Inferring transcription factor regulatory networks from single-cell ATAC-seq data based on graph neural networks

Comprehensive Integration of Single-Cell Data

Stuart, T., Butler, A., Hoffman, P., Hafemeister, C., Papalexi, E., Mauck, W. M., Hao, Y., Stoeckius, M., Smibert, P., & Satija, R. (2019) · Cell · doi:10.1016/j.cell.2019.05.031 · Cited by 15,453 · see citing works

DeepWalk: online learning of social representations

Perozzi, B., Al-Rfou, R., & Skiena, S. (2014) · Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining · doi:10.1145/2623330.2623732 · Cited by 10,123 · see citing works

Scenic: single-cell regulatory network inference and clustering.

Aibar, S., González-Blas, C. B., Moerman, T., Huynh-Thu, V. A., Imrichova, H., Hulselmans, G., Rambow, F., Marine, J.-C., Geurts, P., Aerts, J., et al. (2017) · Nature Methods · doi:10.1038/nmeth.4463 · Cited by 6,520 · see citing works

Modeling Relational Data with Graph Convolutional Networks

Schlichtkrull, M., Kipf, T. N., Bloem, P., van den Berg, R., Titov, I., & Welling, M. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-93417-4_38 · Cited by 5,570 · see citing works

Knowledge Graph Embedding by Translating on Hyperplanes

Wang, Z., Zhang, J., Feng, J., & Chen, Z. (2014) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v28i1.8870 · Cited by 3,915 · see citing works

Link prediction in complex networks: A survey

Lü, L., & Zhou, T. (2010) · Physica A Statistical Mechanics and its Applications · doi:10.1016/j.physa.2010.11.027 · Cited by 2,841 · see citing works

Inferring Regulatory Networks from Expression Data Using Tree-Based Methods

Huynh-Thu, V. A., Irrthum, A., Wehenkel, L., & Geurts, P. (2010) · PLoS ONE · doi:10.1371/journal.pone.0012776 · Cited by 2,131 · see citing works

Large-scale mapping and validation of Escherichia coli transcriptional regulation from a compendium of expression profiles

Faith, J. J., Hayete, B., Thaden, J. T., Mogno, I., Wierzbowski, J., Cottarel, G., Kasif, S., Collins, J. J., & Gardner, T. S. (2007) · PLoS Biology · doi:10.1371/journal.pbio.0050008 · Cited by 1,563 · see citing works

Transcriptome-wide noise controls lineage choice in mammalian progenitor cells

Chang, H. H., Hemberg, M., Barahona, M., Ingber, D. E., & Huang, S. (2008) · Nature · doi:10.1038/nature06965 · Cited by 1,067 · see citing works

Integrative single-cell analysis of transcriptional and epigenetic states in the human adult brain

Lake, B. B., Chen, S., Sos, B. C., Fan, J., Kaeser, G. E., Yung, Y. C., Duong, T. E., Gao, D., Chun, J., Kharchenko, P. V., et al. (2018) · Nature Biotechnology · doi:10.1038/nbt.4038 · Cited by 983 · see citing works

Gaussian interaction profile kernels for predicting drug–target interaction

van Laarhoven, T., Nabuurs, S. B., & Marchiori, E. (2011) · Bioinformatics · doi:10.1093/bioinformatics/btr500 · Cited by 938 · see citing works

Benchmarking algorithms for gene regulatory network inference from single-cell transcriptomic data

Pratapa, A., Jalihal, A. P., Law, J. N., Bharadwaj, A., & Murali, T. M. (2020) · Nature Methods · doi:10.1038/s41592-019-0690-6 · Cited by 792 · see citing works

The origin and evolution of cell types

Arendt, D., Musser, J. M., Baker, C. V. H., Bergman, A., Cepko, C., Erwin, D. H., Pavlicev, M., Schlosser, G., Widder, S., Laubichler, M. D., et al. (2016) · Nature Reviews Genetics · doi:10.1038/nrg.2016.127 · Cited by 764 · see citing works

A Survey of Link Prediction in Complex Networks

Martínez, V., Berzal, F., & Cubero, J.-C. (2016) · ACM Computing Surveys · doi:10.1145/3012704 · Cited by 700 · see citing works

GRNBoost2 and Arboreto: efficient and scalable inference of gene regulatory networks

Moerman, T., Aibar Santos, S., Bravo González-Blas, C., Simm, J., Moreau, Y., Aerts, J., & Aerts, S. (2019) · Bioinformatics · doi:10.1093/bioinformatics/bty916 · Cited by 621 · see citing works

Transcription regulation by the Mediator complex

Soutourina, J. (2017) · Nature Reviews Molecular Cell Biology · doi:10.1038/nrm.2017.115 · Cited by 591 · see citing works

Gene Regulatory Network Inference from Single-Cell Data Using Multivariate Information Measures

Chan, T. E., Stumpf, M. P. H., & Babtie, A. C. (2017) · Cell Systems · doi:10.1016/j.cels.2017.08.014 · Cited by 578 · see citing works

Single-cell multiomic analysis identifies regulatory programs in mixed-phenotype acute leukemia

Granja, J. M., Klemm, S., McGinnis, L. M., Kathiria, A. S., Mezger, A., Corces, M. R., Parks, B., Gars, E., Liedtke, M., Zheng, G. X. Y., et al. (2019) · Nature Biotechnology · doi:10.1038/s41587-019-0332-7 · Cited by 498 · see citing works

Nonclassical Monocytes in Health and Disease

Narasimhan, P. B., Marcovecchio, P., Hamers, A. A. J., & Hedrick, C. C. (2019) · Annual Review of Immunology · doi:10.1146/annurev-immunol-042617-053119 · Cited by 475 · see citing works

Circuitry and Dynamics of Human Transcription Factor Regulatory Networks

Neph, S., Stergachis, A. B., Reynolds, A., Sandstrom, R., Borenstein, E., & Stamatoyannopoulos, J. A. (2012) · Cell · doi:10.1016/j.cell.2012.04.040 · Cited by 467 · see citing works

A human cell atlas of fetal chromatin accessibility

Domcke, S., Hill, A. J., Daza, R. M., Cao, J., O’Day, D. R., Pliner, H. A., Aldinger, K. A., Pokholok, D., Zhang, F., Milbank, J. H., et al. (2020) · Science · doi:10.1126/science.aba7612 · Cited by 375 · see citing works

The cis-regulatory dynamics of embryonic development at single-cell resolution

Cusanovich, D. A., Reddington, J. P., Garfield, D. A., Daza, R. M., Aghamirzaie, D., Marco-Ferreres, R., Pliner, H. A., Christiansen, L., Qiu, X., Steemers, F. J., et al. (2018) · Nature · doi:10.1038/nature25981 · Cited by 363 · see citing works

The genetic basis and cell of origin of mixed phenotype acute leukaemia

Alexander, T., Gu, Z., Iacobucci, I., Dickerson, K. M., Choi, J. K., Xu, B., Payne-Turner, D., Yoshihara, H., Loh, M. L., Horan, J., et al. (2018) · Nature · doi:10.1038/s41586-018-0436-0 · Cited by 360 · see citing works

Epigenetic polymorphism and the stochastic formation of differentially methylated regions in normal and cancerous tissues

Landan, G., Cohen, N. M., Mukamel, Z., Bar, A., Molchadsky, A., Brosh, R., Horn-Saban, S., Zalcenstein, D. A., Goldfinger, N., Zundelevich, A., et al. (2012) · Nature Genetics · doi:10.1038/ng.2442 · Cited by 305 · see citing works

Integration of ATAC-seq and RNA-seq identifies human alpha cell and beta cell signature genes

Ackermann, A. M., Wang, Z. P., Schug, J., Naji, A., & Kaestner, K. H. (2016) · Molecular Metabolism · doi:10.1016/j.molmet.2016.01.002 · Cited by 299 · see citing works

Lisa: inferring transcriptional regulators through integrative modeling of public chromatin accessibility and ChIP-seq data

Qin, Q., Fan, J., Zheng, R., Wan, C., Mei, S., Wu, Q., Sun, H., Zhang, J., Brown, M., Meyer, C. A., et al. (2020) · Genome biology · doi:10.1186/s13059-020-1934-6 · Cited by 287 · see citing works

The transcription factors T-bet and GATA-3 control alternative pathways of T-cell differentiation through a shared set of target genes

Jenner, R. G., Townsend, M. J., Jackson, I., Sun, K., Bouwman, R. D., Young, R. A., Glimcher, L. H., & Lord, G. M. (2009) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0909357106 · Cited by 252 · see citing works

Mapping gene regulatory networks from single-cell omics data

Fiers, M. W. E. J., Minnoye, L., Aibar, S., Bravo González-Blas, C., Kalender Atak, Z., & Aerts, S. (2018) · Briefings in Functional Genomics · doi:10.1093/bfgp/elx046 · Cited by 235 · see citing works

SINCERITIES: inferring gene regulatory networks from time-stamped single cell transcriptional expression profiles

Papili Gao, N., Ud-Dean, S. M. M., Gandrillon, O., & Gunawan, R. (2018) · Bioinformatics · doi:10.1093/bioinformatics/btx575 · Cited by 235 · see citing works

Chromatin accessibility profiling methods

Minnoye, L., Marinov, G. K., Krausgruber, T., Pan, L., Marand, A. P., Secchia, S., Greenleaf, W. J., Furlong, E. E. M., Zhao, K., Schmitz, R. J., et al. (2021) · Nature Reviews Methods Primers · doi:10.1038/s43586-020-00008-9 · Cited by 206 · see citing works

Gene regulatory programmes of tissue regeneration

Goldman, J. A., & Poss, K. D. (2020) · Nature Reviews Genetics · doi:10.1038/s41576-020-0239-7 · Cited by 200 · see citing works

A novel link prediction algorithm for reconstructing protein–protein interaction networks by topological similarity

Lei, C., & Ruan, J. (2012) · Bioinformatics · doi:10.1093/bioinformatics/bts688 · Cited by 196 · see citing works

LEAP: constructing gene co-expression networks for single-cell RNA-sequencing data using pseudotime ordering

Specht, A. T., & Li, J. (2017) · Bioinformatics · doi:10.1093/bioinformatics/btw729 · Cited by 193 · see citing works

Integrative analyses of single-cell transcriptome and regulome using MAESTRO

Wang, C., Sun, D., Huang, X., Wan, C., Li, Z., Han, Y., Qin, Q., Fan, J., Qiu, X., Xie, Y., et al. (2020) · Genome biology · doi:10.1186/s13059-020-02116-x · Cited by 183 · see citing works

An atlas of chromatin accessibility in the adult human brainPreprint

Fullard, J. F., Hauberg, M. E., Bendl, J. M., Egervári, G., Cirnaru, M., Reach, S. M., Motl, J., Ehrlich, M. E., Hurd, Y. L., & Roussos, P. (2018) · Genome Research · doi:10.1101/gr.232488.117 · Cited by 180 · see citing works

A Bayesian framework for the inference of gene regulatory networks from time and pseudo-time series data

Sanchez-Castillo, M., Blanco, D., Tienda-Luna, I. M., Carrion, M. C., & Huang, Y. (2018) · Bioinformatics · doi:10.1093/bioinformatics/btx605 · Cited by 151 · see citing works

Chromatin accessibility maps of chronic lymphocytic leukaemia identify subtype-specific epigenome signatures and transcription regulatory networks

Rendeiro, A. F., Schmidl, C., Strefford, J. C., Walewska, R., Davis, Z., Farlik, M., Oscier, D., & Bock, C. (2016) · Nature Communications · doi:10.1038/ncomms11938 · Cited by 146 · cited 2× · see citing works

Graph-based prediction of Protein-protein interactions with attributed signed graph embedding

Yang, F., Fan, K., Song, D., & Lin, H.-K. (2020) · BMC Bioinformatics · doi:10.1186/s12859-020-03646-8 · Cited by 144 · see citing works

scPower accelerates and optimizes the design of multi-sample single cell transcriptomic studies

Schmid, K. T., Höllbacher, B., Cruceanu, C., Böttcher, A., Lickert, H., Binder, E. B., Theis, F. J., & Heinig, M. (2021) · Nature Communications · doi:10.1038/s41467-021-26779-7 · Cited by 108 · cited 1× · see citing works

Leveraging chromatin accessibility for transcriptional regulatory network inference in T Helper 17 CellsPreprint

Miraldi, E. R., Pokrovskii, M. V., Watters, A., Castro, D. M., Veaux, N. D., Hall, J. A., Lee, J., Ciofani, M., Madar, A., Carriero, N., et al. (2019) · Genome Research · doi:10.1101/gr.238253.118 · Cited by 104 · see citing works

Reconstructing blood stem cell regulatory network models from single-cell molecular profiles

Hamey, F., Nestorowa, S., Kinston, S., Kent, D. G., Wilson, N. K., & Göttgens, B. (2017) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1610609114 · Cited by 102 · see citing works

Glioblastoma epigenome profiling identifies SOX10 as a master regulator of molecular tumour subtype

Wu, Y., Fletcher, M., Gu, Z., Wang, Q., Costa, B., Bertoni, A., Man, K.-H., Schlotter, M., Felsberg, J., Mangei, J., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-20225-w · Cited by 101 · see citing works

Master transcription factors form interconnected circuitry and orchestrate transcriptional networks in oesophageal adenocarcinoma

Chen, L., Huang, M., Plummer, J., Pan, J., Jiang, Y. Y., Yang, Q., Silva, T. C., Gull, N., Chen, S., Ding, L., et al. (2019) · Gut · doi:10.1136/gutjnl-2019-318325 · Cited by 93 · see citing works

Building gene regulatory networks from scATAC-seq and scRNA-seq using Linked Self Organizing Maps

Jansen, C. S., Ramírez, R. N., El-Ali, N. C., Gómez-Cabrero, D., Tegner, J. N., Merkenschlager, M., Conesa, A., & Mortazavi, A. (2019) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1006555 · Cited by 92 · see citing works

Inductive inference of gene regulatory network using supervised and semi-supervised graph neural networks

Wang, J., Ma, A., Ma, Q., Xu, D. H., & Joshi, T. (2020) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2020.10.022 · Cited by 87 · see citing works

CellOracle: Dissecting cell identity via network inference and in silico gene perturbation

Kamimoto, K., Hoffmann, C. M., & Morris, S. A. (2020) · doi:10.1101/2020.02.17.947416 · Cited by 81 · see citing works

Exploiting single-cell expression to characterize co-expression replicability

Crow, M., Paul, A., Ballouz, S., Huang, Z. J., & Gillis, J. (2016) · Genome Biology · doi:10.1186/s13059-016-0964-6 · Cited by 76 · see citing works

Definition of a small core transcriptional circuit regulated by AML1-ETO

Stengel, K. R., Ellis, J. D., Spielman, C., Bomber, M. L., & Hiebert, S. W. (2020) · Molecular Cell · doi:10.1016/j.molcel.2020.12.005 · Cited by 72 · see citing works

SCOUP: a probabilistic model based on the Ornstein–Uhlenbeck process to analyze single-cell expression data during differentiation

Matsumoto, H., & Kiryu, H. (2016) · BMC Bioinformatics · doi:10.1186/s12859-016-1109-3 · Cited by 71 · see citing works

Single-cell chromatin accessibility maps reveal regulatory programs driving early mouse organogenesis

Pijuan-Sala, B., Wilson, N. K., Xia, J., Hou, X., Hannah, R., Kinston, S., Calero‐Nieto, F. J., Poirion, O., Preißl, S., Liu, F., et al. (2020) · Nature Cell Biology · doi:10.1038/s41556-020-0489-9 · Cited by 71 · see citing works

Heterogeneous graph attention network based on meta-paths for lncRNA–disease association prediction

Zhao, X., Zhao, X., & Yin, M. (2021) · Briefings in Bioinformatics · doi:10.1093/bib/bbab407 · Cited by 67 · see citing works

Inferring gene regulatory networks by integrating ChIP-seq/chip and transcriptome data via LASSO-type regularization methods

Qin, J., Hu, Y., Xu, F., Yalamanchili, H. K., & Wang, J. (2014) · Methods · doi:10.1016/j.ymeth.2014.03.006 · Cited by 63 · see citing works

C/EBPγ deregulation results in differentiation arrest in acute myeloid leukemia

Alberich-Jordà, M., Wouters, B. J., Balaštík, M., Shapiro-Koss, C., Zhang, H., Ruscio, A. D., Radomska, H. S., Ebralidze, A. K., Amabile, G., Ye, M., et al. (2012) · Journal of Clinical Investigation · doi:10.1172/jci65102 · Cited by 59 · see citing works

Phenotype-driven gene prioritization for rare diseases using graph convolution on heterogeneous networks

Rao, A., Saipradeep, V. G., Joseph, T., Kotte, S., Sivadasan, N., & Srinivasan, R. (2018) · BMC Medical Genomics · doi:10.1186/s12920-018-0372-8 · Cited by 57 · see citing works

EWS-FLI1 regulates and cooperates with core regulatory circuitry in Ewing sarcoma

Shi, X., Zheng, Y., Jiang, L., Zhou, B. O., Yang, W., Li, L., Ding, L., Huang, M., Gery, S., Lin, D., et al. (2020) · Nucleic Acids Research · doi:10.1093/nar/gkaa901 · Cited by 54 · see citing works

Integration of single-cell multi-omics for gene regulatory network inference

Hu, X., Hu, Y., Wu, F., Leung, R. W. T., & Qin, J. (2020) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2020.06.033 · Cited by 53 · see citing works

Prostaglandin E1 and Its Analog Misoprostol Inhibit Human CML Stem Cell Self-Renewal via EP4 Receptor Activation and Repression of AP-1

Li, F., He, B., Ma, X., Yu, S., Bhave, R. R., Lentz, S. R., Tan, K., Guzmán, M. L., Zhao, C., & Xue, H. (2017) · Cell stem cell · doi:10.1016/j.stem.2017.08.001 · Cited by 46 · see citing works

Rational Targeting of Cooperating Layers of the Epigenome Yields Enhanced Therapeutic Efficacy against AML

Duy, C., Teater, M. R., Garrett-Bakelman, F. E., Lee, T. C., Meydan, C., Glass, J. L., Li, M., Hellmuth, J. C., Mohammad, H. P., Smitheman, K. N., et al. (2019) · Cancer Discovery · doi:10.1158/2159-8290.cd-19-0106 · Cited by 45 · see citing works

A KLF4-DYRK2–mediated pathway regulating self-renewal in CML stem cells

Park, C. S., Lewis, A. H., Chen, T. J., Bridges, C. S., Shen, Y., Suppipat, K., Puppi, M., Tomolonis, J., Pang, P. D., Mistretta, T., et al. (2019) · Blood · doi:10.1182/blood.2018875922 · Cited by 44 · see citing works

Network-principled deep generative models for designing drug combinations as graph sets

Karimi, M., Hasanzadeh, A., & Shen, Y. (2020) · Bioinformatics · doi:10.1093/bioinformatics/btaa317 · Cited by 43 · see citing works

Characterisation of CD4+ T-cell subtypes using single cell RNA sequencing and the impact of cell number and sequencing depth

Ding, J., Smith, S. L., Orozco, G., Barton, A. C., Eyre, S., & Martin, P. (2020) · Scientific Reports · doi:10.1038/s41598-020-76972-9 · Cited by 42 · see citing works

EZH2 -Deficient T-cell Acute Lymphoblastic Leukemia Is Sensitized to CHK1 Inhibition through Enhanced Replication Stress

León, T. E., Rapoz-D’Silva, T., Bertoli, C., Rahman, S., Magnussen, M., Philip, B., Farah, N., Richardson, S. E., Ahrabi, S., Guerra‐Assunção, J. A., et al. (2020) · Cancer Discovery · doi:10.1158/2159-8290.cd-19-0789 · Cited by 38 · see citing works

Derepression of the Iroquois Homeodomain Transcription Factor Gene IRX3 Confers Differentiation Block in Acute Leukemia

Somerville, T. D. D., Simeoni, F., Chadwick, J. A., Williams, E. L., Spencer, G. J., Boros, K., Wirth, C., Tholouli, E., Byers, R. J., & Somervaille, T. C. P. (2018) · Cell Reports · doi:10.1016/j.celrep.2017.12.063 · Cited by 32 · see citing works

Activation of Paired-homeobox gene PITX1 by del(5)(q31) in T-cell acute lymphoblastic leukemia

Nagel, S., Venturini, L., Przybylski, G. K., Grabarczyk, P., Schneider, B., Meyer, C., Kaufmann, M., Schmidt, C. A., Scherr, M., Drexler, H. G., et al. (2011) · Leukemia & lymphoma/Leukemia and lymphoma · doi:10.3109/10428194.2011.566391 · Cited by 28 · see citing works

Characterization of the genome-wide TLX1 binding profile in T-cell acute lymphoblastic leukemia

Durinck, K., Loocke, W. V., der Meulen, J. V., de Walle, I. V., Ongenaert, M., Rondou, P., Wallaert, A., de Bock, C. E., Roy, N. V., Poppe, B. G., et al. (2015) · Leukemia · doi:10.1038/leu.2015.162 · Cited by 27 · see citing works

Graph embedding ensemble methods based on the heterogeneous network for lncRNA-miRNA interaction prediction

Zhao, C., Qiu, Y., Zhou, S., Liu, S., Zhang, W., & Niu, Y. (2020) · BMC Genomics · doi:10.1186/s12864-020-07238-x · Cited by 24 · see citing works

ChIP-Array 2: integrating multiple omics data to construct gene regulatory networks

Wang, P., Qin, J., Qin, Y., Zhu, Y. K., Wang, L. Y., Li, M. J., Zhang, M. Q., & Wang, J. (2015) · Nucleic Acids Research · doi:10.1093/nar/gkv398 · Cited by 23 · see citing works

A Knowledge Selective Adversarial Network for Link Prediction in Knowledge Graph

Hu, K., Liu, H., & Hao, T. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-32233-5_14 · Cited by 19 · see citing works

Cross-talk between GLI transcription factors and FOXC1 promotes T-cell acute lymphoblastic leukemia dissemination

Tosello, V., Bongiovanni, D., Liu, J., Pan, Q., Yan, K., Saccomani, V., Trimpont, M. V., Pizzi, M., Mazzoni, M., Tos, A. P. D., et al. (2020) · Leukemia · doi:10.1038/s41375-020-0999-2 · Cited by 16 · see citing works

Gene Regulatory Networks

Davidson, E. H., & Peter, I. S. (2015) · Elsevier eBooks · doi:10.1016/b978-0-12-404729-7.00002-2 · Cited by 13 · see citing works

Single-nucleus chromatin accessibility reveals intratumoral epigenetic heterogeneity in IDH1 mutant gliomas

Al-Ali, R., Bauer, K., Park, J.-W., Abdulla, R. A., Fermi, V., von Deimling, A., Herold-Mende, C., Mallm, J.-P., Herrmann, C., Wick, W., et al. (2019) · Acta Neuropathologica Communications · doi:10.1186/s40478-019-0851-y · Cited by 12 · see citing works

Effects of Sample Size on Plant Single-Cell RNA Profiling

Chen, H., Lv, Y., Yin, X., Chen, X., Chu, Q., Zhu, Q., Fan, L., & Guo, L. (2021) · Current Issues in Molecular Biology · doi:10.3390/cimb43030119 · Cited by 6 · see citing works

sunyolo/DeepTFni:

Sunyolo (2022) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.6050542 · Cited by 1 · see citing works

A benchmark study of deep learning-based multi-omics data fusion methods for cancer

Leng, D., Zheng, L., Wen, Y., Zhang, Y., Wu, L., Wang, J., Wang, M., Zhang, Z., He, S., & Bo, X. (2022) · Genome biology · doi:10.1186/s13059-022-02739-2 · Cited by 163 · see citing works

Inferring gene regulatory networks from single-cell multiome data using atlas-scale external data

Yuan, Q., & Duren, Z. (2025) · Nature Biotechnology · doi:10.1038/s41587-024-02182-7 · Cited by 118 · see citing works

Gene regulatory network reconstruction: harnessing the power of single-cell multi-omic data

Kim, D. J., Tran, A., Kim, H. J., Lin, Y., Yang, J. Y. H., & Yang, P. (2023) · npj Systems Biology and Applications · doi:10.1038/s41540-023-00312-6 · Cited by 110 · see citing works

Single-cell omics: experimental workflow, data analyses and applications

Sun, F., Li, H., Sun, D., Fu, S., Gu, L., Shao, X., Wang, Q., Dong, X., Duan, B., Xing, F., et al. (2024) · Science China Life Sciences · doi:10.1007/s11427-023-2561-0 · Cited by 81 · see citing works

CGMega: explainable graph neural network framework with attention mechanisms for cancer gene module dissection

Li, H., Han, Z., Sun, Y., Wang, F., Hu, P., Gao, Y., Bai, X., Peng, S., Ren, C., Xu, X., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-50426-6 · Cited by 72 · see citing works

Advances and applications in single-cell and spatial genomics

Wang, J., Ye, F., Chai, H., Jiang, Y., Wang, T., Ran, X., Xia, Q., Xu, Z., Fu, Y., Zhang, G., et al. (2024) · Science China Life Sciences · doi:10.1007/s11427-024-2770-x · Cited by 69 · see citing works

STGRNS: an interpretable transformer-based method for inferring gene regulatory networks from single-cell transcriptomic data

Xu, J., Zhang, A., Liu, F., & Zhang, X. (2023) · Bioinformatics · doi:10.1093/bioinformatics/btad165 · Cited by 64 · see citing works

scButterfly: a versatile single-cell cross-modality translation method via dual-aligned variational autoencoders

Cao, Y., Zhao, X., Tang, S., Jiang, Q., Li, S., Li, S., & Chen, S. (2024) · Nature Communications · doi:10.1038/s41467-024-47418-x · Cited by 64 · see citing works

TRAPT: a multi-stage fused deep learning framework for predicting transcriptional regulators based on large-scale epigenomic data

Zhang, G., Song, C., Yin, M., Liu, L., Zhang, Y., Li, Y., Zhang, J., Guo, M., & Li, C. (2025) · Nature Communications · doi:10.1038/s41467-025-58921-0 · Cited by 55 · see citing works

Deep learning resilience inference for complex networked systems

Liu, C., Xu, F., Gao, C., Wang, Z., Li, Y., & Gao, J. (2024) · Nature Communications · doi:10.1038/s41467-024-53303-4 · Cited by 51 · see citing works

Deep learning-based cell-specific gene regulatory networks inferred from single-cell multiome data

Xu, J., Lu, C., Jin, S., Meng, Y., Fu, X., Zeng, X., Nussinov, R., & Cheng, F. (2025) · Nucleic Acids Research · doi:10.1093/nar/gkaf138 · Cited by 48 · see citing works

Prior knowledge-guided multilevel graph neural network for tumor risk prediction and interpretation via multi-omics data integration

Yan, H. H., Weng, D., Li, D., Gu, Y., Ma, W., & Liu, Q. (2024) · Briefings in Bioinformatics · doi:10.1093/bib/bbae184 · Cited by 44 · see citing works

Graph neural networks for single-cell omics data: a review of approaches and applications

Li, S., Hua, H., & Chen, S. (2025) · Briefings in Bioinformatics · doi:10.1093/bib/bbaf109 · Cited by 43 · see citing works

Artificial intelligence and machine learning applications for cultured meat

Todhunter, M. E., Jubair, S., Verma, R., Saqe, R., Shen, K., & Duffy, B. (2024) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2024.1424012 · Cited by 46 · see citing works

Engineered autonomous dynamic regulation of metabolic flux

Ream, M. J., & Prather, K. L. J. (2023) · Nature Reviews Bioengineering · doi:10.1038/s44222-023-00140-7 · Cited by 40 · see citing works

Rise of Deep Learning Clinical Applications and Challenges in Omics Data: A Systematic Review

Mohammed, M. A., Abdulkareem, K. H., Dinar, A. M., & García-Zapirain, B. (2023) · Diagnostics · doi:10.3390/diagnostics13040664 · Cited by 39 · see citing works

DeepFGRN: inference of gene regulatory network with regulation type based on directed graph embedding

Gao, Z., Su, Y., Xia, J., Cao, R., Ding, Y., Zheng, C.-H., & Wei, P.-J. (2024) · Briefings in Bioinformatics · doi:10.1093/bib/bbae143 · Cited by 31 · see citing works

Opportunities and challenges of graph neural networks in electrical engineering

Chien, E., Li, M., Aportela, A., Ding, K., Jia, S., Maji, S., Zhao, Z., Duarte, J. M., Fung, V., Hao, C., et al. (2024) · Nature Reviews Electrical Engineering · doi:10.1038/s44287-024-00076-z · Cited by 29 · see citing works

Graph neural network approaches for single-cell data: a recent overview

Lazaros, K., Koumadorakis, D. E., Vlamos, P., & Vrahatis, A. G. (2024) · Neural Computing and Applications · doi:10.1007/s00521-024-09662-6 · Cited by 27 · see citing works

Boosting single-cell gene regulatory network reconstruction via bulk-cell transcriptomic data

Shu, H., Ding, F., Zhou, J., Xue, Y., Zhao, D. Y., Zeng, J., & Ma, J. (2022) · Briefings in Bioinformatics · doi:10.1093/bib/bbac389 · Cited by 20 · see citing works

Deep Learning‐Based Multiomics Data Integration Methods for Biomedical Application

Wen, Y., Zheng, L., Leng, D., Dai, C., Jing, L., Zhang, Z., He, S., & Bo, X. (2023) · Advanced Intelligent Systems · doi:10.1002/aisy.202200247 · Cited by 20 · see citing works

Feature selection of microarray data using multidimensional graph neural network and supernode hierarchical clustering

Xie, W., Zhang, S., Wang, L., Yu, K., & Li, W. (2024) · Artificial Intelligence Review · doi:10.1007/s10462-023-10700-3 · Cited by 14 · see citing works

scTFBridge: a disentangled deep generative model informed by TF-motif binding for gene regulation inference in single-cell multi-omics

Wang, F., Yi, C., Chen, J., He, R., Liu, J., & Li, Y. (2025) · Nature Communications · doi:10.1038/s41467-025-64227-y · Cited by 12 · see citing works

Reusability report: exploring the utility of variational graph encoders for predicting molecular toxicity in drug design

Li, R., Lu, J., Liu, Z., Yi, D., Wan, M., Zhang, Y., Zan, P., He, S., & Bo, X. (2024) · Nature Machine Intelligence · doi:10.1038/s42256-024-00923-6 · Cited by 11 · see citing works

Inferring gene regulatory networks by hypergraph generative model

Su, G., Wang, H., Zhang, Y., Coster, A. C., Wilkins, M. R., Cañete, P. F., Yu, D., Yang, Y., & Zhang, W. (2025) · Cell Reports Methods · doi:10.1016/j.crmeth.2025.101026 · Cited by 14 · see citing works

Analyses of single-cell and bulk RNA sequencing combined with machine learning reveal the expression patterns of disrupted mitophagy in schizophrenia

Yang, W., Lian, K., Ye, J., Cheng, Y., & Xu, X. (2024) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2024.1429437 · Cited by 8 · see citing works

EpiCarousel: memory- and time-efficient identification of metacells for atlas-level single-cell chromatin accessibility data

Li, S., Li, Y., Sun, Y., Li, Y., Chen, X., Tang, S., & Chen, S. (2024) · Bioinformatics · doi:10.1093/bioinformatics/btae191 · Cited by 7 · see citing works

KEGNI: knowledge graph enhanced framework for gene regulatory network inference

Li, P., Li, L., Nan, J., Chen, J., Sun, J., & Cao, Y. (2025) · Genome biology · doi:10.1186/s13059-025-03780-7 · Cited by 6 · see citing works

Continuous lifelong learning for modeling of gene regulation from single cell multiome data by leveraging atlas-scale external dataPreprint

Yuan, Q., & Duren, Z. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.08.01.551575 · Cited by 6 · see citing works

GRDGNN: A directed graph neural network framework for multi-relational inference of gene regulatory networks

Zhang, W., & Guo, Z. (2025) · Expert Systems with Applications · doi:10.1016/j.eswa.2025.129827 · Cited by 6 · see citing works

MultiGNN: A Graph Neural Network Framework for Inferring Gene Regulatory Networks from Single-Cell Multi-Omics Data

Liu, D., Chen, H., Wang, J., & Wang, Y. (2025) · Computation · doi:10.3390/computation13050124 · Cited by 5 · see citing works

GAEDGRN: reconstruction of gene regulatory networks based on gravity-inspired graph autoencoders

Wei, P.-J., Jin, H.-W., Gao, Z., Su, Y., & Chun-Hou, Z. (2025) · Briefings in Bioinformatics · doi:10.1093/bib/bbaf232 · Cited by 5 · see citing works

Fast clustering and cell-type annotation of scATAC data using pre-trained embeddings

LeRoy, N. J., Smith, J. P., Zheng, G., Rymuza, J., Gharavi, E., Brown, D. E., Zhang, A., & Sheffield, N. C. (2024) · NAR Genomics and Bioinformatics · doi:10.1093/nargab/lqae073 · Cited by 5 · see citing works

ScReNI: Single-cell Regulatory Network Inference Through Integrating scRNA-seq and scATAC-seq Data

Xu, X., Liang, Y., Tang, M., Wang, J., Wang, X., Li, Y., & Wang, J. (2025) · Genomics Proteomics & Bioinformatics · doi:10.1093/gpbjnl/qzaf060 · Cited by 4 · see citing works

Bias-aware training and evaluation of link prediction algorithms in network biology

Yılmaz, S., Yorgancıoğlu, K., & Koyutürk, M. (2025) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2416646122 · Cited by 4 · see citing works

CellPolaris: Decoding Cell Fate through Generalization Transfer Learning of Gene Regulatory NetworksPreprint

Feng, G., Qin, X., Zhang, J., Huang, W., Zhang, Y., Cui, W., Li, S., Yao, C., Liu, W., Tian, Y., et al. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.09.25.559244 · Cited by 4 · see citing works

Single‐cell insights into plant growth, adaptation, and evolution

Wang, F., Wang, F., Wu, Y., Pu, L., & Le, L. (2026) · Journal of Integrative Plant Biology · doi:10.1111/jipb.70272 · Cited by 3 · see citing works

HyGAnno: hybrid graph neural network–based cell type annotation for single-cell ATAC sequencing data

Zhang, W., Cui, Y., Liu, B., Loza, M., Park, S., & Nakai, K. (2024) · Briefings in Bioinformatics · doi:10.1093/bib/bbae152 · Cited by 3 · see citing works

DuCGRN: A dual context-aware model for Gene Regulatory Network prediction from ScRNA-seq data

Dong, D., & Li, W. (2025) · Knowledge-Based Systems · doi:10.1016/j.knosys.2025.113724 · Cited by 3 · see citing works

Inference and prioritization of tissue-specific regulons in Arabidopsis and Oryza

Dai, H., Fan, Y., Mei, Y., Chen, L., & Gao, J. (2024) · aBIOTECH · doi:10.1007/s42994-024-00176-2 · Cited by 3 · see citing works

Constructing the dynamic transcriptional regulatory networks to identify phenotype-specific transcription regulators

Guo, Y., & Xiao, Z. (2024) · Briefings in Bioinformatics · doi:10.1093/bib/bbae542 · Cited by 3 · see citing works

Inferring gene regulatory networks by hypergraph variational autoencoderPreprint

Su, G., Wang, H., Zhang, Y., Coster, A. C. F., Wilkins, M. R., Cañete, P. F., Yu, D., Yang, Y., & Zhang, W. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.04.01.586509 · Cited by 3 · see citing works

TFcomb identifies transcription factor combinations for cellular reprogramming based on single-cell multiomics dataPreprint

Li, C., Chen, S., Chen, Y., Bian, H., Hao, M., Wei, L., & Zhang, X. (2025) · Genome Research · doi:10.1101/gr.279955.124 · Cited by 3 · see citing works

Genome-wide nucleosome and transcription factor responses to genetic perturbations reveal chromatin-mediated mechanisms of transcriptional regulationPreprint

Moyung, K., Li, Y., MacAlpine, H. K., Hartemink, A. J., & MacAlpine, D. M. (2025) · Genome Research · doi:10.1101/gr.279637.124 · Cited by 4 · see citing works

CLAMP: predicting specific protein-mediated chromatin loops in diverse species with a chromatin accessibility language model

He, Z., Sun, Y., Li, H., Sun, C., Yang, X., Chen, H., Liao, M., & Bo, X. (2026) · Genome biology · doi:10.1186/s13059-026-03948-9 · Cited by 2 · see citing works

MultiCausGRN: directed prior-guided graph attention model for multi-omics gene regulatory network inference

Alkhateeb, N. J., & Awad, M. (2026) · Frontiers in Bioinformatics · doi:10.3389/fbinf.2026.1883130 · Cited by 2 · see citing works

Effectiveness of machine learning at modeling the relationship between Hi‐C data and copy number variation

Wang, Y., Sun, Y., Liu, Z., Chen, B., Chen, H., Ren, C., Lin, X., Hu, P., Jia, P., Xu, X., et al. (2024) · Quantitative Biology · doi:10.1002/qub2.52 · Cited by 2 · see citing works

scDirect: key transcription factor identification for directing cell state transitions based on single-cell multi-omics dataPreprint

Li, C., Chen, S., Chen, Y., Bian, H., Hao, M., Wei, L., & Zhang, X. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.01.08.574757 · Cited by 2 · see citing works

Learning sequence-based regulatory dynamics in single-cell genomicsPreprint

Ibarra, I. L., Schneeberger, J., Erdogan, E., Redl, L., Martens, L. D., Klein, D., Aliee, H., & Theis, F. J. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.08.07.605876 · Cited by 2 · see citing works

A novel coarsened graph learning method for scalable single-cell data analysis

Kataria, M., Srivastava, E., Arjun, K., Kumar, S., Gupta, I., & Jayadeva (2025) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2025.109873 · Cited by 1 · see citing works

CellPolaris: Transfer Learning for Gene Regulatory Network Construction to Guide Cell State Transitions

Feng, G., Qin, X. Y., Zhang, J., Huang, W., Zhang, Y., Cui, W., Yao, C., Li, S., Liu, W., Tian, Y., et al. (2026) · Advanced Science · doi:10.1002/advs.202508697 · Cited by 5 · see citing works

Artificial intelligence and multiomics for longevity across species

Nie, C., Shi, Z., Song, Z., Wang, F., & Min, J. (2026) · Element · doi:10.20517/element.2026.01 · Cited by 1 · see citing works

Identifying fate-determining transcription factors with single-cell omics

Xi, X., Li, C., Wei, L., & Zhang, X. (2026) · Trends in Genetics · doi:10.1016/j.tig.2026.05.005 · Cited by 1 · see citing works

A comprehensive survey on graph neural networks for gene regulatory network inference

Alkhateeb, N. J., & Awad, M. (2026) · Briefings in Bioinformatics · doi:10.1093/bib/bbag443 · Cited by 1 · see citing works

Bioconvergence: Bridging Science and Nature to Shape Tomorrow

Siragusa, N., & Matz, L. (2024) · Springer eBooks · doi:10.1007/978-3-031-61593-1_1 · Cited by 1 · see citing works

Inferring Gene Regulatory Network Based on scATAC-seq Data with Gene Perturbation

Shao, W., Liu, Y., Zhang, S., Chen, S., Liu, Q., Zhou, J., & Zeng, W. (2024) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm62325.2024.10822868 · Cited by 1 · see citing works

Are under-studied proteins under-represented? How to fairly evaluate link prediction algorithms in network biologyPreprint

Yılmaz, S., Yorgancıoğlu, K., & Koyutürk, M. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.10.13.511953 · Cited by 1 · see citing works

TRAPT: A multi-stage fused deep learning framework for transcriptional regulators prediction via integrating large-scale epigenomic dataPreprint

Zhang, G., Song, C., Yin, M., Liu, L., Zhang, Y., Li, Y., Zhang, J., Guo, M., & Li, C. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.05.17.594242

GAGER: gene regulatory network assisted gene expression restorationPreprint

Chowdhury, M. Z. U. S., Any, S. S., Samee, M. A. H., & Rahman, A. H. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.11.27.625595

Predictive Modelling of Multimodal Single Cell Genomic Data with Machine Learning Algorithms

Khan, F., Rehman, J. U., & Alankar, B. (2025) · Algorithms for intelligent systems · doi:10.1007/978-981-97-8074-7_12

Cell-Type Annotation for scATAC-Seq Data by Integrating Chromatin Accessibility and Genome Sequence

Guo, W., Wang, L., Liu, Y., & Zhang, X. (2025) · Biomolecules · doi:10.3390/biom15070938

scPOEM: robust co-embedding of peaks and genes revealing peak–gene regulation

Zhong, Y., Hou, Y., Yang, Y., Zheng, X., & Cai, J. J. (2025) · Bioinformatics · doi:10.1093/bioinformatics/btaf483

Additional file 2 of A benchmark study of deep learning-based multi-omics data fusion methods for cancer

Leng, D., Zheng, L., Wen, Y., Zhang, Y., Wu, L., Wang, J., Wang, M., Zhang, Z., He, S., & Bo, X. (2022) · Figshare · doi:10.6084/m9.figshare.20461986

Additional file 1 of A benchmark study of deep learning-based multi-omics data fusion methods for cancer

Leng, D., Zheng, L., Wen, Y., Zhang, Y., Wu, L., Wang, J., Wang, M., Zhang, Z., He, S., & Bo, X. (2022) · Figshare · doi:10.6084/m9.figshare.20461983

spaNetReg: A Dependency-Aware Graph Neural Network for Inferring TRNS from Spatial ATAC-Seq

Miao, J., Hou, P., & Tian, T. (2025) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm66473.2025.11356838

Paradigm Shift and Application of Life Science Driven by Big Data and Artificial Intelligence

Li, X., Jiang, H., Liu, W., Fang, C., Li, C., Wang, H., Ma, X., & Wu, H. (2026) · Springer eBooks · doi:10.1007/978-981-96-8252-2_13

Using single cell atlas data to reconstruct regulatory networks

Song, Q., Ruffalo, M., & Bar‐Joseph, Z. (2023) · Nucleic Acids Research · doi:10.1093/nar/gkad053 · Cited by 21 · see citing works

spaNetReg: A Dependency-Aware Graph Neural Network for Inferring TRNS from Spatial ATAC-Seq

Miao, J., Hou, P., & Tian, T. (2025) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm66473.2025.11356838

Knowledge-Guided Machine Learning for Single-Cell Regulatory Genomics

Zandigohar, M. (2026) · University of Illinois Chicago · doi:10.25417/uic.32994986

scMGATGRN: a multiview graph attention network–based method for inferring gene regulatory networks from single-cell transcriptomic data

Yuan, L., Zhao, L., Jiang, Y., Shen, Z., Zhang, Q., Zhang, M., Zheng, C.-H., & Huang, D.-S. (2024) · Briefings in Bioinformatics · doi:10.1093/bib/bbae526 · Cited by 66 · see citing works

MultiCausGRN: directed prior-guided graph attention model for multi-omics gene regulatory network inference

Alkhateeb, N. J., & Awad, M. (2026) · Frontiers in Bioinformatics · doi:10.3389/fbinf.2026.1883130 · Cited by 2 · see citing works