cited by 38

BNT162b2 vaccine induces antibody release in saliva: a possible role for mucosal viral protection?

Darwich, A., Pozzi, C., Fornasa, G., Lizier, M., Azzolini, E., Spadoni, I., Carli, F., Voza, A., Desai, A., Ferrero, C. A., et al. (2022) · EMBO Molecular Medicine · doi:10.15252/emmm.202115326 · Cited by 34

VAC-CNN: A Visual Analytics System for Comparative Studies of Deep Convolutional Neural Networks

Xuan, X., Zhang, X., Kwon, O.-H., & Ma, K. (2022) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2022.3165347 · Cited by 36

Ranking reprogramming factors for cell differentiation

Hammelman, J., Patel, T., Closser, M., Wichterle, H., & Gifford, D. K. (2022) · Nature Methods · doi:10.1038/s41592-022-01522-2 · Cited by 34

Prognosis of Alzheimer’s Disease Using Quantitative Mass Spectrometry of Human Blood Plasma Proteins and Machine Learning

Kononikhin, A. S., Zakharova, N. V., Semenov, S. D., Bugrova, A. E., Brzhozovskiy, A. G., Indeykina, M. I., Fedorova, Y. B., Колыхалов, И. В., Strelnikova, P. A., Ikonnikova, A. Y., et al. (2022) · International Journal of Molecular Sciences · doi:10.3390/ijms23147907 · Cited by 33

A high quality, industrial data set for binding affinity prediction: performance comparison in different early drug discovery scenarios

Tosstorff, A., Rudolph, M. G., Cole, J. C., Reutlinger, M., Krämer, C., Schaffhauser, H., Nilly, A., Flohr, A., & Kuhn, B. (2022) · Journal of Computer-Aided Molecular Design · doi:10.1007/s10822-022-00478-x · Cited by 37

Plagued by a cryptic clock: insight and issues from the global phylogeny of Yersinia pestis

Eaton, K., Featherstone, L. A., Duchêne, S., Carmichael, A. G., Varlık, N., Golding, G. B., Holmes, E. C., & Poinar, H. N. (2023) · Communications Biology · doi:10.1038/s42003-022-04394-6 · Cited by 34

A Mental Health and Well-Being Chatbot: User Event Log Analysis

Booth, F. G., Potts, C., Bond, R. R., Mulvenna, M., Kostenius, C., Dhanapala, I., Vakaloudis, A., Cahill, B., Kuosmanen, L., & Ennis, E. (2022) · JMIR mHealth and uHealth · doi:10.2196/43052 · Cited by 29

Identification of a covert evolutionary pathway between two protein folds

Chakravarty, D., Sreenivasan, S., Swint-Kruse, L., & Porter, L. L. (2023) · Nature Communications · doi:10.1038/s41467-023-38519-0 · Cited by 29

Near-Surface NO2 Concentration Estimation by Random Forest Modeling and Sentinel-5P and Ancillary Data

Li, M., Wu, Y., Bao, Y., Liu, B., & Petropoulos, G. P. (2022) · Remote Sensing · doi:10.3390/rs14153612 · Cited by 33

Protein prediction models support widespread post-transcriptional regulation of protein abundance by interacting partners

Srivastava, H., Lippincott, M. J., Currie, J., Canfield, R., Lam, M. P. Y., & Lau, E. (2022) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1010702 · Cited by 36

Structural interaction fingerprints and machine learning for predicting and explaining binding of small molecule ligands to RNA

Szulc, N. A., Mackiewicz, Z., Bujnicki, J. M., & Stefaniak, F. (2023) · Briefings in Bioinformatics · doi:10.1093/bib/bbad187 · Cited by 34

A computationally efficient modeling of flow in complex porous media by coupling multiscale digital rock physics and deep learning: Improving the tradeoff between resolution and field-of-view

Nabipour, I., Raoof, A., Cnudde, V., Aghaei, H., & Qajar, J. (2024) · Advances in Water Resources · doi:10.1016/j.advwatres.2024.104695 · Cited by 33

Deep learning is widely applicable to phenotyping embryonic development and disease

Naert, T., Çiçek, Ö., Ogar, P., Bürgi, M., Shaidani, N., Kaminski, M. M., Xu, Y., Grand, K., Vujanovic, M., Prata, D., et al. (2021) · Development · doi:10.1242/dev.199664 · Cited by 31

Evolution of Hierarchically Porous Nickel Alumina Catalysts Studied by X‐Ray Ptychography

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