Simulation physiologischer Regelkreise mit der objektorientierten Modellbibliothek “HumanLib”

Systems analysis of arterial pressure regulation and hypertension

Guyton, A. C., Coleman, T. G., Cowley, A. W., Liard, J.-F., Norman, R. A., & Manning, R. D. (1972) · Annals of Biomedical Engineering · doi:10.1007/bf02584211 · Cited by 122

Evolutionary-scale prediction of atomic level protein structure with a language model

Lin, Z., Akin, H., Rao, R., Hie, B., Zhu, Z., Lu, W., Smetanin, N., Verkuil, R., Kabeli, O., Shmueli, Y., et al. (2022) · doi:10.1101/2022.07.20.500902 · Cited by 5,990

Ranking the effectiveness of worldwide COVID-19 government interventions

Haug, N., Geyrhofer, L., Londei, A., Dervic, E., Desvars-Larrive, A., Loreto, V., Pinior, B., Thurner, S., & Klimek, P. (2020) · Nature Human Behaviour · doi:10.1038/s41562-020-01009-0 · Cited by 1,412

Nucleotide Transformer: building and evaluating robust foundation models for human genomics

Dalla-Torre, H., Gonzalez, L., Mendoza-Revilla, J., Lopez Carranza, N., Grzywaczewski, A. H., Oteri, F., Dallago, C., Trop, E., de Almeida, B. P., Sirelkhatim, H., et al. (2025) · Nature Methods · doi:10.1038/s41592-024-02523-z · Cited by 772

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

Transformer protein language models are unsupervised structure learners

Rao, R., Meier, J., Sercu, T., Ovchinnikov, S., & Rives, A. (2020) · doi:10.1101/2020.12.15.422761 · Cited by 341

Anipose: A toolkit for robust markerless 3D pose estimation

Karashchuk, P., Rupp, K. L., Dickinson, E. S., Walling-Bell, S., Sanders, E., Azim, E., Brunton, B. W., & Tuthill, J. C. (2021) · Cell Reports · doi:10.1016/j.celrep.2021.109730 · Cited by 324

Combining structured and unstructured data for predictive models: a deep learning approach

Zhang, D., Yin, C., Zeng, J., Yuan, X., & Zhang, P. (2020) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-020-01297-6 · Cited by 269

Analytic Pearson residuals for normalization of single-cell RNA-seq UMI data

Lause, J., Berens, P., & Kobak, D. (2021) · Genome Biology · doi:10.1186/s13059-021-02451-7 · Cited by 195

AlphaFold2 fails to predict protein fold switching

Chakravarty, D., & Porter, L. L. (2022) · Protein Science · doi:10.1002/pro.4353 · Cited by 193

Decoding gene regulation in the fly brain

Janssens, J., Aibar, S., Taskiran, I. I., Ismail, J. N., Spanier, K. I., Bravo González-Blas, C., Quan, X. J., Papasokrati, D., Hulselmans, G., Makhzami, S., et al. (2021) · doi:10.1101/2021.08.11.454937 · Cited by 170

Decoding gene regulation in the fly brain

Janssens, J., Aibar, S., Taskiran, I. I., Ismail, J. N., Gomez, A. E., Aughey, G., Spanier, K. I., De Rop, F. V., González-Blas, C. B., Dionne, M., et al. (2022) · Nature · doi:10.1038/s41586-021-04262-z · Cited by 170

TAPAS: An Open-Source Software Package for Translational Neuromodeling and Computational Psychiatry

Frässle, S., Aponte, E. A., Bollmann, S., Brodersen, K. H., Do, C. T., Harrison, O. K., Harrison, S. J., Heinzle, J., Iglesias, S., Kasper, L., et al. (2021) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2021.680811 · Cited by 169

What can 1.8 billion regressions tell us about the pressures shaping high-level visual representation in brains and machines?

Conwell, C., Prince, J. S., Kay, K. N., Alvarez, G. A., & Konkle, T. (2022) · doi:10.1101/2022.03.28.485868 · Cited by 160

DNA language models are powerful predictors of genome-wide variant effects

Benegas, G., Batra, S. S., & Song, Y. S. (2023) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2311219120 · Cited by 157

Personal transcriptome variation is poorly explained by current genomic deep learning models

Huang, C., Shuai, R. W., Baokar, P., Chung, R., Rastogi, R., Kathail, P., & Ioannidis, N. M. (2023) · Nature Genetics · doi:10.1038/s41588-023-01574-w · Cited by 127

Transparent medical image AI via an image–text foundation model grounded in medical literature

Kim, C., Gadgil, S. U., DeGrave, A. J., Omiye, J. A., Cai, Z. R., Daneshjou, R., & Lee, S.-I. (2024) · Nature Medicine · doi:10.1038/s41591-024-02887-x · Cited by 125

Hippocampo-cortical circuits for selective memory encoding, routing, and replay

Harvey, R. E., Robinson, H. L., Liu, C., Oliva, A., & Fernandez-Ruiz, A. (2023) · Neuron · doi:10.1016/j.neuron.2023.04.015 · Cited by 101

The landscape of biomedical research

González-Márquez, R., Schmidt, L., Schmidt, B. M., Berens, P., & Kobak, D. (2024) · Patterns · doi:10.1016/j.patter.2024.100968 · Cited by 89

Race, Sex, and Age Disparities in the Performance of ECG Deep Learning Models Predicting Heart Failure

Kaur, D., Hughes, J. W., Rogers, A. J., Kang, G., Narayan, S. M., Ashley, E. A., & Perez, M. V. (2024) · Circulation: Heart Failure · doi:10.1161/circheartfailure.123.010879 · Cited by 58

Evolving the olfactory system with machine learning

Wang, P. Y., Sun, Y., Axel, R., Abbott, L. F., & Yang, G. R. (2021) · Neuron · doi:10.1016/j.neuron.2021.09.010 · Cited by 54

Yield Prediction Through Integration of Genetic, Environment, and Management Data Through Deep Learning

Kick, D. R., Wallace, J. G., Schnable, J. C., Kolkman, J. M., Alaca, B., Beissinger, T. M., Edwards, J., Ertl, D., Flint-Garcia, S., Gage, J. L., et al. (2023) · G3: Genes|Genomes|Genetics · doi:10.1093/g3journal/jkad006 · Cited by 53

In situ single particle classification reveals distinct 60S maturation intermediates in cells

Lucas, B. A., Zhang, K., Loerch, S., & Grigorieff, N. (2022) · eLife · doi:10.7554/elife.79272 · Cited by 53

Descending neuron population dynamics during odor-evoked and spontaneous limb-dependent behaviors

Aymanns, F., Chen, C.-L., & Ramdya, P. (2022) · eLife · doi:10.7554/elife.81527 · Cited by 43

Accurate prediction of protein assembly structure by combining AlphaFold and symmetrical docking

Jeppesen, M., & André, I. (2023) · Nature Communications · doi:10.1038/s41467-023-43681-6 · Cited by 44

Fusion of electronic health records and radiographic images for a multimodal deep learning prediction model of atypical femur fractures

Schilcher, J., Nilsson, A., Andlid, O., & Eklund, A. (2024) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2023.107704 · Cited by 41

Data consistency in the English Hospital Episodes Statistics database

Hardy, F., Heyl, J., Tucker, K., Hopper, A., Marchã, M. J., Briggs, T. W. R., Yates, J., Day, J., Wheeler, A., Eve-Jones, S., et al. (2022) · BMJ Health & Care Informatics · doi:10.1136/bmjhci-2022-100633 · Cited by 38

Epistasis between promoter activity and coding mutations shapes gene evolvability

Cisneros, A. F., Gagnon-Arsenault, I., Dubé, A. K., Després, P. C., Kumar, P., Lafontaine, K., Pelletier, J. N., & Landry, C. R. (2023) · Science Advances · doi:10.1126/sciadv.add9109 · Cited by 33

Natural statistics support a rational account of confidence biases

Webb, T. W., Miyoshi, K., So, T. Y., Rajananda, S., & Lau, H. (2023) · Nature Communications · doi:10.1038/s41467-023-39737-2 · Cited by 31

Connectome-constrained deep mechanistic networks predict neural responses across the fly visual system at single-neuron resolution

Lappalainen, J. K., Tschopp, F. D., Prakhya, S., McGill, M., Nern, A., Shinomiya, K., Takemura, S.-Y., Gruntman, E., Macke, J. H., & Turaga, S. C. (2023) · doi:10.1101/2023.03.11.532232 · Cited by 31

EnGens: a computational framework for generation and analysis of representative protein conformational ensembles

Conev, A., Rigo, M. M., Devaurs, D., Fonseca, A. F., Kalavadwala, H., de Freitas, M. V., Clementi, C., Zanatta, G., Antunes, D. A., & Kavraki, L. E. (2023) · Briefings in Bioinformatics · doi:10.1093/bib/bbad242 · Cited by 28

Improved computational epitope profiling using structural models identifies a broader diversity of antibodies that bind to the same epitope

Spoendlin, F. C., Abanades, B., Raybould, M. I. J., Wong, W. K., Georges, G., & Deane, C. M. (2023) · Frontiers in Molecular Biosciences · doi:10.3389/fmolb.2023.1237621 · Cited by 26

Elucidation of genome-wide understudied proteins targeted by PROTAC-induced degradation using interpretable machine learning

Xie, L., & Xie, L. (2023) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1010974 · Cited by 26

Toward Predicting 30-Day Readmission Among Oncology Patients: Identifying Timely and Actionable Risk Factors

Hwang, S., Urbanowicz, R., Lynch, S., Vernon, T., Bresz, K., Giraldo, C., Kennedy, E., Leabhart, M., Bleacher, T., Ripchinski, M. R., et al. (2023) · JCO Clinical Cancer Informatics · doi:10.1200/cci.22.00097 · Cited by 25

Large-Scale Discovery of Microbial Fibrillar Adhesins and Identification of Novel Members of Adhesive Domain Families

(2022) · doi:10.1128/jb.00107-22 · Cited by 23

Comparative modeling reveals the molecular determinants of aneuploidy fitness cost in a wild yeast model

Rojas, J., Hose, J., Dutcher, H. A., Place, M., Wolters, J. F., Hittinger, C. T., & Gasch, A. P. (2024) · Cell Genomics · doi:10.1016/j.xgen.2024.100656 · Cited by 22

Harmonizing and aligning M/EEG datasets with covariance-based techniques to enhance predictive regression modeling

Mellot, A., Collas, A., Rodrigues, P. L. C., Engemann, D., & Gramfort, A. (2023) · Imaging Neuroscience · doi:10.1162/imag_a_00040 · Cited by 16

Longevity and rejuvenation effects of cell reprogramming are decoupled from loss of somatic identity.

Kriukov, D., Khrameeva, E. E., Gladyshev, V. N., Dmitriev, S. E., & Tyshkovskiy, A. (2022) · doi:10.1101/2022.12.12.520058 · Cited by 13

Age-associated DNA methylation changes in Xenopus frogs

Morselli, M., Bennett, R., Shaidani, N.-I., Horb, M., Peshkin, L., & Pellegrini, M. (2023) · Epigenetics · doi:10.1080/15592294.2023.2201517 · Cited by 10

Conformational and oligomeric states of SPOP from small-angle X-ray scattering and molecular dynamics simulations

Thomasen, F. E., Cuneo, M. J., Mittag, T., & Lindorff-Larsen, K. (2023) · eLife · doi:10.7554/elife.84147 · Cited by 9

Inferring kinetic parameters of oscillatory gene regulation from single cell time-series data

Burton, J., Manning, C. S., Rattray, M., Papalopulu, N., & Kursawe, J. (2021) · Journal of The Royal Society Interface · doi:10.1098/rsif.2021.0393 · Cited by 9

Domain Expansion and Functional Diversification in Vertebrate Reproductive Proteins

Rivera, A. M., Wilburn, D. B., & Swanson, W. J. (2022) · Molecular Biology and Evolution · doi:10.1093/molbev/msac105 · Cited by 6

An accessible infrastructure for artificial intelligence using a Docker-based JupyterLab in Galaxy

Kumar, A., Cuccuru, G., Grüning, B., & Backofen, R. (2022) · GigaScience · doi:10.1093/gigascience/giad028 · Cited by 6

Guiding discovery of protein sequence-structure-function modeling

(2024) · doi:10.1093/bioinformatics/btae002 · Cited by 4

Improved antibody-specific epitope prediction using AlphaFold and AbAdapt

Xu, Z., Davila, A., Wiamowski, J., Teraguchi, S., & Standley, D. M. (2022) · doi:10.1101/2022.05.21.492907 · Cited by 4

Searching for Structure: Characterizing the Protein Conformational Landscape with Clustering-Based Algorithms

Macke, A. C., Stump, J. E., Kelly, M. S., Rowley, J., Herath, V., Mullen, S., & Dima, R. I. (2024) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.3c01511 · Cited by 4

Predicting Affinity Through Homology (PATH): Interpretable binding affinity prediction with persistent homology

(2025) · doi:10.1371/journal.pcbi.1013216 · Cited by 4

HLA3DB: comprehensive annotation of peptide/HLA complexes enables blind structure prediction of T cell epitopes

Gupta, S., Nerli, S., Kandy, S. K., Mersky, G. L., & Sgourakis, N. G. (2023) · doi:10.1101/2023.03.20.533510 · Cited by 2

Modeling the Orthosteric Binding Site of the G Protein-Coupled Odorant Receptor OR5K1

Nicoli, A., Haag, F., Marcinek, P., He, R., Kreißl, J., Stein, J., Marchetto, A., Dunkel, A., Hofmann, T., Krautwurst, D., et al. (2022) · doi:10.1101/2022.06.01.494157 · Cited by 2

CysPresso: A classification model utilizing deep learning protein representations to predict recombinant expression of cysteine-dense peptides

Ouellet, S., Ferguson, L., Lau, A. Z., & Lim, T. K. Y. (2022) · doi:10.1101/2022.09.17.508377

Dissecting the Determinants of Domain Insertion Tolerance and Allostery in Proteins

Mathony, J., Aschenbrenner, S., Becker, P., & Niopek, D. (2023) · doi:10.1101/2023.04.11.536407

Disulfide bridge-dependent dimerization triggers FGF2 membrane translocation into the extracellular space

Lolicato, F., Steringer, J., Saleppico, R., Beyer, D., Fernandez-Sobaberas, J., Unger, S., Klein, S., Riegerová, P., Wegehingel, S., Müller, H., et al. (2023) · doi:10.1101/2023.04.12.536539