UMAP: Uniform Manifold Approximation and Projection

UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction

McInnes, L., Healy, J., & Melville, J. (2018) · arXiv · doi:10.48550/arxiv.1802.03426 · Cited by 7,101

McInnes,%20Leland%20and%20Healy,%20John%20and%20Saul,%20Nathaniel%20and%20Grossberger,%20Lukas, UMAP, 2018, https://github.com/lmcinnes/umap, 2018-07-22

(n.d.)

Macrophages are metabolically heterogeneous within the tumor microenvironment

Geeraerts, X., Fernández-García, J., Hartmann, F. J., de Goede, K. E., Martens, L., Elkrim, Y., Debraekeleer, A., Stijlemans, B., Vandekeere, A., Rinaldi, G., et al. (2021) · Cell Reports · doi:10.1016/j.celrep.2021.110171 · Cited by 151

Pan-vaccine analysis reveals innate immune endotypes predictive of antibody responses to vaccination

Fourati, S., Tomalin, L. E., Mulè, M. P., Chawla, D. G., Gerritsen, B., Rychkov, D., Henrich, E., Miller, H. E. R., Hagan, T., Diray‐Arce, J., et al. (2022) · Nature Immunology · doi:10.1038/s41590-022-01329-5 · Cited by 155

Multicellular communities are perturbed in the aging human brain and Alzheimer’s disease

Cain, A., Taga, M., McCabe, C., Green, G. S., Hekselman, I., White, C. C., Lee, D. I., Gaur, P., Rozenblatt-Rosen, O., Zhang, F., et al. (2023) · Nature Neuroscience · doi:10.1038/s41593-023-01356-x · Cited by 142

Single-cell ATAC-Seq in human pancreatic islets and deep learning upscaling of rare cells reveals cell-specific type 2 diabetes regulatory signatures

Rai, V., Quang, D. X., Erdos, M. R., Cusanovich, D. A., Daza, R. M., Narisu, N., Zou, L. S., Didion, J. P., Guan, Y., Shendure, J., et al. (2019) · Molecular Metabolism · doi:10.1016/j.molmet.2019.12.006 · Cited by 139

Resistance to targeted therapies as a multifactorial, gradual adaptation to inhibitor specific selective pressures

Velde, R. V., Yoon, N., Marusyk, V., Durmaz, A., Dhawan, A., Miroshnychenko, D., Lozano‐Peral, D., Desai, B., Balynska, O. V., Poleszczuk, J., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-16212-w · Cited by 141

The harsh microenvironment in early breast cancer selects for a Warburg phenotype

Damaghi, M., West, J., Robertson‐Tessi, M., Xu, L., Ferrall‐Fairbanks, M. C., Stewart, P. A., Persi, E., Fridley, B. L., Altrock, P. M., Gatenby, R. A., et al. (2021) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2011342118 · Cited by 146

Next generation plasma proteome profiling to monitor health and disease

Zhong, W., Edfors, F., Gummesson, A., Bergström, G. M. L., Fagerberg, L., & Uhlén, M. (2021) · Nature Communications · doi:10.1038/s41467-021-22767-z · Cited by 141

Single-cell atlas of human liver development reveals pathways directing hepatic cell fates

Wesley, B. T., Ross, A. D. B., Muraro, D., Miao, Z., Saxton, S., Tomaz, R. A., Morell, C. M., Ridley, K., Zacharis, E. D., Petrus-Reurer, S., et al. (2022) · Nature Cell Biology · doi:10.1038/s41556-022-00989-7 · Cited by 146

Age-determined expression of priming protease TMPRSS2 and localization of SARS-CoV-2 in lung epithelium

Schuler, B. A., Habermann, A. C., Plosa, E. J., Taylor, C. J., Jetter, C. S., Kapp, M., Benjamin, J. T., Gulleman, P., Nichols, D. S., Braunstein, L. Z., et al. (2020) · Journal of Clinical Investigation · doi:10.1172/jci140766 · Cited by 136

Multi-task deep learning for cardiac rhythm detection in wearable devices

Soto, J. T., & Ashley, E. A. (2020) · npj Digital Medicine · doi:10.1038/s41746-020-00320-4 · Cited by 148

Cardiac cell type–specific gene regulatory programs and disease risk association

Hocker, J. D., Poirion, O., Zhu, F., Buchanan, J., Zhang, K., Chiou, J., Wang, T., Zhang, Q., Hou, X., Li, Y. E., et al. (2021) · Science Advances · doi:10.1126/sciadv.abf1444 · Cited by 136

Morphology and gene expression profiling provide complementary information for mapping cell state

Way, G. P., Natoli, T. E., Adeboye, A., Litichevskiy, L., Yang, A. X., Lü, X., Caicedo, J. C., Cimini, B. A., Karhohs, K. W., Logan, D. J., et al. (2022) · Cell Systems · doi:10.1016/j.cels.2022.10.001 · Cited by 146

Comparative analysis of cell–cell communication at single-cell resolution

Wilk, A. J., Shalek, A. K., Holmes, S., & Blish, C. A. (2023) · Nature Biotechnology · doi:10.1038/s41587-023-01782-z · Cited by 146

MAGUS: machine learning and graph theory assisted universal structure searcher

Wang, J., Gao, H., Han, Y., Ding, C., Pan, S., Wang, Y., Jia, Q., Wang, H., Xing, D., & Sun, J. (2023) · National Science Review · doi:10.1093/nsr/nwad128 · Cited by 142

Granzyme K activates the entire complement cascade

Donado, C., Theisen, E., Zhang, F., Nathan, A., Fairfield, M. L., Rupani, K. V., Jones, D., Johannes, K., Accelerating Medicines Partnership RA/SLE Network, Albrecht, J., et al. (2025) · Nature · doi:10.1038/s41586-025-08713-9 · Cited by 146

Attention-based multi-label neural networks for integrated prediction and interpretation of twelve widely occurring RNA modifications

Song, Z., Huang, D., Song, B., Chen, K., Song, Y., Liu, G., Su, J., de Magalhães, J. P., Rigden, D. J., & Meng, J. (2021) · Nature Communications · doi:10.1038/s41467-021-24313-3 · Cited by 142

Aldehyde-driven transcriptional stress triggers an anorexic DNA damage response

Mulderrig, L., Garaycoechea, J. I., Tuong, Z. K., Millington, C. L., Dingler, F. A., Ferdinand, J. R., Gaul, L., Tadross, J. A., Arends, M. J., O’Rahilly, S., et al. (2021) · Nature · doi:10.1038/s41586-021-04133-7 · Cited by 139

Human- and machine-centred designs of molecules and materials for sustainability and decarbonization

Peng, J., Schwalbe‐Koda, D., Akkiraju, K., Xie, T., Giordano, L., Yu, Y., Eom, C. J., Lunger, J. R., Zheng, D. J., Rao, R. R., et al. (2022) · Nature Reviews Materials · doi:10.1038/s41578-022-00466-5 · Cited by 157

Biomolecular dynamics with machine-learned quantum-mechanical force fields trained on diverse chemical fragments

Unke, O. T., Stöhr, M., Ganscha, S., Unterthiner, T., Maennel, H., Kashubin, S., Ahlin, D., Gastegger, M., Sandonas, L. M., Berryman, J. T., et al. (2024) · Science Advances · doi:10.1126/sciadv.adn4397 · Cited by 160

Joint single-cell measurements of nuclear proteins and RNA in vivo

Chung, H., Parkhurst, C. N., Magee, E. M., Phillips, D., Habibi, E., Chen, F., Yeung, B. Z., Waldman, J., Artis, D., & Regev, A. (2021) · Nature Methods · doi:10.1038/s41592-021-01278-1 · Cited by 144

Deep learning the collisional cross sections of the peptide universe from a million experimental values

Meier, F., Köhler, N., Brunner, A., Wanka, J.-M. H., Voytik, E., Strauss, M. T., Theis, F. J., & Mann, M. (2021) · Nature Communications · doi:10.1038/s41467-021-21352-8 · Cited by 136

Harmonizome 3.0: integrated knowledge about genes and proteins from diverse multi-omics resources

Diamant, I., Clarke, D. J. B., Evangelista, J. E., Lingam, N., & Ma’ayan, A. (2024) · Nucleic Acids Research · doi:10.1093/nar/gkae1080 · Cited by 176

Enteric glial cell heterogeneity regulates intestinal stem cell niches

Baghdadi, M. B., Ayyaz, A., Coquenlorge, S., Chu, B., Kumar, S., Streutker, C., Wrana, J. L., & Kim, T.-H. (2021) · Cell stem cell · doi:10.1016/j.stem.2021.10.004 · Cited by 143

Single-cell DNA methylome and 3D multi-omic atlas of the adult mouse brain

Liu, H., Zeng, Q., Zhou, J., Bartlett, A., Wang, B.-A., Berube, P., Tian, W., Kenworthy, M., Altshul, J., Nery, J. R., et al. (2023) · Nature · doi:10.1038/s41586-023-06805-y · Cited by 139

Single-cell genomics meets human genetics

Cuomo, A. S. E., Nathan, A., Raychaudhuri, S., MacArthur, D. G., & Powell, J. E. (2023) · Nature Reviews Genetics · doi:10.1038/s41576-023-00599-5 · Cited by 124

Catch them alive: A malware detection approach through memory forensics, manifold learning and computer vision

Bozkır, A. S., Tahıllıoğlu, E., Aydos, M., & Kara, İ. (2021) · Computers & Security · doi:10.1016/j.cose.2020.102166 · Cited by 144

Challenges in benchmarking metagenomic profilers

Sun, Z., Huang, S. W., Zhang, M., Zhu, Q., Haiminen, N., Carrieri, A. P., Vázquez‐Baeza, Y., Parida, L., Kim, H., Knight, R. D., et al. (2021) · Nature Methods · doi:10.1038/s41592-021-01141-3 · Cited by 140

Prolonged SARS-CoV-2 Infection in Patients with Lymphoid Malignancies

Lee, C. Y., Shah, M. K., Hoyos, D., Solovyov, A., Douglas, M., Taur, Y., Maslak, P. G., Babady, N. E., Greenbaum, B., Kamboj, M., et al. (2021) · Cancer Discovery · doi:10.1158/2159-8290.cd-21-1033 · Cited by 133

The transcription factor IRF2 drives interferon-mediated CD8+ T cell exhaustion to restrict anti-tumor immunity

Lukhele, S., Abd-Rabbo, D., Guo, M., Shen, J., Elsaesser, H. J., Quevedo, R., Carew, M., Gadalla, R., Snell, L. M., Mahesh, L., et al. (2022) · Immunity · doi:10.1016/j.immuni.2022.10.020 · Cited by 136

Cell-type-specific signaling networks in heterocellular organoids

Qin, X., Sufi, J., Vlckova, P., Kyriakidou, P., Acton, S. E., Li, V. S. W., Nitz, M., & Tape, C. J. (2020) · Nature Methods · doi:10.1038/s41592-020-0737-8 · Cited by 134

Unsupervised topological alignment for single-cell multi-omics integration

Cao, K., Bai, X., Hong, Y., & Wan, L. (2020) · Bioinformatics · doi:10.1093/bioinformatics/btaa443 · Cited by 130

Identification of leukemic and pre-leukemic stem cells by clonal tracking from single-cell transcriptomics

Velten, L., Story, B. A., Hernández-Malmierca, P., Raffel, S., Leonce, D. R., Milbank, J. H., Paulsen, M. S., Demir, A., Szu‐Tu, C., Frömel, R., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-21650-1 · Cited by 144

Single-Cell Transcriptomics: Current Methods and Challenges in Data Acquisition and Analysis

Adil, A., Kumar, V., Jan, A. T., & Asger, M. (2021) · Frontiers in Neuroscience · doi:10.3389/fnins.2021.591122 · Cited by 138

Industrial data science – a review of machine learning applications for chemical and process industries

Mowbray, M., Vallerio, M., Pérez-Galván, C., Zhang, D., del Rio‐Chanona, E. A., & Navarro-Brull, F. J. (2022) · Reaction Chemistry & Engineering · doi:10.1039/d1re00541c · Cited by 149

Light attention predicts protein location from the language of life

Stärk, H., Dallago, C., Heinzinger, M., & Rost, B. (2021) · Bioinformatics Advances · doi:10.1093/bioadv/vbab035 · Cited by 130

Prenatal immune stress blunts microglia reactivity, impairing neurocircuitry

Hayes, L. N., An, K., Carloni, E., Li, F., Vincent, E., Trippaers, C., Paranjpe, M. D., Dölen, G., Goff, L. A., Ramos, A., et al. (2022) · Nature · doi:10.1038/s41586-022-05274-z · Cited by 138

Mental Health Analysis in Social Media Posts: A Survey

Garg, M. (2023) · Archives of Computational Methods in Engineering · doi:10.1007/s11831-022-09863-z · Cited by 161

Mapping Materials and Molecules

Cheng, B., Griffiths, R., Wengert, S., Künkel, C., Stenczel, T. K., Zhu, B., Deringer, V. L., Bernstein, N., Margraf, J. T., Reuter, K., et al. (2020) · Accounts of Chemical Research · doi:10.1021/acs.accounts.0c00403 · Cited by 130

Opposing Wnt signals regulate cervical squamocolumnar homeostasis and emergence of metaplasia

Chumduri, C., Gurumurthy, R. K., Berger, H., Dietrich, O., Kumar, N., Koster, S., Brinkmann, V., Hoffmann, K., Drabkina, M., Arampatzi, P., et al. (2021) · Nature Cell Biology · doi:10.1038/s41556-020-00619-0 · Cited by 133

Self-supervised deep learning encodes high-resolution features of protein subcellular localization

Kobayashi, H., Cheveralls, K. C., Leonetti, M. D., & Royer, L. A. (2022) · Nature Methods · doi:10.1038/s41592-022-01541-z · Cited by 140

Paths and timings of the peopling of Polynesia inferred from genomic networks

Ioannidis, A. G., Blanco-Portillo, J., Sandoval, K., Hagelberg, E., Barberena-Jonas, C., Hill, A. V. S., Rodríguez-Rodríguez, J. E., Fox, K., Robson, K., Haoa-Cardinali, S., et al. (2021) · Nature · doi:10.1038/s41586-021-03902-8 · Cited by 124

Putative cell type discovery from single-cell gene expression data

Miao, Z., Moreno, P., Huang, N., Papatheodorou, I., Brazma, A., & Teichmann, S. A. (2020) · Nature Methods · doi:10.26226/morressier.5ebd45acffea6f735881af34 · Cited by 133

A single-cell view of the transcriptome during lateral root initiation in Arabidopsis thaliana

Gala, H., Lanctot, A., Jean-Baptiste, K., Guiziou, S., Chu, J. C., Zemke, J. E., George, W., Queitsch, C., Cuperus, J. T., & Nemhauser, J. L. (2021) · The Plant Cell · doi:10.1093/plcell/koab101 · Cited by 136

A single-cell atlas of human teeth

Pagella, P., Roditi, L. D. V., Stadlinger, B., Moor, A. E., & Mitsiadis, T. A. (2021) · iScience · doi:10.1016/j.isci.2021.102405 · Cited by 131

Single cell T cell landscape and T cell receptor repertoire profiling of AML in context of PD-1 blockade therapy

Abbas, H. A., Hao, D., Tomczak, K. J., Barrodia, P., Im, J. S., Reville, P. K., Alaniz, Z., Wang, W., Wang, R., Wang, F., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-26282-z · Cited by 130

Whole-animal multiplexed single-cell RNA-seq reveals transcriptional shifts across Clytia medusa cell types

Chari, T., Weissbourd, B., Gehring, J., Ferraioli, A., Leclère, L., Herl, M., Gao, F., Chevalier, S., Copley, R. R., Houliston, E., et al. (2021) · Science Advances · doi:10.1126/sciadv.abh1683 · Cited by 123

Proteomic analysis of archival breast cancer clinical specimens identifies biological subtypes with distinct survival outcomes

Asleh, K., Negri, G. L., Miko, S. E. S., Colborne, S., Hughes, C. S., Wang, X. Q., Gao, D., Gilks, C. B., Chia, S., Nielsen, T. O., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-28524-0 · Cited by 132

Brain-wide cell-type-specific transcriptomic signatures of healthy ageing in mice

Jin, K., Yao, Z., van Velthoven, C. T. J., Kaplan, E. S., Glattfelder, K. J., Barlow, S. T., Boyer, G., Carey, D., Casper, T., Chakka, A. B., et al. (2025) · Nature · doi:10.1038/s41586-024-08350-8 · Cited by 152

Tracking neural activity from the same cells during the entire adult life of mice

Zhao, S., Tang, X., Tian, W., Partarrieu, S., Liu, R., Shen, H., Lee, J. Y., Guo, S., Lin, Z., & Jia Liu, B. (2023) · Nature Neuroscience · doi:10.1038/s41593-023-01267-x · Cited by 134

Toward universal cell embeddings: integrating single-cell RNA-seq datasets across species with SATURN

Rosen, Y., Brbić, M., Roohani, Y., Swanson, K., Li, Z., & Leskovec, J. (2024) · Nature Methods · doi:10.1038/s41592-024-02191-z · Cited by 144

Machete identifies interferon-encompassing chromosome 9p21.3 deletions as mediators of immune evasion and metastasis

Barriga, F. M., Tsanov, K. M., Ho, Y.-J., Sohail, N., Zhang, A., Baslan, T., Wuest, A. N., Del Priore, I., Meškauskaitė, B., Livshits, G., et al. (2022) · Nature Cancer · doi:10.1038/s43018-022-00443-5 · Cited by 125

Latent Space Cartography: Visual Analysis of Vector Space Embeddings

Liu, Y., Jun, E., Li, Q., & Heer, J. (2019) · Computer Graphics Forum · doi:10.1111/cgf.13672 · Cited by 146

LSTM-PHV: prediction of human-virus protein–protein interactions by LSTM with word2vec

Tsukiyama, S., Hasan, M. M., Fujii, S., & Kurata, H. (2021) · Briefings in Bioinformatics · doi:10.1093/bib/bbab228 · Cited by 131

Comprehensive visualization of cell–cell interactions in single-cell and spatial transcriptomics with NICHES

Raredon, M. S. B., Yang, J., Kothapalli, N., Lewis, W., Kaminski, N., Niklason, L. E., & Kluger, Y. (2022) · Bioinformatics · doi:10.1093/bioinformatics/btac775 · Cited by 131

scSorter: assigning cells to known cell types according to marker genes

Guo, H., & Li, J. (2021) · Genome biology · doi:10.1186/s13059-021-02281-7 · Cited by 132

A hierarchical 3D-motion learning framework for animal spontaneous behavior mapping

Huang, K., Han, Y., Chen, K., Pan, H., Zhao, G., Yi, W., Li, X., Liu, S., Wei, P., & Wang, L. (2021) · Nature Communications · doi:10.1038/s41467-021-22970-y · Cited by 128

Single-cell characterization of macrophages in glioblastoma reveals MARCO as a mesenchymal pro-tumor marker

Chen, A., Gartrell, R. D., Zhao, J., Upadhyayula, P. S., Zhao, W., Yuan, J., Minns, H. E., Dovas, A., Bruce, J. N., Lasorella, A., et al. (2021) · Genome Medicine · doi:10.1186/s13073-021-00906-x · Cited by 125

Integrating T cell receptor sequences and transcriptional profiles by clonotype neighbor graph analysis (CoNGA)

Schattgen, S., Guion, K., Crawford, J. C., Souquette, A. H., Barrio, Á. M., Stubbington, M. J. T., Thomas, P. G., & Bradley, P. H. (2021) · Nature Biotechnology · doi:10.1038/s41587-021-00989-2 · Cited by 131

Modeling embryo-endometrial interface recapitulating human embryo implantation

Shibata, S., Endo, S., Nagai, L. A. E., Kobayashi, E. H., Oike, A., Kobayashi, N., Kitamura, A., Hori, T., Nashimoto, Y., Nakato, R., et al. (2024) · Science Advances · doi:10.1126/sciadv.adi4819 · Cited by 138

New interpretable deep learning model to monitor real-time PM2.5 concentrations from satellite data

Yan, X., Zang, Z., Luo, N., Jiang, Y., & Li, Z. (2020) · Environment International · doi:10.1016/j.envint.2020.106060 · Cited by 129

Single-cell analysis of patient-derived PDAC organoids reveals cell state heterogeneity and a conserved developmental hierarchy

Krieger, T. G., Blanc, S. L., Jabs, J., Ten, F. W., Ishaque, N., Jechow, K., Debnath, O., Leonhardt, C.-S., Giri, A., Eils, R., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-26059-4 · Cited by 131

Benchmarking principal component analysis for large-scale single-cell RNA-sequencing

Tsuyuzaki, K., Sato, H., Sato, K., & Nikaido, I. (2020) · Genome biology · doi:10.1186/s13059-019-1900-3 · Cited by 130

Single-cell transcriptomes of developing and adult olfactory receptor neurons in Drosophila

McLaughlin, C. N., Brbić, M., Xie, Q., Li, T., Horns, F., Kolluru, S. S., Kebschull, J. M., Vacek, D., Xie, A., Li, J., et al. (2021) · eLife · doi:10.7554/elife.63856 · Cited by 115

UMAP as a Dimensionality Reduction Tool for Molecular Dynamics Simulations of Biomacromolecules: A Comparison Study

Trozzi, F., Wang, X., & Tao, P. (2021) · The Journal of Physical Chemistry B · doi:10.1021/acs.jpcb.1c02081 · Cited by 128

Single-cell transcriptomics identifies Mcl-1 as a target for senolytic therapy in cancer

Troiani, M., Colucci, M., D’Ambrosio, M., Guccini, I., Pasquini, E., Varesi, A., Valdata, A., Mosole, S., Revandkar, A., Attanasio, G., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-29824-1 · Cited by 134

Age-induced alterations of granulopoiesis generate atypical neutrophils that aggravate stroke pathology

Gullotta, G. S., Feo, D. D., Friebel, E., Semerano, A., Scotti, G. M., Bergamaschi, A., Butti, E., Brambilla, E., Genchi, A., Capotondo, A., et al. (2023) · Nature Immunology · doi:10.1038/s41590-023-01505-1 · Cited by 124

Tools for the analysis of high-dimensional single-cell RNA sequencing data

Wu, Y., & Zhang, K. (2020) · Nature Reviews Nephrology · doi:10.1038/s41581-020-0262-0 · Cited by 123

Avocado: a multi-scale deep tensor factorization method learns a latent representation of the human epigenome

Schreiber, J. M., Durham, T., Bilmes, J. A., & Noble, W. S. (2020) · Genome biology · doi:10.1186/s13059-020-01977-6 · Cited by 115

CUP-AI-Dx: A tool for inferring cancer tissue of origin and molecular subtype using RNA gene-expression data and artificial intelligence

Zhao, Y., Pan, Z., Namburi, S., Pattison, A., Posner, A., Balachander, S., Paisie, C. A., Reddi, H. V., Rueter, J., Gill, A. J., et al. (2020) · EBioMedicine · doi:10.1016/j.ebiom.2020.103030 · Cited by 120

BMP gradient along the intestinal villus axis controls zonated enterocyte and goblet cell states

Beumer, J., Puschhof, J., Yengej, F. Y., Zhao, L., Martínez-Silgado, A., Blotenburg, M., Begthel, H., Boot, C., van Oudenaarden, A., Chen, Y., et al. (2022) · Cell Reports · doi:10.1016/j.celrep.2022.110438 · Cited by 126

Single-cell and spatial transcriptomics reveal aberrant lymphoid developmental programs driving granuloma formation

Krausgruber, T., Redl, A., Barreca, D., Doberer, K., Romanovskaia, D., Dobnikar, L., Guarini, M., Unterluggauer, L., Kleißl, L., Atzmüller, D., et al. (2023) · Immunity · doi:10.1016/j.immuni.2023.01.014 · Cited by 132

Manifold Learning: What, How, and Why

Meilă, M., & Zhang, H. (2023) · Annual Review of Statistics and Its Application · doi:10.1146/annurev-statistics-040522-115238 · Cited by 141

Pervasive sublethal effects of agrochemicals on insects at environmentally relevant concentrations

Gándara, L., Jacoby, R. P., Laurent, F., Spatuzzi, M., Vlachopoulos, N., Borst, N. O., Ekmen, G., Potel, C. M., Garrido‐Rodríguez, M., Böhmert, A. L., et al. (2024) · Science · doi:10.1126/science.ado0251 · Cited by 139

Combining Label Propagation and Simple Models Out-performs Graph Neural NetworksPreprint

Huang, Q., He, H., Singh, A. P., Lim, S.-N., & Benson, A. R. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2010.13993 · Cited by 210

Differentiation of Human Intestinal Organoids with Endogenous Vascular Endothelial Cells

Holloway, E. M., Wu, J. H., Czerwinski, M. J., Sweet, C. W., Wu, A., Tsai, Y.-H., Huang, S., Stoddard, A. E., Capeling, M. M., Glass, I. A., et al. (2020) · Developmental Cell · doi:10.1016/j.devcel.2020.07.023 · Cited by 131

Classification of Human White Blood Cells Using Machine Learning for Stain‐Free Imaging Flow Cytometry

Lippeveld, M., Knill, C., Ladlow, E., Fuller, A., Michaelis, L. J., Saeys, Y., Filby, A. J., & Peralta, D. (2019) · Cytometry Part A · doi:10.1002/cyto.a.23920 · Cited by 117

Machine learning can identify newly diagnosed patients with CLL at high risk of infection

Agius, R., Brieghel, C., Andersen, M. A., Pearson, A. T., Ledergerber, B., Cozzi‐Lepri, A., Louzoun, Y., Andersen, C. L., Bergstedt, J., von Stemann, J. H., et al. (2020) · Nature Communications · doi:10.1038/s41467-019-14225-8 · Cited by 127

Single-Cell Transcriptomic Atlas of Different Human Cardiac Arteries Identifies Cell Types Associated With Vascular Physiology

Hu, Z., Liu, W., Hua, X., Chen, X., Chang, Y., Hu, Y., Xu, Z., & Song, J. (2021) · Arteriosclerosis Thrombosis and Vascular Biology · doi:10.1161/atvbaha.120.315373 · Cited by 120

Appyters: Turning Jupyter Notebooks into data-driven web apps

Clarke, D. J. B., Jeon, M., Stein, D. J., Moiseyev, N., Kropiwnicki, E., Dai, C. S., Xie, Z., Wojciechowicz, M. L., Litz, S., Hom, J., et al. (2021) · Patterns · doi:10.1016/j.patter.2021.100213 · Cited by 129

Cortical overgrowth in a preclinical forebrain organoid model of CNTNAP2-associated autism spectrum disorder

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Single-cell nuclear architecture across cell types in the mouse brain

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