Statsmodels: Econometric and Statistical Modeling with Python

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

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. (2019) · Apollo - University of Cambridge Repository · doi:10.17863/cam.64134 · Cited by 10,460

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

Impacts of the COVID-19 pandemic on life of higher education students: A global perspective

Aristovnik, A., Keržič, D., Ravšelj, D., Tomaževič, N., & Umek, L. (2020) · Sustainability · doi:10.3390/su12208438 · Cited by 2,047

Molecular graph convolutions: moving beyond fingerprints

Kearnes, S., McCloskey, K., Berndl, M., Pande, V., & Riley, P. (2016) · Journal of Computer-Aided Molecular Design · doi:10.1007/s10822-016-9938-8 · Cited by 1,631

Evaluation metrics and statistical tests for machine learning

Rainio, O., Teuho, J., & Klén, R. (2024) · Scientific Reports · doi:10.1038/s41598-024-56706-x · Cited by 1,338

A universal graph deep learning interatomic potential for the periodic table

Chen, C., & Ong, S. P. (2022) · Nature Computational Science · doi:10.1038/s43588-022-00349-3 · Cited by 1,140

Consolidation alters motor sequence-specific distributed representations

Pinsard, B., Boutin, A., Gabitov, E., Lungu, O., Benali, H., & Doyon, J. (2019) · eLife · doi:10.7554/elife.39324 · Cited by 1,066

Efficient integration of heterogeneous single-cell transcriptomes using Scanorama

Hie, B., Bryson, B., & Berger, B. (2019) · Nature Biotechnology · doi:10.1038/s41587-019-0113-3 · Cited by 974

Host lifestyle affects human microbiota on daily timescales

David, L. A., Materna, A. C., Friedman, J., Campos-Baptista, M. I., Blackburn, M. C., Perrotta, A., Erdman, S. E., & Alm, E. J. (2014) · Genome biology · doi:10.1186/gb-2014-15-7-r89 · Cited by 950

Simulating 500 million years of evolution with a language model

Hayes, T., Rao, R., Akin, H., Sofroniew, N. J., Oktay, D., Lin, Z., Verkuil, R., Tran, V. Q., Deaton, J., Wiggert, M., et al. (2025) · Science · doi:10.1126/science.ads0018 · Cited by 937

Organ aging signatures in the plasma proteome track health and disease

Oh, H. S.-H., Rutledge, J., Nachun, D., Pálovics, R., Abiose, O., Moran-Losada, P., Channappa, D., Urey, D. Y., Kim, K., Sung, Y. J., et al. (2023) · Nature · doi:10.1038/s41586-023-06802-1 · Cited by 634

BOHB: Robust and Efficient Hyperparameter Optimization at Scale

Falkner, S., Klein, A., & Hutter, F. (2018) · arXiv · doi:10.48550/arxiv.1807.01774 · Cited by 551

A high-performance neuroprosthesis for speech decoding and avatar control

Metzger, S. L., Littlejohn, K. T., Silva, A. B., Moses, D. A., Seaton, M. P., Wang, R., Dougherty, M. E., Liu, J. R., Wu, P., Berger, M. A., et al. (2023) · Nature · doi:10.1038/s41586-023-06443-4 · Cited by 522

Pan-cancer analyses reveal cancer-type-specific fungal ecologies and bacteriome interactions

Narunsky-Haziza, L., Sepich-Poore, G. D., Livyatan, I., Asraf, O., Martino, C., Nejman, D., Gavert, N., Stajich, J. E., Amit, G., González, A., et al. (2022) · Cell · doi:10.1016/j.cell.2022.09.005 · Cited by 538

Integrating spatial gene expression and breast tumour morphology via deep learning

He, B., Bergenstråhle, L., Stenbeck, L., Abid, A., Andersson, A., Borg, Å., Maaskola, J., Lundeberg, J., & Zou, J. (2020) · Nature Biomedical Engineering · doi:10.1038/s41551-020-0578-x · Cited by 507

Dynamic CD8+ T cell responses to cancer immunotherapy in human regional lymph nodes are disrupted in metastatic lymph nodes

Rahim, M. K., Okholm, T. L. H., Jones, K. B., McCarthy, E. E., Liu, C. C., Yee, J. L., Tamaki, S. J., Marquez, D. M., Tenvooren, I., Wai, K., et al. (2023) · Cell · doi:10.1016/j.cell.2023.02.021 · Cited by 455

Quantifying the local adaptive landscape of a nascent bacterial community

Ascensao, J. A., Wetmore, K. M., Good, B. H., Arkin, A. P., & Hallatschek, O. (2023) · Nature Communications · doi:10.1038/s41467-022-35677-5 · Cited by 447

PyDESeq2: A Python Package for Bulk RNA-Seq Differential Expression Analysis

Muzellec, B., Teleńczuk, M., Cabeli, V., & Andreux, M. (2023) · Bioinformatics · doi:10.1093/bioinformatics/btad547 · Cited by 446

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

FactSheets: Increasing trust in AI services through supplier's declarations of conformity

Arnold, M., Bellamy, R. K. E., Hind, M., Houde, S., Mehta, S., Mojsilović, A., Nair, R., Ramamurthy, K. N., Olteanu, A., Piorkowski, D., et al. (2019) · IBM Journal of Research and Development · doi:10.1147/jrd.2019.2942288 · Cited by 434

A wellness study of 108 individuals using personal, dense, dynamic data clouds

Price, N. D., Magis, A. T., Earls, J. C., Glusman, G., Levy, R., Lausted, C., McDonald, D. T., Kusebauch, U., Moss, C. L., Zhou, Y., et al. (2017) · Nature Biotechnology · doi:10.1038/nbt.3870 · Cited by 427

Can AI Help Reduce Disparities in General Medical and Mental Health Care?

(2019) · AMA Journal of Ethics · doi:10.1001/amajethics.2019.167 · Cited by 401

Emergent analogical reasoning in large language models

Webb, T., Holyoak, K. J., & Lu, H. (2023) · Nature Human Behaviour · doi:10.1038/s41562-023-01659-w · Cited by 404

Prospective, multi-site study of patient outcomes after implementation of the TREWS machine learning-based early warning system for sepsis

Adams, R., Henry, K. E., Sridharan, A., Soleimani, H., Zhan, A., Rawat, N., Johnson, L., Hager, D. N., Cosgrove, S. E., Markowski, A., et al. (2022) · Nature Medicine · doi:10.1038/s41591-022-01894-0 · Cited by 365

Genome-wide gene expression tuning reveals diverse vulnerabilities of M. tuberculosis

Bosch, B., DeJesus, M. A., Poulton, N. C., Zhang, W., Engelhart, C. A., Zaveri, A., Lavalette, S., Ruecker, N., Trujillo, C., Wallach, J. B., et al. (2021) · Cell · doi:10.1016/j.cell.2021.06.033 · Cited by 316

Multiple Deeply Divergent Denisovan Ancestries in Papuans

Jacobs, G. S., Hudjashov, G., Saag, L., Kusuma, P., Darusallam, C. C., Lawson, D. J., Mondal, M., Pagani, L., Ricaut, F.-X., Stoneking, M., et al. (2019) · Cell · doi:10.1016/j.cell.2019.02.035 · Cited by 346

Systematic Evaluation of Molecular Networks for Discovery of Disease Genes

Huang, J. K., Carlin, D. E., Yu, M. K., Zhang, W., Kreisberg, J. F., Tamayo, P., & Ideker, T. (2018) · Cell Systems · doi:10.1016/j.cels.2018.03.001 · Cited by 289

Disparities in rooftop photovoltaics deployment in the United States by race and ethnicity

Sunter, D. A., Castellanos, S., & Kammen, D. M. (2019) · Nature Sustainability · doi:10.1038/s41893-018-0204-z · Cited by 324

A Unified Game-Theoretic Approach to Multiagent Reinforcement Learning

Lanctot, M., Zambaldi, V., Gruslys, A., Lazaridou, A., Tuyls, K., Perolat, J., Silver, D., & Graepel, T. (2017) · arXiv · doi:10.48550/arxiv.1711.00832 · Cited by 295

Human neocortical expansion involves glutamatergic neuron diversification

Berg, J., Sorensen, S. A., Ting, J. T., Miller, J. A., Chartrand, T., Buchin, A., Bakken, T. E., Budzillo, A., Dee, N., Ding, S.-L., et al. (2021) · Nature · doi:10.1038/s41586-021-03813-8 · Cited by 285

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

Network-based machine learning approach to predict immunotherapy response in cancer patients

Kong, J., Ha, D., Lee, J., Kim, I., Park, M., Im, S.-H., Shin, K., & Kim, S. (2022) · Nature Communications · doi:10.1038/s41467-022-31535-6 · Cited by 219

Federated learning for predicting histological response to neoadjuvant chemotherapy in triple-negative breast cancer

Ogier du Terrail, J., Leopold, A., Joly, C., Béguier, C., Andreux, M., Maussion, C., Schmauch, B., Tramel, E. W., Bendjebbar, E., Zaslavskiy, M., et al. (2023) · Nature Medicine · doi:10.1038/s41591-022-02155-w · Cited by 217

Reconstruction of neocortex: Organelles, compartments, cells, circuits, and activity

Turner, N. L., Macrina, T., Bae, J. A., Yang, R., Wilson, A. M., Schneider-Mizell, C., Lee, K., Lu, R., Wu, J., Bodor, A. L., et al. (2022) · Cell · doi:10.1016/j.cell.2022.01.023 · Cited by 206

A general model to predict small molecule substrates of enzymes based on machine and deep learning

Kroll, A., Ranjan, S., Engqvist, M. K. M., & Lercher, M. J. (2023) · Nature Communications · doi:10.1038/s41467-023-38347-2 · 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

Metagenome-assembled genome binning methods with short reads disproportionately fail for plasmids and genomic islands

Maguire, F., Jia, B., Gray, K. L., Lau, W. Y. V., Beiko, R. G., & Brinkman, F. S. L. (2020) · Microbial Genomics · doi:10.1099/mgen.0.000436 · Cited by 152

Exploring the Early Organization and Maturation of Linguistic Pathways in the Human Infant Brain

Dubois, J., Poupon, C., Thirion, B., Simonnet, H., Kulikova, S., Leroy, F., Hertz-Pannier, L., & Dehaene-Lambertz, G. (2016) · Cerebral Cortex · doi:10.1093/cercor/bhv082 · Cited by 165

Accuracy Assessment of Oura Ring Nocturnal Heart Rate and Heart Rate Variability in Comparison With Electrocardiography in Time and Frequency Domains: Comprehensive Analysis

Cao, R., Azimi, I., Sarhaddi, F., Niela-Vilen, H., Axelin, A., Liljeberg, P., & Rahmani, A. M. (2022) · Journal of Medical Internet Research · doi:10.2196/27487 · Cited by 159

Genomic diversity and ecology of human-associated Akkermansia species in the gut microbiome revealed by extensive metagenomic assembly

Karcher, N., Nigro, E., Punčochář, M., Blanco-Míguez, A., Ciciani, M., Manghi, P., Zolfo, M., Cumbo, F., Manara, S., Golzato, D., et al. (2021) · Genome Biology · doi:10.1186/s13059-021-02427-7 · Cited by 159

Humans primarily use model-based inference in the two-stage task

Feher da Silva, C., & Hare, T. A. (2020) · Nature Human Behaviour · doi:10.1038/s41562-020-0905-y · Cited by 153

Unexplored microbial diversity from 2,500 food metagenomes and links with the human microbiome.

Carlino, N., Blanco-Míguez, A., Punčochář, M., Mengoni, C., Pinto, F., Tatti, A., Manghi, P., Armanini, F., Avagliano, M., Barcenilla, C., et al. (2024) · Cell · doi:10.1016/j.cell.2024.07.039 · Cited by 146

Isolating the AFFF Signature in Coastal Watersheds Using Oxidizable PFAS Precursors and Unexplained Organofluorine

Ruyle, B. J., Pickard, H. M., LeBlanc, D. R., Tokranov, A. K., Thackray, C. P., Hu, X. C., Vecitis, C. D., & Sunderland, E. M. (2021) · Environmental Science & Technology · doi:10.1021/acs.est.0c07296 · Cited by 136

DLATK: Differential Language Analysis ToolKit

Schwartz, H. A., Giorgi, S., Sap, M., Crutchley, P., Ungar, L., & Eichstaedt, J. (2017) · Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing: System Demonstrations · doi:10.18653/v1/d17-2010 · Cited by 130

Current sequence-based models capture gene expression determinants in promoters but mostly ignore distal enhancers

Karollus, A., Mauermeier, T., & Gagneur, J. (2023) · Genome Biology · doi:10.1186/s13059-023-02899-9 · Cited by 128

Factors driving provider adoption of the TREWS machine learning-based early warning system and its effects on sepsis treatment timing

Henry, K. E., Adams, R., Parent, C., Soleimani, H., Sridharan, A., Johnson, L., Hager, D. N., Cosgrove, S. E., Markowski, A., Klein, E. Y., et al. (2022) · Nature Medicine · doi:10.1038/s41591-022-01895-z · Cited by 144

An Evaluation of the Human-Interpretability of Explanation

Lage, I., Chen, E., He, J., Narayanan, M., Kim, B., Gershman, S., & Doshi-Velez, F. (2019) · arXiv · doi:10.48550/arxiv.1902.00006 · Cited by 125

Microanatomical dissection of human intestinal T-cell immunity reveals site-specific changes in gut-associated lymphoid tissues over life

Senda, T., Dogra, P., Granot, T., Furuhashi, K., Snyder, M. E., Carpenter, D. J., Szabo, P. A., Thapa, P., Miron, M., & Farber, D. L. (2019) · Mucosal Immunology · doi:10.1038/s41385-018-0110-8 · Cited by 114

Assessing student perceptions and use of instructor versus AI ‐generated feedback

Er, E., Akçapınar, G., Bayazıt, A., Noroozi, O., & Banihashem, S. K. (2025) · British Journal of Educational Technology · doi:10.1111/bjet.13558 · Cited by 114

A toolbox for surfacing health equity harms and biases in large language models

Pfohl, S. R., Cole-Lewis, H., Sayres, R., Neal, D., Asiedu, M., Dieng, A., Tomasev, N., Rashid, Q. M., Azizi, S., Rostamzadeh, N., et al. (2024) · Nature Medicine · doi:10.1038/s41591-024-03258-2 · Cited by 113

Screening Human Embryos for Polygenic Traits Has Limited Utility

Karavani, E., Zuk, O., Zeevi, D., Barzilai, N., Stefanis, N. C., Hatzimanolis, A., Smyrnis, N., Avramopoulos, D., Kruglyak, L., Atzmon, G., et al. (2019) · Cell · doi:10.1016/j.cell.2019.10.033 · Cited by 111

PepNN: a deep attention model for the identification of peptide binding sites

Abdin, O., Nim, S., Wen, H., & Kim, P. M. (2022) · Communications Biology · doi:10.1038/s42003-022-03445-2 · Cited by 104

DLAB-deep learning methods for structure-based virtual screening of antibodies

Schneider, C., Buchanan, A., Taddese, B., & Deane, C. M. (2022) · Bioinformatics · doi:10.1093/bioinformatics/btab660 · Cited by 102

Deep language algorithms predict semantic comprehension from brain activity

Caucheteux, C., Gramfort, A., & King, J.-R. (2022) · Scientific Reports · doi:10.1038/s41598-022-20460-9 · Cited by 91

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

Effectiveness of molecular fingerprints for exploring the chemical space of natural products

Boldini, D., Ballabio, D., Consonni, V., Todeschini, R., Grisoni, F., & Sieber, S. A. (2024) · Journal of Cheminformatics · doi:10.1186/s13321-024-00830-3 · Cited by 97

Deep Neural Networks for Modeling Visual Perceptual Learning

Wenliang, L. K., & Seitz, A. R. (2018) · The Journal of Neuroscience · doi:10.1523/jneurosci.1620-17.2018 · Cited by 83

Paralog buffering contributes to the variable essentiality of genes in cancer cell lines

De Kegel, B., & Ryan, C. J. (2019) · PLOS Genetics · doi:10.1371/journal.pgen.1008466 · Cited by 92

Warning: Humans cannot reliably detect speech deepfakes

Mai, K. T., Bray, S., Davies, T., & Griffin, L. D. (2023) · PLOS ONE · doi:10.1371/journal.pone.0285333 · Cited by 98

Morphoelectric and transcriptomic divergence of the layer 1 interneuron repertoire in human versus mouse neocortex

Chartrand, T., Dalley, R., Close, J., Goriounova, N. A., Lee, B. R., Mann, R., Miller, J. A., Molnar, G., Mukora, A., Alfiler, L., et al. (2023) · Science · doi:10.1126/science.adf0805 · Cited by 88

Microbial hitchhikers harbouring antimicrobial-resistance genes in the riverine plastisphere

Zadjelovic, V., Wright, R. J., Borsetto, C., Quartey, J., Cairns, T. N., Langille, M. G. I., Wellington, E. M. H., & Christie-Oleza, J. A. (2023) · Microbiome · doi:10.1186/s40168-023-01662-3 · Cited by 87

Cross-modal autoencoder framework learns holistic representations of cardiovascular state

Radhakrishnan, A., Friedman, S. F., Khurshid, S., Ng, K., Batra, P., Lubitz, S. A., Philippakis, A. A., & Uhler, C. (2023) · Nature Communications · doi:10.1038/s41467-023-38125-0 · Cited by 82

Experimental reproducibility limits the correlation between mRNA and protein abundances in tumor proteomic profiles

Upadhya, S. R., & Ryan, C. J. (2022) · Cell Reports Methods · doi:10.1016/j.crmeth.2022.100288 · Cited by 81

A virtual rodent predicts the structure of neural activity across behaviours

Aldarondo, D., Merel, J., Marshall, J. D., Hasenclever, L., Klibaite, U., Gellis, A., Tassa, Y., Wayne, G., Botvinick, M., & Ölveczky, B. P. (2024) · Nature · doi:10.1038/s41586-024-07633-4 · Cited by 70

Addressing the use of generative AI in academic writing.

van Niekerk, J., Delport, P. M. J., & Sutherland, I. (2025) · Computers and Education: Artificial Intelligence · doi:10.1016/j.caeai.2024.100342 · Cited by 72

Large neutral amino acid levels tune perinatal neuronal excitability and survival

Knaus, L. S., Basilico, B., Malzl, D., Gerykova Bujalkova, M., Smogavec, M., Schwarz, L. A., Gorkiewicz, S., Amberg, N., Pauler, F. M., Knittl-Frank, C., et al. (2023) · Cell · doi:10.1016/j.cell.2023.02.037 · Cited by 70

Behavioral Activation and Depression Symptomatology: Longitudinal Assessment of Linguistic Indicators in Text-Based Therapy Sessions

Burkhardt, H. A., Alexopoulos, G. S., Pullmann, M. D., Hull, T. D., Areán, P. A., & Cohen, T. (2021) · Journal of Medical Internet Research · doi:10.2196/28244 · Cited by 68

Inheritance of paternal DNA damage by histone-mediated repair restriction

Wang, S., Meyer, D. H., & Schumacher, B. (2023) · Nature · doi:10.1038/s41586-022-05544-w · Cited by 68

Protein language models trained on multiple sequence alignments learn phylogenetic relationships

Lupo, U., Sgarbossa, D., & Bitbol, A.-F. (2022) · Nature Communications · doi:10.1038/s41467-022-34032-y · Cited by 67

Single-cell transcriptome landscape of circulating CD4+ T cell populations in autoimmune diseases

Yasumizu, Y., Takeuchi, D., Morimoto, R., Takeshima, Y., Okuno, T., Kinoshita, M., Morita, T., Kato, Y., Wang, M., Motooka, D., et al. (2024) · Cell Genomics · doi:10.1016/j.xgen.2023.100473 · Cited by 60

The efficacy of Facebook’s vaccine misinformation policies and architecture during the COVID-19 pandemic

Broniatowski, D. A., Simons, J. R., Gu, J., Jamison, A. M., & Abroms, L. C. (2023) · Science Advances · doi:10.1126/sciadv.adh2132 · Cited by 62

Bacterial SNPs in the human gut microbiome associate with host BMI

Zahavi, L., Lavon, A., Reicher, L., Shoer, S., Godneva, A., Leviatan, S., Rein, M., Weissbrod, O., Weinberger, A., & Segal, E. (2023) · Nature Medicine · doi:10.1038/s41591-023-02599-8 · Cited by 58

Unequal airborne exposure to toxic metals associated with race, ethnicity, and segregation in the USA

Kodros, J. K., Bell, M. L., Dominici, F., L’Orange, C., Godri Pollitt, K. J., Weichenthal, S., Wu, X., & Volckens, J. (2022) · Nature Communications · doi:10.1038/s41467-022-33372-z · Cited by 58

Progression subtypes in Parkinson’s disease identified by a data-driven multi cohort analysis

Hähnel, T., Raschka, T., Sapienza, S., Klucken, J., Glaab, E., Corvol, J.-C., Falkenburger, B. H., & Fröhlich, H. (2024) · npj Parkinson's Disease · doi:10.1038/s41531-024-00712-3 · Cited by 58

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

Digital cognitive behavior therapy intervention for depression and anxiety: retrospective study

Venkatesan, A., Rahimi, L., Kaur, M., & Mosunic, C. (2020) · JMIR Mental Health · doi:10.2196/21304 · Cited by 47

State transitions in the statistically stable place cell population correspond to rate of perceptual change

Tanni, S., de Cothi, W., & Barry, C. (2022) · Current Biology · doi:10.1016/j.cub.2022.06.046 · Cited by 37

High-resolution functional mapping of RAD51C by saturation genome editing

Olvera-León, R., Zhang, F., Offord, V., Zhao, Y., Tan, H. K., Gupta, P., Pal, T., Robles-Espinoza, C. D., Arriaga-González, F. G., Matsuyama, L. S. A. S., et al. (2024) · Cell · doi:10.1016/j.cell.2024.08.039 · Cited by 44

Structural determinants of co-translational protein complex assembly

Mallik, S., Venezian, J., Lobov, A., Heidenreich, M., Garcia-Seisdedos, H., Yeates, T. O., Shiber, A., & Levy, E. D. (2025) · Cell · doi:10.1016/j.cell.2024.11.013 · Cited by 42

Naturalistic use of psychedelics is related to emotional reactivity and self-consciousness: The mediating role of ego-dissolution and mystical experiences

Orłowski, P., Ruban, A., Szczypiński, J., Hobot, J., Bielecki, M., & Bola, M. (2022) · Journal of Psychopharmacology · doi:10.1177/02698811221089034 · Cited by 41

The global and promoter-centric 3D genome organization temporally resolved during a circadian cycle

Furlan-Magaril, M., Ando-Kuri, M., Arzate-Mejía, R. G., Morf, J., Cairns, J., Román-Figueroa, A., Tenorio-Hernández, L., Poot-Hernández, A. C., Andrews, S., Várnai, C., et al. (2021) · Genome Biology · doi:10.1186/s13059-021-02374-3 · Cited by 39

SCING: Inference of robust, interpretable gene regulatory networks from single cell and spatial transcriptomics

Littman, R., Cheng, M., Wang, N., Peng, C., & Yang, X. (2023) · iScience · doi:10.1016/j.isci.2023.107124 · Cited by 35

Broadly inhibitory antibodies to severe malaria virulence proteins

Reyes, R. A., Raghavan, S. S. R., Hurlburt, N. K., Introini, V., Bol, S., Kana, I. H., Jensen, R. W., Martinez-Scholze, E., Gestal-Mato, M., López-Gutiérrez, B., et al. (2024) · Nature · doi:10.1038/s41586-024-08220-3 · Cited by 35

Sexual differentiation of neural mechanisms of stress sensitivity during puberty

Wright, E. C., Luo, P. X., Zakharenkov, H. C., Serna Godoy, A., Lake, A. A., Prince, Z. D., Sekar, S., Culkin, H. I., Ramirez, A. V., Dwyer, T., et al. (2023) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2306475120 · Cited by 34

The bifidobacterial distribution in the microbiome of captive primates reflects parvorder and feed specialization of the host

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State of the interactomes: an evaluation of molecular networks for generating biological insights

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