Massively multiplex chemical transcriptomics at single-cell resolution

Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles

Subramanian, A., Tamayo, P., Mootha, V. K., Mukherjee, S., Ebert, B. L., Gillette, M. A., Paulovich, A., Pomeroy, S. L., Golub, T. R., Lander, E. S., et al. (2005) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0506580102 · Cited by 53,625

limma powers differential expression analyses for RNA-sequencing and microarray studies

Ritchie, M. E., Phipson, B., Wu, D., Hu, Y., Law, C. W., Shi, W., & Smyth, G. K. (2015) · Nucleic Acids Research · doi:10.1093/nar/gkv007 · Cited by 40,458

BEDTools: a flexible suite of utilities for comparing genomic features

Quinlan, A. R., & Hall, I. M. (2010) · Bioinformatics · doi:10.1093/bioinformatics/btq033 · Cited by 27,437

UMAP: Uniform Manifold Approximation and Projection

McInnes, L., Healy, J., Saul, N., & Großberger, L. (2018) · Journal of Open Source Software · doi:10.21105/joss.00861 · Cited by 9,826

The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity

Barretina, J., Caponigro, G., Stransky, N., Venkatesan, K., Margolin, A. A., Kim, S., Wilson, C. J., Lehár, J., Kryukov, G. V., Sonkin, D., et al. (2012) · Nature · doi:10.1038/nature11003 · Cited by 7,950

The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells

Trapnell, C., Cacchiarelli, D., Grimsby, J., Pokharel, P., Li, S., Morse, M., Lennon, N. J., Livak, K. J., Mikkelsen, T. S., & Rinn, J. L. (2014) · Nature Biotechnology · doi:10.1038/nbt.2859 · Cited by 7,314

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

Stochastic Gene Expression in a Single Cell

Elowitz, M. B., Levine, A. J., Siggia, E. D., & Swain, P. S. (2002) · Science · doi:10.1126/science.1070919 · Cited by 5,441

The Connectivity Map: Using Gene-Expression Signatures to Connect Small Molecules, Genes, and Disease

Lamb, J., Crawford, E. D., Peck, D., Modell, J. W., Blat, I. C., Wrobel, M. J., Lerner, J., Brunet, J.-P., Subramanian, A., Ross, K. N., et al. (2006) · Science · doi:10.1126/science.1132939 · Cited by 5,257

Dissecting the multicellular ecosystem of metastatic melanoma by single-cell RNA-seq

Tirosh, I., Izar, B., Prakadan, S. M., Wadsworth, M. H., Treacy, D., Trombetta, J. J., Rotem, A., Rodman, C., Lian, C., Murphy, G., et al. (2016) · Science · doi:10.1126/science.aad0501 · Cited by 4,944

Mitochondrial Proteostasis Requires Genes Encoded in a Neurodevelopmental Syndrome Locus

Gokhale, A., Lee, C. E., Zlatic, S. A., Freeman, A. A. H., Shearing, N., Hartwig, C. L., Ogunbona, O. B., Bassell‐Hawkins, J., Wynne, M. E., Werner, E., et al. (2021) · Journal of Neuroscience · doi:10.1523/jneurosci.2197-20.2021 · Cited by 30

Developmental Deconvolution for Classification of Cancer Origin

Moiso, E., Farahani, A. A., Marble, H. D., Hendricks, A., Mildrum, S., Levine, S. S., Lennerz, J. K., & Garg, S. (2022) · Cancer Discovery · doi:10.1158/2159-8290.cd-21-1443 · Cited by 30

PerturbNet predicts single-cell responses to unseen chemical and genetic perturbations

Yu, H., Qian, W., Song, Y., & Welch, J. D. (2025) · Molecular Systems Biology · doi:10.1038/s44320-025-00131-3 · Cited by 57

High‐Throughput Screening and General Synthesis Strategy of Single‐Atom Nanozymes for Oral Squamous Cell Carcinoma Therapy

Shen, J., Zhang, G.-M., Zhang, Z., Zhang, L., Zhuang, Z., Qian, Y., Dou, Y., Wang, S., Wang, D. S., & Wang, Y. (2025) · Advanced Materials · doi:10.1002/adma.202416463 · Cited by 33

Decoding heterogeneous single-cell perturbation responses

Song, B., Liu, D., Dai, W., McMyn, N. F., Wang, Q., Yang, D., Krejčí, A., Vasilyev, A., Untermoser, N., Loregger, A., et al. (2025) · Nature Cell Biology · doi:10.1038/s41556-025-01626-9 · Cited by 40

Computational estimation of quality and clinical relevance of cancer cell lines

Trastulla, L., Noorbakhsh, J., Vazquez, F., McFarland, J., & Iorio, F. (2022) · Molecular Systems Biology · doi:10.15252/msb.202211017 · Cited by 31

Pooled genetic screens with image‐based profiling

Walton, R. T., Singh, A. K., & Blainey, P. C. (2022) · Molecular Systems Biology · doi:10.15252/msb.202110768 · Cited by 29

scPriorGraph: constructing biosemantic cell–cell graphs with prior gene set selection for cell type identification from scRNA-seq data

Cao, X., Huang, Y., You, Z., Shang, X., Hu, L., Hu, P., & Huang, Z.-A. (2024) · Genome biology · doi:10.1186/s13059-024-03357-w · Cited by 30

What is a cell type?

Fleck, J. S., Camp, J. G., & Treutlein, B. (2023) · Science · doi:10.1126/science.adf6162 · Cited by 33

Phenotypic screening with target identification and validation in the discovery and development of E3 ligase modulators

Ege, N., Bouguenina, H., Tatari, M. N., & Chopra, R. (2021) · Cell chemical biology · doi:10.1016/j.chembiol.2021.02.011 · Cited by 31

Combined morphological and proteome profiling reveals target-independent impairment of cholesterol homeostasis

Schneidewind, T., Brause, A., Schölermann, B., Sievers, S., Pahl, A., Sankar, M. G., Winzker, M., Janning, P., Kumar, K., Ziegler, S., et al. (2021) · Cell chemical biology · doi:10.1016/j.chembiol.2021.06.003 · Cited by 29

Combi-seq for multiplexed transcriptome-based profiling of drug combinations using deterministic barcoding in single-cell droplets

Mathur, L., Szalai, B., Du, N. H., Utharala, R., Ballinger, M., Landry, J. J. M., Ryckelynck, M., Beneš, V., Sáez-Rodríguez, J., & Merten, C. A. (2022) · Nature Communications · doi:10.1038/s41467-022-32197-0 · Cited by 27

Deep Learning in Single-cell Analysis

Molho, D., Ding, J., Tang, W., Li, Z., Wen, H., Wang, Y., Venegas, J., Jin, W., Liu, R., Su, R., et al. (2024) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3641284 · Cited by 38

PerturBase: a comprehensive database for single-cell perturbation data analysis and visualization

Wei, Z., Si, D., Duan, B., Gao, Y., Yu, Q., Zhang, Z., Guo, L., & Liu, Q. (2024) · Nucleic Acids Research · doi:10.1093/nar/gkae858 · Cited by 35

Deciphering the Biology of Circulating Tumor Cells through Single-Cell RNA Sequencing: Implications for Precision Medicine in Cancer

Orrapin, S., Thongkumkoon, P., Udomruk, S., Moonmuang, S., Sutthitthasakul, S., Yongpitakwattana, P., Pruksakorn, D., & Chaiyawat, P. (2023) · International Journal of Molecular Sciences · doi:10.3390/ijms241512337 · Cited by 28

Identification of Dihydroorotate Dehydrogenase Inhibitors Using the Cell Painting Assay

Schölermann, B., Bonowski, J., Grigalunas, M., Burhop, A., Xie, Y., Hoock, J., Liu, J., Dow, M., Nelson, A. S., Nowak, C., et al. (2022) · ChemBioChem · doi:10.1002/cbic.202200475 · Cited by 24

A Systematic Comparison of Single-Cell Perturbation Response Prediction Models

Li, L., You, Y., Fu, Y., Liao, W., Fan, X., Lu, S., Cao, Y., Li, B., Ren, W., Kong, J., et al. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.12.23.630036 · Cited by 25

Transcriptional Regulation of the Hippo Pathway: Current Understanding and Insights from Single-Cell Technologies

Paul, S., Xie, S., Yao, X., & Dey, A. (2022) · Cells · doi:10.3390/cells11142225 · Cited by 25

Phospho-seq: integrated, multi-modal profiling of intracellular protein dynamics in single cells

Blair, J. D., Hartman, A., Zenk, F. L., Wahle, P., Brancati, G., Dalgarno, C., Treutlein, B., & Satija, R. (2025) · Nature Communications · doi:10.1038/s41467-025-56590-7 · Cited by 27

Pooled Genome-Scale CRISPR Screens in Single Cells

Schraivogel, D., Steinmetz, L. M., & Parts, L. (2023) · Annual Review of Genetics · doi:10.1146/annurev-genet-072920-013842 · Cited by 28

Single-cell transcriptomics in tissue engineering and regenerative medicine

Ruta, A., Krishnan, K., & Elisseeff, J. H. (2023) · Nature Reviews Bioengineering · doi:10.1038/s44222-023-00132-7 · Cited by 25

scTrends: A living review of commercial single-cell and spatial 'omic technologies

Jonghe, J. D., Opzoomer, J. W., Vilas‐Zornoza, A., Nilges, B. S., Crane, P., Vicari, M., Lee, H., Lara‐Astiaso, D., Groß, T., Morf, J., et al. (2024) · Cell Genomics · doi:10.1016/j.xgen.2024.100723 · Cited by 27

Single-cell omics reveal the mechanisms of traditional Chinese medicines

Jiang, B., Si, J., Luo, H., & Le, L. (2025) · Phytomedicine · doi:10.1016/j.phymed.2025.157204 · Cited by 26

Estrogen and Androgen Receptor Inhibitors: Unexpected Allies in the Fight Against COVID-19

Bravaccini, S., Fonzi, E., Tebaldi, M., Angeli, D., Martinelli, G., Nicolini, F., Parrella, P., & Mazza, M. (2021) · Cell Transplantation · doi:10.1177/0963689721991477 · Cited by 25

PerturbNet predicts single-cell responses to unseen chemical and genetic perturbations

Yu, H., & Welch, J. D. (2022) · doi:10.1101/2022.07.20.500854 · Cited by 57

Applications of single-cell technologies in drug discovery for tumor treatment

Liu, B., Hu, S., & Wang, X. (2024) · iScience · doi:10.1016/j.isci.2024.110486 · Cited by 24

D-SPIN constructs gene regulatory network models from multiplexed scRNA-seq data revealing organizing principles of cellular perturbation response

Jiang, J., Chen, S., Tsou, T., McGinnis, C. S., Khazaei, T., Zhu, Q., Park, J. H., Strazhnik, I.-M., Vielmetter, J., Gong, Y., et al. (2023) · doi:10.1101/2023.04.19.537364 · Cited by 21

FIPRESCI: droplet microfluidics based combinatorial indexing for massive-scale 5′-end single-cell RNA sequencing

Li, Y., Huang, Z., Zhang, Z., Wang, Q., Li, F., Wang, S., Ji, X., Shu, S., Fang, X., & Jiang, L. (2023) · Genome biology · doi:10.1186/s13059-023-02893-1 · Cited by 22

Metabolic reprogramming by histone deacetylase inhibition preferentially targets NRF2-activated tumors

Karagiannis, D., Wu, W., Li, A. M., Hayashi, M., Chen, X., Yip, M., Mangipudy, V. S., Xu, X., Rivera, F. J. S., Soto‐Feliciano, Y. M., et al. (2023) · Cell Reports · doi:10.1016/j.celrep.2023.113629 · Cited by 26

Single-cell transcriptomics: a novel precision medicine technique in nephrology

Kim, J., & Park, J. (2020) · The Korean Journal of Internal Medicine · doi:10.3904/kjim.2020.415 · Cited by 22

A Transcriptome‐Based Drug Discovery Paradigm for Neurodevelopmental Disorders

Dhindsa, R. S., Zoghbi, A. W., Krizay, D. K., Vasavda, C., & Goldstein, D. B. (2020) · Annals of Neurology · doi:10.1002/ana.25950 · Cited by 22

Insights gained from single-cell analysis of chimeric antigen receptor T-cell immunotherapy in cancer

Tang, L., Huang, Z., Mei, H., & Hu, Y. (2023) · Military Medical Research · doi:10.1186/s40779-023-00486-4 · Cited by 24

Multiplex single-cell chemical genomics reveals the kinase dependence of the response to targeted therapy

McFaline‐Figueroa, J. L., Srivatsan, S. R., Hill, A. J., Gasperini, M. J., Jackson, D. L., Saunders, L. M., Domcke, S., Regalado, S. G., Lazarchuck, P., Alvarez, S., et al. (2024) · Cell Genomics · doi:10.1016/j.xgen.2023.100487 · Cited by 25

CASB: a concanavalin A‐based sample barcoding strategy for single‐cell sequencing

Fang, L., Li, G., Sun, Z., Zhu, Q., Cui, H., Li, Y., Zhang, J., Liang, W., Wei, W., Hu, Y., et al. (2021) · Molecular Systems Biology · doi:10.15252/msb.202010060 · Cited by 20

Genetically engineered zebrafish as models of skeletal development and regeneration

Henke, K., Farmer, D. T., Niu, X., Kraus, J. M., Galloway, J. L., & Youngstrom, D. W. (2022) · Bone · doi:10.1016/j.bone.2022.116611 · Cited by 22

Responsiveness to perturbations is a hallmark of transcription factors that maintain cell identity in vitro

Mellis, I. A., Edelstein, H. I., Truitt, R., Goyal, Y., Beck, L. E., Symmons, O., Dunagin, M. C., Linares-Saldana, R., Shah, P. P., Pérez-Bermejo, J. A., et al. (2021) · Cell Systems · doi:10.1016/j.cels.2021.07.003 · Cited by 20

Single-Cell Toolkits Opening a New Era for Cell Engineering

Lee, S., Kim, J., & Park, J.-E. (2021) · Molecules and Cells · doi:10.14348/molcells.2021.0002 · Cited by 20

New horizons in the stormy sea of multimodal single-cell data integration

Jackson, C. A., & Vogel, C. (2022) · Molecular Cell · doi:10.1016/j.molcel.2021.12.012 · Cited by 21

Transcriptomic profiling of tissue environments critical for post-embryonic patterning and morphogenesis of zebrafish skin

Aman, A. J., Saunders, L. M., Carr, A. A., Srivatasan, S., Eberhard, C., Carrington, B., Watkins-Chow, D., Pavan, W. J., Trapnell, C., & Parichy, D. M. (2023) · eLife · doi:10.7554/elife.86670 · Cited by 30

High-Throughput Strategies for the Discovery of Anticancer Drugs by Targeting Transcriptional Reprogramming

Huang, L., Yi, X., Yu, X., Wang, Y., Zhang, C., Qin, L., Guo, D., Zhou, S., Zhang, G., Deng, Y., et al. (2021) · Frontiers in Oncology · doi:10.3389/fonc.2021.762023 · Cited by 19

GenePert: Leveraging GenePT Embeddings for Gene Perturbation PredictionPreprint

Chen, Y. T., & Zou, J. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.10.27.620513 · Cited by 24

Revealing gene function with statistical inference at single-cell resolution

Trapnell, C. (2024) · Nature Reviews Genetics · doi:10.1038/s41576-024-00750-w · Cited by 19

Single-Cell Epigenomics Reveals Mechanisms of Cancer Progression

LaFave, L. M., Savage, R. E., & Buenrostro, J. D. (2022) · Annual Review of Cancer Biology · doi:10.1146/annurev-cancerbio-070620-094453 · Cited by 20

Variant-to-function analysis of the childhood obesity chr12q13 locus implicates rs7132908 as a causal variant within the 3′ UTR of FAIM2

Littleton, S. H., Trang, B. K., Volpe, C. M., Cook, K., DeBruyne, N., Maguire, J. A., Weidekamp, M. A., Hodge, K. M., Boehm, K. D., Lu, S., et al. (2024) · Cell Genomics · doi:10.1016/j.xgen.2024.100556 · Cited by 20

scNym: Semi-supervised adversarial neural networks for single cell classificationPreprint

Kimmel, J. C., & Kelley, D. R. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.06.04.132324 · Cited by 19

2D profiling of tumor chemotactic and molecular phenotype at single cell resolution using a SERS-microfluidic chip

Zhang, Y., Wu, L., Yang, K., Zong, S., Wang, Z., & Cui, Y. (2022) · Nano Research · doi:10.1007/s12274-022-4100-5 · Cited by 18

Combinatorial perturbation sequencing on single cells using microwell-based droplet random pairing

Xie, R., Liu, Y., Wang, S., Shi, X., Zhao, Z., Liu, Y., Li, Z., & Li, Z. (2022) · Biosensors and Bioelectronics · doi:10.1016/j.bios.2022.114913 · Cited by 18

Dynamic protein deacetylation is a limited carbon source for acetyl-CoA–dependent metabolism

Soaita, I., Megill, E., Kantner, D. S., Chatoff, A. G., Cheong, Y. J., Clarke, P., Arany, Z. P., Snyder, N. W., Wellen, K. E., & Trefely, S. (2023) · Journal of Biological Chemistry · doi:10.1016/j.jbc.2023.104772 · Cited by 18

Multiparametric Assays for Accelerating Early Drug Discovery

Herholt, A., Galinski, S., Geyer, P. E., Rossner, M. J., & Wehr, M. C. (2020) · Trends in Pharmacological Sciences · doi:10.1016/j.tips.2020.02.005 · Cited by 18

Roles of steroid receptors in the lung and COVID-19

Leach, D. A., Brooke, G. N., & Bevan, C. L. (2021) · Essays in Biochemistry · doi:10.1042/ebc20210005 · Cited by 19

Scalable, multimodal profiling of chromatin accessibility and protein levels in single cellsPreprint

Mimitou, E. P., Lareau, C. A., Chen, K. Y., Zorzetto-Fernandes, A. L., Takeshima, Y., Luo, W., Huang, T.-S., Yeung, B. Z., Thakore, P. I., Wing, J. B., et al. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.09.08.286914 · Cited by 18

Optimal distance metrics for single-cell RNA-seq populationsPreprint

Ji, Y., Green, T. D., Peidli, S., Bahrami, M., Liu, M., Zappia, L., Hrovatin, K., Sander, C., & Theis, F. J. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.12.26.572833 · Cited by 22

Single‐cell proteomics by mass spectrometry: Advances and implications in cancer research

Tan, Y. C., Low, T. Y., Lee, P. Y., & Lim, L. C. (2024) · PROTEOMICS · doi:10.1002/pmic.202300210 · Cited by 17

Deep molecular, cellular and temporal phenotyping of developmental perturbations at whole organism scalePreprint

Saunders, L. M., Srivatsan, S. R., Duran, M., Dorrity, M. W., Ewing, B., Linbo, T. H., Shendure, J., Raible, D. W., Moens, C. B., Kimelman, D., et al. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.08.04.502764 · Cited by 17

Signals in the Cells: Multimodal and Contextualized Machine Learning Foundations for TherapeuticsPreprint

Velez-Arce, A., Lin, X., Li, M. M., Huang, K., Gao, W., Fu, T., Pentelute, B. L., Kellis, M., & Žitnik, M. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.06.12.598655 · Cited by 20

Disentangling cellular heterogeneity into interpretable biological factors through structured latent representationsPreprint

Moinfar, A. A., & Theis, F. J. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.11.06.622266 · Cited by 22

Technological and computational advances driving high-throughput oncology

Kolmar, L., Autour, A., Ma, X., Vergier, B., Eduati, F., & Merten, C. A. (2022) · Trends in Cell Biology · doi:10.1016/j.tcb.2022.04.008 · Cited by 16

Therapeutic HDAC inhibition in hypermutant diffuse intrinsic pontine glioma

Noll, A., Myers, C. H., Biery, M. C., Meechan, M., Tahiri, S., Rajendran, A., Berens, M. E., Paine, D., Byron, S. A., Zhang, J., et al. (2023) · Neoplasia · doi:10.1016/j.neo.2023.100921 · Cited by 16

Metabolic sinkholes: Histones as methyl repositories

Karimian, A., Vogelauer, M., & Kurdistani, S. K. (2023) · PLoS Biology · doi:10.1371/journal.pbio.3002371 · Cited by 16

Preparation of single-cell suspensions of mouse glomeruli for high-throughput analysis

Korin, B., Chung, J.-J., Avraham, S., & Shaw, A. S. (2021) · Nature Protocols · doi:10.1038/s41596-021-00578-2 · Cited by 17

LINEAGE: Label-free identification of endogenous informative single-cell mitochondrial RNA mutation for lineage analysis

Lin, L., Zhang, Y., Qian, W., Liu, Y., Zhang, Y., Lin, F., Liu, C., Lu, G., Sun, D., Guo, X., et al. (2022) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2119767119 · Cited by 16

Bioengineering translational models of lymphoid tissues

Michaels, Y. S., Buchanan, C. F., Gjorevski, N., & Moisan, A. (2023) · Nature Reviews Bioengineering · doi:10.1038/s44222-023-00101-0 · Cited by 16

Polypharmacology prediction: the long road toward comprehensively anticipating small-molecule selectivity to de-risk drug discovery

Manen-Freixa, L., & Antolín, A. A. (2024) · Expert Opinion on Drug Discovery · doi:10.1080/17460441.2024.2376643 · Cited by 15

Single-cell technologies and analyses in hematopoiesis and hematological malignancies

Campillo‐Marcos, I., Álvarez‐Errico, D., Alandes, R. A., Mereu, E., & Esteller, M. (2021) · Experimental Hematology · doi:10.1016/j.exphem.2021.05.001 · Cited by 14

EFMC: Trends in Medicinal Chemistry and Chemical Biology

Auberson, Y. P., Arimondo, P. B., Duca, M., Essig, S., Grether, U., Rufer, A. C., Sbardella, G., Schopfer, U., Torrens, A., van der Stelt, M., et al. (2023) · ChemBioChem · doi:10.1002/cbic.202200690 · Cited by 16

Quantifying orthogonal barcodes for sequence census assays

Booeshaghi, A. S., Min, K. H., Gehring, J., & Pachter, L. S. (2023) · Bioinformatics Advances · doi:10.1093/bioadv/vbad181 · Cited by 15

TMPRSS2 Transcriptional Inhibition as a Therapeutic Strategy for COVID-19Preprint

Wang, X., Dhindsa, R. S., Povysil, G., Zoghbi, A. W., Motelow, J. E., Hostyk, J. A., Nickols, N., Rettig, M. B., & Goldstein, D. B. (2020) · Preprints.org · doi:10.20944/preprints202003.0360.v2 · Cited by 15

Scalable and universal prediction of cellular phenotypes enables in silico experimentsPreprint

Ji, Y., Tejada-Lapuerta, A., Schmacke, N. A., Zheng, Z., Zhang, X., Khan, S., Rothenaigner, I., Tschuck, J., Hadian, K., Hornung, V., et al. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.08.12.607533 · Cited by 18

Predicting single-cell cellular responses to perturbations using cycle consistency learning

Huang, W., & Liu, H. (2024) · Bioinformatics · doi:10.1093/bioinformatics/btae248 · Cited by 14

Single-cell sequencing: expansion, integration and translation

Dai, X., Cai, L., & He, F. (2022) · Briefings in Functional Genomics · doi:10.1093/bfgp/elac011 · Cited by 13

Predicting drug responses of unseen cell types through transfer learning with foundation models

Wang, Y., Liu, X., Fan, Y., Xie, B., Cheng, J. S.-C., Wong, K. C., Cheung, P. P., King, I., & Li, Y. (2025) · Nature Computational Science · doi:10.1038/s43588-025-00887-6 · Cited by 14

A dynamical perspective: moving towards mechanism in single-cell transcriptomics

Maizels, R. J. (2024) · Philosophical Transactions of the Royal Society B Biological Sciences · doi:10.1098/rstb.2023.0049 · Cited by 17

Interpretation, extrapolation and perturbation of single cells

Dimitrov, D., Schrod, S., Rohbeck, M., & Stegle, O. (2026) · Nature Reviews Genetics · doi:10.1038/s41576-025-00920-4 · Cited by 26

Mass Spectrometry-based Solutions for Single-cell Proteomics

Li, S., Liu, S., Liu, S., & Ren, Y. (2025) · Genomics Proteomics & Bioinformatics · doi:10.1093/gpbjnl/qzaf012 · Cited by 12

Expanding horizons of CRISPR applications beyond genome editing

Liang, Y., Tong, S., Zhang, J., Tan, G., Zhang, L., Lee, S. Y., & Tong, Y. (2025) · Trends in Genetics · doi:10.1016/j.tig.2025.06.003 · Cited by 12

Latency reversal agents modulate HIV antigen processing and presentation to CD8 T cells

Boucau, J., Das, J., Joshi, N., & Gall, S. L. (2020) · PLoS Pathogens · doi:10.1371/journal.ppat.1008442 · Cited by 12

Advances in Single-Cell Multi-Omics and Application in Cardiovascular Research

Zhang, X., Qiu, H., Zhang, F., & Ding, S. (2022) · Frontiers in Cell and Developmental Biology · doi:10.3389/fcell.2022.883861 · Cited by 14

Advances in high-throughput drug screening based on pharmacotranscriptomics

Qiao, L., Huo, X., Huang, W., Wang, Z., Ren, Y., Liu, Y., Li, Q., & Zhang, Y. (2025) · Journal of Advanced Research · doi:10.1016/j.jare.2025.09.006 · Cited by 10

The optimization of combinatorial drug therapies: Strategies and laboratorial platforms

Wang, B., Warden, A. R., & Ding, X. (2021) · Drug Discovery Today · doi:10.1016/j.drudis.2021.07.023 · Cited by 11

Deciphering Drug Targets and Actions with Single-Cell and Spatial Resolution

Pang, Z., Cravatt, B. F., & Li, Y. (2023) · The Annual Review of Pharmacology and Toxicology · doi:10.1146/annurev-pharmtox-033123-123610 · Cited by 11

Review of Disentanglement Approaches for Medical Applications: Towards Solving the Gordian Knot of Generative Models in HealthcarePreprint

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Compressed Perturb-seq: highly efficient screens for regulatory circuits using random composite perturbationsPreprint

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