Drug target prioritization by perturbed gene expression and network information
Open accessAbstract
Drugs bind to their target proteins, which interact with downstream effectors and ultimately perturb the transcriptome of a cancer cell. These perturbations reveal information about their source, i.e., drugs' targets. Here, we investigate whether these perturbations and protein interaction networks can uncover drug targets and key pathways. We performed the first systematic analysis of over 500 drugs from the Connectivity Map. First, we show that the gene expression of drug targets is usually not significantly affected by the drug perturbation. Hence, expression changes after drug treatment on their own are not sufficient to identify drug targets. However, ranking of candidate drug targets by network topological measures prioritizes the targets. We introduce a novel measure, local radiality, which combines perturbed genes and functional interaction network information. The new measure outperforms other methods in target prioritization and proposes cancer-specific pathways from drugs to affected genes for the first time. Local radiality identifies more diverse targets with fewer neighbors and possibly less side effects.
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The degree of toxicity would positively correlate with the degree of connectivity of the genetic network, because the number of affected genes would increase explosively as the complexity of the network increases.
“the degree of toxicity would positively correlate with the degree of connectivity of the genetic network, because the number of affected genes would increase explosively as the complexity of the network increases [137,138].”
From Machine Learning Based Toxicity Prediction: From Chemical Structural Description to Transcriptome Analysis (2018) · cites this paper as [137]
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Cytoscape: A software Environment for integrated models of biomolecular interaction networks
Shannon, P., Markiel, A., Ozier, O., Baliga, N. S., Wang, J. T., Ramage, D., Amin, N., Schwikowski, B., & Ideker, T. (2003) · Genome Research · doi:10.1101/gr.1239303 · Cited by 51,888 · see citing works
Berman, H. M. (2000) · Nucleic Acids Research · doi:10.1093/nar/28.1.235 · Cited by 40,196 · see citing works
An introduction to ROC analysis
Fawcett, T. (2006) · Pattern Recognition Letters · doi:10.1016/j.patrec.2005.10.010 · Cited by 21,879 · see citing works
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 · see citing works
Summaries of Affymetrix GeneChip probe level data
Irizarry, R. A. (2003) · Nucleic Acids Research · doi:10.1093/nar/gng015 · Cited by 4,906 · see citing works
STRING v9.1: protein-protein interaction networks, with increased coverage and integration
Franceschini, A., Szklarczyk, D., Frankild, S., Kuhn, M., Simonovic, M., Roth, A., Lin, J., Minguez, P., Bork, P., von Mering, C., et al. (2012) · Nucleic Acids Research · doi:10.1093/nar/gks1094 · Cited by 4,398 · see citing works
BindingDB: a web-accessible database of experimentally determined protein-ligand binding affinities
Liu, T., Lin, Y., Wen, X., Jorissen, R. N., & Gilson, M. K. (2006) · Nucleic Acids Research · doi:10.1093/nar/gkl999 · Cited by 2,145 · see citing works
Structure and dynamics of molecular networks: A novel paradigm of drug discovery
Csermely, P., Korcsmáros, T., Kiss, H., London, G., & Nussinov, R. (2013) · Pharmacology & Therapeutics · doi:10.1016/j.pharmthera.2013.01.016 · Cited by 889 · see citing works
Estimating the size of the human interactome
Stumpf, M. P. H., Thorne, T., de Silva, E., Stewart, R., An, H. J., Lappé, M., & Wiuf, C. (2008) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0708078105 · Cited by 827 · see citing works
Drug target validation and identification of secondary drug target effects using DNA microarrays
Marton, M. J., DeRisi, J. L., Bennett, H. A., Iyer, V. R., Meyer, M. R., Roberts, C. J., Stoughton, R. B., Burchard, J., Slade, D., Dai, H. A., et al. (1998) · Nature Medicine · doi:10.1038/3282 · Cited by 643 · see citing works
Zhu, J., Zhang, B., Smith, E. N., Drees, B., Brem, R. B., Kruglyak, L., Bumgarner, R. E., & Schadt, E. E. (2008) · Nature Genetics · doi:10.1038/ng.167 · Cited by 549 · see citing works
Drug–target interaction prediction by random walk on the heterogeneous network
Chen, X., Liu, M.-X., & Yan, G. (2012) · Molecular BioSystems · doi:10.1039/c2mb00002d · Cited by 532 · see citing works
Therapeutic target database update 2012: a resource for facilitating target-oriented drug discovery
Zhu, F., Shi, Z., Qin, C., Tao, L., Liu, X., Xu, F., Zhang, L., Song, Y., Liu, X., Zhang, J., et al. (2011) · Nucleic Acids Research · doi:10.1093/nar/gkr797 · Cited by 479 · see citing works
Cannistraci, C. V., Alanis‐Lobato, G., & Ravasi, T. (2013) · Scientific Reports · doi:10.1038/srep01613 · Cited by 404 · see citing works
Dirk Koschützki, A., & Schreiber, F. (2008) · Gene Regulation and Systems Biology · doi:10.4137/grsb.s702 · Cited by 344 · see citing works
PSICQUIC and PSISCORE: accessing and scoring molecular interactions
Aranda, B., Blankenburg, H., Kerrien, S., Brinkman, F. S. L., Ceol, A. G. M., Chautard, É., Dana, J. M., Rivas, J. D. L., Dumousseau, M., Galeota, E., et al. (2011) · Nature Methods · doi:10.1038/nmeth.1637 · Cited by 297 · see citing works
Yang, F., Tuxhorn, J. A., Ressler, S. J., McAlhany, S. J., Dang, T. D., & Rowley, D. R. (2005) · Cancer Research · doi:10.1158/0008-5472.can-05-1702 · Cited by 277 · see citing works
STITCH 3: zooming in on protein-chemical interactions
Kuhn, M., Szklarczyk, D., Franceschini, A., von Mering, C., Jensen, L. J., & Peer Bork, B. (2011) · Nucleic Acids Research · doi:10.1093/nar/gkr1011 · Cited by 275 · see citing works
Li, M., Zhang, H., Wang, J., & Pan, Y. F. (2012) · BMC Systems Biology · doi:10.1186/1752-0509-6-15 · Cited by 257 · see citing works
Drug Target Prediction and Repositioning Using an Integrated Network-Based Approach
Emig, D., Ivliev, A. E., Pustovalova, O., Lancashire, L., Bureeva, S. V., Nikolsky, Y., & Bessarabova, M. (2013) · PLoS ONE · doi:10.1371/journal.pone.0060618 · Cited by 242 · see citing works
Pathway databases and tools for their exploitation: benefits, current limitations and challenges
Bauer‐Mehren, A., Furlong, L. I., & Sanz, F. (2009) · Molecular Systems Biology · doi:10.1038/msb.2009.47 · Cited by 193 · see citing works
Fu, M., Zhang, J., Zhu, X., Myles, D., Willson, T. M., Liu, X., & Chen, Y. E. (2001) · Journal of Biological Chemistry · doi:10.1074/jbc.m105490200 · Cited by 170 · see citing works
Peng, W., Wang, J., Wang, W., Liu, Q., Wu, F., & Pan, Y. F. (2012) · BMC Systems Biology · doi:10.1186/1752-0509-6-87 · Cited by 151 · see citing works
Drug Promiscuity in PDB: Protein Binding Site Similarity Is Key
Haupt, V. J., Daminelli, S., & Schroeder, M. (2013) · PLoS ONE · doi:10.1371/journal.pone.0065894 · Cited by 148 · see citing works
Reka, A. K., Kurapati, H., Narala, V. R., Bommer, G. T., Chen, J., Standiford, T. J., & Keshamouni, V. G. (2010) · Molecular Cancer Therapeutics · doi:10.1158/1535-7163.mct-10-0570 · Cited by 136 · see citing works
Wang, J., Place, R. F., Huang, V., Wang, X., Noonan, E. J., Magyar, C. E., Huang, J., & Li, L.-C. (2010) · Cancer Research · doi:10.1158/0008-5472.can-10-2414 · Cited by 128 · see citing works
Iskar, M., Zeller, G., Blattmann, P., Campillos, M., Kuhn, M., Kaminska, K. H., Runz, H., Gavin, A., Pepperkok, R., van Noort, V., et al. (2013) · Molecular Systems Biology · doi:10.1038/msb.2013.20 · Cited by 118 · see citing works
Discovering pathways by orienting edges in protein interaction networks
Gitter, A., Klein‐Seetharaman, J., Gupta, A., & Bar‐Joseph, Z. (2010) · Nucleic Acids Research · doi:10.1093/nar/gkq1207 · Cited by 109 · see citing works
Schaefer, M. H., Lopes, T. J. S., Mah, N., Shoemaker, J. E., Matsuoka, Y., Fontaine, J., Louis‐Jeune, C., Eisfeld, A. J., Neumann, G., Perez‐Iratxeta, C., et al. (2013) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1002860 · Cited by 92 · see citing works
Network-based characterization of drug-regulated genes, drug targets, and toxicity
Kotlyar, M., Fortney, K., & Jurisica, I. (2012) · Methods · doi:10.1016/j.ymeth.2012.06.003 · Cited by 92 · see citing works
Das, R., Hammamieh, R., Neill, R. J., Melhem, M. F., & Jett, M. (2001) · PubMed · Cited by 79 · see citing works
Hirsch, J., Johnson, C. L., Nelius, T., Kennedy, R. C., de Riese, W. T. W., & Filleur, S. (2011) · Cytokine · doi:10.1016/j.cyto.2011.04.010 · Cited by 65 · see citing works
Tölle, A., Suhail, S., Jung, M., Jung, K., & Stephan, C. (2011) · BMC Cancer · doi:10.1186/1471-2407-11-302 · Cited by 64 · see citing works
Laenen, G., Thorrez, L., Börnigen, D., & Moreau, Y. (2013) · Molecular BioSystems · doi:10.1039/c3mb25438k · Cited by 62 · see citing works
The TissueNet database of human tissue protein–protein interactions
Barshir, R., Basha, O., Eluk, A., Smoly, I. Y., Lan, A., & Yeger‐Lotem, E. (2012) · Nucleic Acids Research · doi:10.1093/nar/gks1198 · Cited by 61 · see citing works
Drug repositioning through incomplete bi-cliques in an integrated drug–target–disease network
Daminelli, S., Haupt, V. J., Reimann, M., & Schroeder, M. (2012) · Integrative Biology · doi:10.1039/c2ib00154c · Cited by 56 · see citing works
Yeh, S.-H., Yeh, H., & Soo, V. (2012) · Journal of Clinical Bioinformatics · doi:10.1186/2043-9113-2-1 · Cited by 55 · see citing works
Utility of network integrity methods in therapeutic target identification
Peng, Q., & Schork, N. J. (2014) · Frontiers in Genetics · doi:10.3389/fgene.2014.00012 · Cited by 42 · see citing works
Li, S., Zhou, Q., He, H., Zhao, Y., & Liu, Z. (2012) · Journal of Biological Chemistry · doi:10.1074/jbc.m111.317487 · Cited by 40 · see citing works
SPECTRA: An Integrated Knowledge Base for Comparing Tissue and Tumor-Specific PPI Networks in Human
Micale, G., Ferro, A., Pulvirenti, A., & Giugno, R. (2015) · Frontiers in Bioengineering and Biotechnology · doi:10.3389/fbioe.2015.00058 · Cited by 18 · see citing works
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L1000CDS2: LINCS L1000 characteristic direction signatures search engine
Duan, Q., Reid, S. P., Clark, N. R., Wang, Z., Fernandez, N., Rouillard, A. D., Readhead, B., Tritsch, S. R., Hodos, R. A., Hafner, M. A., et al. (2016) · npj Systems Biology and Applications · doi:10.1038/npjsba.2016.15 · Cited by 359 · see citing works
An omics perspective on drug target discovery platforms
Paananen, J., & Fortino, V. (2019) · Briefings in Bioinformatics · doi:10.1093/bib/bbz122 · Cited by 207 · see citing works
Wu, Y., & Wang, G. (2018) · International Journal of Molecular Sciences · doi:10.3390/ijms19082358 · Cited by 193 · see citing works
Kotlyar, M., Pastrello, C., Malik, Z., & Jurišica, I. (2018) · Nucleic Acids Research · doi:10.1093/nar/gky1037 · Cited by 193 · see citing works
To Embed or Not: Network Embedding as a Paradigm in Computational Biology
Nelson, W., Žitnik, M., Wang, B., Leskovec, J., Goldenberg, A., & Sharan, R. (2019) · Frontiers in Genetics · doi:10.3389/fgene.2019.00381 · Cited by 159 · see citing works
A proteome-wide atlas of drug mechanism of action
Mitchell, D. C., Kuljanin, M., Li, J., Vranken, J. G. V., Bulloch, N. J., Schweppe, D. K., Huttlin, E. L., & Gygi, S. P. (2023) · Nature Biotechnology · doi:10.1038/s41587-022-01539-0 · Cited by 142 · see citing works
gene2drug: a computational tool for pathway-based rational drug repositioning
Napolitano, F., Carrella, D., Mandriani, B., Pisonero‐Vaquero, S., Sirci, F., Medina, D. L., Brunetti‐Pierri, N., & di Bernardo, D. (2017) · Bioinformatics · doi:10.1093/bioinformatics/btx800 · Cited by 110 · see citing works
JAK‐STAT core cancer pathway: An integrative cancer interactome analysis
Erdogan, F., Radu, T. B., Orlova, A., Qadree, A., de Araujo, E. D., Israelian, J., Valent, P., Mustjoki, S. M., Herling, M., Moriggl, R., et al. (2022) · Journal of Cellular and Molecular Medicine · doi:10.1111/jcmm.17228 · Cited by 109 · see citing works
Kotlyar, M., Pastrello, C., Ahmed, Z., Chee, J., Varyova, Z., & Jurišica, I. (2021) · Nucleic Acids Research · doi:10.1093/nar/gkab1034 · Cited by 83 · see citing works
A network-based approach to identify deregulated pathways and drug effects in metabolic syndrome
Misselbeck, K., Parolo, S., Lorenzini, F., Savoca, V., Leonardelli, L., Bora, P., Morine, M. J., Mione, M., Domenici, E., & Priami, C. (2019) · Nature Communications · doi:10.1038/s41467-019-13208-z · Cited by 73 · see citing works
Predicting protein targets for drug-like compounds using transcriptomics
Pabon, N. A., Xia, Y., Estabrooks, S. K., Ye, Z., Herbrand, A. K., Süß, E., Biondi, R. M., Assimon, V. A., Gestwicki, J. E., Brodsky, J. L., et al. (2018) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1006651 · Cited by 70 · see citing works
Durán, C., Daminelli, S., Thomas, J. M., Haupt, V. J., Schroeder, M., & Cannistraci, C. V. (2017) · Briefings in Bioinformatics · doi:10.1093/bib/bbx041 · Cited by 69 · see citing works
HEDD: the human epigenetic drug database
Qi, Y., Wang, D., Wang, D., Jin, T., Yang, L., Wu, H., Li, Y., Zhao, J., Du, F., Song, M., et al. (2016) · Database · doi:10.1093/database/baw159 · Cited by 64 · see citing works
Machine learning prediction of oncology drug targets based on protein and network properties
Dezső, Z., & Ceccarelli, M. (2020) · BMC Bioinformatics · doi:10.1186/s12859-020-3442-9 · Cited by 64 · see citing works
Bayesian Optimization Objective-Based Experimental Design
Imani, M., & Ghoreishi, S. F. (2020) · Proceedings of the ... American Control Conference/Proceedings of the American Control Conference · doi:10.23919/acc45564.2020.9147824 · Cited by 59 · see citing works
Connectivity Mapping: Methods and Applications
Keenan, A. B., Wojciechowicz, M. L., Wang, Z., Jagodnik, K. M., Jenkins, S. L., Lachmann, A., & Ma’ayan, A. (2019) · Annual Review of Biomedical Data Science · doi:10.1146/annurev-biodatasci-072018-021211 · Cited by 57 · see citing works
Ciucci, S., Ge, Y., Durán, C., Palladini, A., Jiménez-Jiménez, V., Martínez-Sánchez, L. M., Wang, Y., Sales, S., Shevchenko, A., Poser, S., et al. (2017) · Scientific Reports · doi:10.1038/srep43946 · Cited by 52 · see citing works
Noh, H., Shoemaker, J. E., & Gunawan, R. (2017) · Nucleic Acids Research · doi:10.1093/nar/gkx1314 · Cited by 50 · see citing works
In silico drug repositioning: from large-scale transcriptome data to therapeutics
Kwon, O., Kim, W., Cha, H., & Lee, H. (2019) · Archives of Pharmacal Research · doi:10.1007/s12272-019-01176-3 · Cited by 52 · see citing works
Sharma, A., Kitsak, M., Cho, M. H., Ameli, A., Zhou, X., Jiang, Z., Crapo, J. D., Beaty, T. H., Menche, J., Bakke, P. S., et al. (2018) · Scientific Reports · doi:10.1038/s41598-018-32173-z · Cited by 48 · see citing works
Biological Network Approaches and Applications in Rare Disease Studies
Zhang, P., & Itan, Y. (2019) · Genes · doi:10.3390/genes10100797 · Cited by 45 · see citing works
Li, C., Shao, X., Zhang, S., Wang, Y., Jin, K., Yang, P., Lu, X., Fan, X., & Wang, Y. (2024) · Cell Reports Medicine · doi:10.1016/j.xcrm.2024.101568 · Cited by 46 · see citing works
Ma, J., Wang, J., Ghoraie, L. S., Men, X., Haibe‐Kains, B., & Dai, P. (2019) · Frontiers in Pharmacology · doi:10.3389/fphar.2019.00109 · Cited by 43 · see citing works
Perspectives on Drug Repurposing
Schcolnik‐Cabrera, A., Juárez-López, D., & Dueñas‐González, A. (2020) · Current Medicinal Chemistry · doi:10.2174/0929867327666200831141337 · Cited by 43 · see citing works
Lee, H., & Kim, W. (2019) · Pharmaceutics · doi:10.3390/pharmaceutics11080377 · Cited by 39 · see citing works
RepTB: a gene ontology based drug repurposing approach for tuberculosis
Passi, A., Rajput, N. K., Wild, D. J., & Bhardwaj, A. (2018) · Journal of Cheminformatics · doi:10.1186/s13321-018-0276-9 · Cited by 36 · see citing works
Knowledge-guided gene prioritization reveals new insights into the mechanisms of chemoresistance
Emad, A., Cairns, J., Kalari, K. R., Wang, L., & Sinha, S. (2017) · Genome biology · doi:10.1186/s13059-017-1282-3 · Cited by 33 · see citing works
Liu, G., Singha, M., Pu, L., Neupane, P., Feinstein, J., Wu, H., Ramanujam, J., & Bryliński, M. (2021) · Journal of Cheminformatics · doi:10.1186/s13321-021-00540-0 · Cited by 32 · see citing works
Giménez, N., Tripathi, R., Giró, A., Rosich, L., López‐Guerra, M., López‐Oreja, I., Playà-Albinyana, H., Arenas, F., Mas, J. M., Pérez‐Galán, P., et al. (2020) · Scientific Reports · doi:10.1038/s41598-020-78315-0 · Cited by 32 · see citing works
GuiltyTargets: Prioritization of Novel Therapeutic Targets With Network Representation Learning
Muslu, Ö., Hoyt, C. T., Lacerda, M., Hofmann-Apitius, M., & Frohlich, H. (2020) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2020.3003830 · Cited by 28 · see citing works
Computational systems biology approaches for Parkinson’s disease
Glaab, E. (2017) · Cell and Tissue Research · doi:10.1007/s00441-017-2734-5 · Cited by 27 · see citing works
Exploring Drug Treatment Patterns Based on the Action of Drug and Multilayer Network Model
Yu, L., Shi, Y., Zou, Q., Wang, S., Zheng, L., & Gao, L. (2020) · International Journal of Molecular Sciences · doi:10.3390/ijms21145014 · Cited by 29 · see citing works
Sun, J., Xu, M., Ru, J., James-Bott, A., Xiong, D., Wang, X., & Cribbs, A. P. (2023) · European Journal of Medicinal Chemistry · doi:10.1016/j.ejmech.2023.115500 · Cited by 26 · see citing works
Cannistraci, C. V. (2018) · Scientific Reports · doi:10.1038/s41598-018-33576-8 · Cited by 24 · see citing works
Wang, Y., Sui, Y., Yao, J., Jiang, H., Tian, Q., Tang, Y., Ou, Y., Tang, J. P., & Tan, N. (2024) · Briefings in Bioinformatics · doi:10.1093/bib/bbae362 · Cited by 22 · see citing works
Narula, V., Zippo, A. G., Muscoloni, A., Biella, G. E. M., & Cannistraci, C. V. (2017) · Applied Network Science · doi:10.1007/s41109-017-0048-x · Cited by 21 · see citing works
Mohseni Behbahani, Y., Laine, E., & Carbone, A. (2023) · Bioinformatics · doi:10.1093/bioinformatics/btad231 · Cited by 23 · see citing works
Xiao, Y., Zhang, B., Cloyd, J. M., Alaimo, L., Xu, G., Du, S., Mao, Y., & Pawlik, T. M. (2022) · Cancers · doi:10.3390/cancers14133284 · Cited by 20 · see citing works
Network hub-node prioritization of gene regulation with intra-network association
Chang, H.-C., Chu, C.-P., Lin, S.-J., & Hsiao, C. K. (2020) · BMC Bioinformatics · doi:10.1186/s12859-020-3444-7 · Cited by 20 · see citing works
Hyperlipidemia, Disease Associations, and Top 10 Potential Drug Targets: A Network View
Rai, S., & Bhatnagar, S. (2016) · OMICS A Journal of Integrative Biology · doi:10.1089/omi.2015.0172 · Cited by 20 · see citing works
Applications of Machine Learning in Drug Target Discovery
Gao, D., Chen, Q., Zeng, Y., Jiang, M., & Zhang, Y. (2020) · Current Drug Metabolism · doi:10.2174/1567201817999200728142023 · Cited by 20 · see citing works
Pang, C. N. I., Lai, Y.-W., Campbell, L. T., Chen, S. C.-A., Carter, D. A., & Wilkins, M. R. (2017) · Scientific Reports · doi:10.1038/srep40232 · Cited by 19 · see citing works
FocusHeuristics – expression-data-driven network optimization and disease gene prediction
Ernst, M., Du, Y., Warsow, G., Hamed, M., Endlich, N., Endlich, K., Escobar, H. M., Sklarz, L.-M., Sender, S., Junghanß, C., et al. (2017) · Scientific Reports · doi:10.1038/srep42638 · Cited by 18 · see citing works
Network-based method for drug target discovery at the isoform level
Ma, J., Wang, J., Ghoraie, L. S., Men, X., Liu, L., & Dai, P. (2019) · Scientific Reports · doi:10.1038/s41598-019-50224-x · Cited by 16 · see citing works
Ke, R., Payne, P., & Li, F. (2017) · PubMed Central · Cited by 16 · see citing works
Manen-Freixa, L., & Antolín, A. A. (2024) · Expert Opinion on Drug Discovery · doi:10.1080/17460441.2024.2376643 · Cited by 15 · see citing works
Is the average shortest path length of gene set a reflection of their biological relatedness?
Embar, V., Handen, A. L., & Ganapathiraju, M. K. (2016) · Journal of Bioinformatics and Computational Biology · doi:10.1142/s0219720016600027 · Cited by 14 · see citing works
Multi-target drug repositioning by bipartite block-wise sparse multi-task learning
Li, L., He, X., & Borgwardt, K. M. (2018) · BMC Systems Biology · doi:10.1186/s12918-018-0569-7 · Cited by 14 · see citing works
Park, S., Lee, S., Pak, M., & Kim, S. (2024) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2024.3350083 · Cited by 13 · see citing works
Jiang, J.-C., Hu, C., McIntosh, A. M., & Shah, S. H. (2023) · Translational Psychiatry · doi:10.1038/s41398-023-02403-8 · Cited by 13 · see citing works
Gong, Y., Wang, L., Chippada-Venkata, U., Dai, X., Oh, W. K., & Zhu, J. (2016) · Oncotarget · doi:10.18632/oncotarget.11925 · Cited by 13 · see citing works
BEERE: a web server for biomedical entity expansion, ranking and explorations
Yue, Z., Willey, C., Hjelmeland, A., & Chen, J. (2019) · Nucleic Acids Res. · doi:10.1093/nar/gkz428 · Cited by 11 · see citing works
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Aguilar, D., Lemonnier, N., Koppelman, G. H., Melén, E., Oliva, B., Pinart, M., Guerra, S., Bousquet, J., & Antó, J. M. (2019) · PLoS ONE · doi:10.1371/journal.pone.0224448 · Cited by 13 · see citing works
Discovering anticancer drug target combinations via network-informed signaling-based approach
Yavuz, B. R., Jang, H., & Nussinov, R. (2025) · Communications Medicine · doi:10.1038/s43856-025-01150-9 · Cited by 12 · see citing works
Relationship between drug targets and drug-signature networks: a network-based genome-wide landscape
Lee, C. W., Kim, S., Sa, S., Hong, M., Nam, S. M., & Han, H. W. (2023) · BMC Medical Genomics · doi:10.1186/s12920-023-01444-8 · Cited by 12 · see citing works
Integration of RNA-Seq and RPPA data for survival time prediction in cancer patients
Işık, Z., & Ercan, M. E. (2017) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2017.08.028 · Cited by 12 · see citing works
Network-Based Approaches for Drug Target Identification
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ProphTools: general prioritization tools for heterogeneous biological networks
Navarro, C., Martínez, V., Blanco, A. J., & Cano, C. (2017) · GigaScience · doi:10.1093/gigascience/gix111 · Cited by 10 · see citing works
Novel Computational Methods for Cancer Drug Design
Talluri, S., Kamal, M. A., & Malla, R. R. (2023) · Current Medicinal Chemistry · doi:10.2174/0929867330666230403100008 · Cited by 10 · see citing works
Nájera, G. S., Narganes-Carlón, D., & Crowther, D. J. (2021) · Scientific Reports · doi:10.1038/s41598-021-94897-9 · Cited by 9 · see citing works
Application of Network Pharmacology Based on Artificial Intelligence Algorithms in Drug Development
Zhou, W., Li, X., Han, L., & Fan, S. (2021) · Springer eBooks · doi:10.1007/978-981-16-0753-0_2 · Cited by 10 · see citing works
A graph convolutional neural network for gene expression data analysis with multiple gene networks
Hu, Y., Zhuang, Z., & Pan, W. (2021) · Statistics in Medicine · doi:10.1002/sim.9140 · Cited by 10 · see citing works
Tran, T.-D., & Pham, D.-T. (2021) · Scientific Reports · doi:10.1038/s41598-021-93336-z · Cited by 10 · see citing works
Prabhakar, V., & Liu, K. (2023) · Archives of Clinical and Biomedical Research · doi:10.26502/acbr.50170342 · Cited by 8 · see citing works
Compound Target Identification in Tissue-Specific Interaction Networks
Dalkılıç, F., & Işık, Z. (2021) · IEEE Access · doi:10.1109/access.2021.3086051 · Cited by 8 · see citing works
Xiao, Y., Zhang, B., Cloyd, J. M., Xu, G., Du, S., Mao, Y., & Pawlik, T. M. (2022) · Surgical Oncology · doi:10.1016/j.suronc.2022.101849 · Cited by 8 · see citing works
Spendlove, M., Gibson, T. M., McCain, S., Stone, B. C., Gill, T., & Pickett, B. E. (2023) · PeerJ · doi:10.7717/peerj.16088 · Cited by 7 · see citing works
Sadeghi, M., Cava, C., Mousavi, P., Sabetian, S., & Morowvat, M. H. (2023) · Letters in Drug Design & Discovery · doi:10.2174/0115701808255183230922110002 · Cited by 8 · see citing works
Genomics-based tools for drug discovery and development: From network maps to efficacy prediction
Fang, J., Qi, C., & Wu, G. (2023) · Journal of Holistic Integrative Pharmacy · doi:10.1016/j.jhip.2023.11.001 · Cited by 8 · see citing works
GWAS and Beyond: Using Omics Approaches to Interpret SNP Associations
Chen, H., Petty, L. E., Bush, W. S., Naj, A. C., & Below, J. E. (2019) · Current Genetic Medicine Reports · doi:10.1007/s40142-019-0159-z · Cited by 8 · see citing works
Graphlet eigencentralities capture novel central roles of genes in pathways
Windels, S. F. L., Malod‐Dognin, N., & Pržulj, N. (2022) · PLoS ONE · doi:10.1371/journal.pone.0261676 · Cited by 8 · see citing works
Voitalov, I., Zhang, L., Kilpatrick, C. L., Withers, J. B., Saleh, A., Akmaev, V. R., & Ghiassian, S. D. (2022) · Scientific Reports · doi:10.1038/s41598-022-26276-x · Cited by 8 · see citing works
Asif, H. M. A., Kamal, S., Rehman, A.-U., Bibi, I., AlMasoud, N., Alomar, T. S., & Iqbal, M. (2023) · Arabian Journal of Chemistry · doi:10.1016/j.arabjc.2023.104626 · Cited by 8 · see citing works
Gene regulatory networks associated with lateral root and nodule development in soybean
Smita, S., Kiehne, J., Adhikari, S., Zeng, E., Ma, Q., & Subramanian, S. (2020) · in silico Plants · doi:10.1093/insilicoplants/diaa002 · Cited by 7 · see citing works
Bhardwaj, A., Josse, C., Daele, D. V., Poulet, C., Chavez, M., Struman, I., & Steen, K. V. (2022) · Scientific Reports · doi:10.1038/s41598-022-14592-1 · Cited by 7 · see citing works
Systems-Level Analysis of Bacterial Regulatory Small RNA Networks
Wong, J. L., Pang, C. N. I., Wilkins, M. R., & Tree, J. J. (2018) · RNA technologies · doi:10.1007/978-3-319-92967-5_6 · Cited by 7 · see citing works
Rath, S., Panda, S., Sacchettini, J. C., & Berthel, S. J. (2023) · ACS Pharmacology & Translational Science · doi:10.1021/acsptsci.3c00034 · Cited by 6 · see citing works
Kotlyar, M., Pastrello, C., Abovsky, M., Mizeranschi, A. E., Keating, A., Cameron, L. C., Chandran, V., & Jurišica, I. (2025) · Nucleic Acids Research · doi:10.1093/nar/gkaf1259 · Cited by 6 · see citing works
LEVELNET to visualize, explore, and compare protein–protein interaction networks
Behbahani, Y. M., Saighi, P., Corsi, F., Laine, É., & Carbone, A. (2023) · PROTEOMICS · doi:10.1002/pmic.202200159 · Cited by 6 · see citing works
Adaptive one-class Gaussian processes allow accurate prioritization of oncology drug targets
de Falco, A., Dezső, Z., Ceccarelli, F., Cerulo, L., Ciaramella, A., & Ceccarelli, M. (2020) · Bioinformatics · doi:10.1093/bioinformatics/btaa968 · Cited by 6 · see citing works
Network Propagation for the Analysis of Multi-omics Data
Pak, M., Jeong, D., Moon, J. H., Ann, H., Hur, B., Lee, S., & Kim, S. (2020) · Springer eBooks · doi:10.1007/978-3-030-57173-3_9 · Cited by 6 · see citing works
Target repositioning using multi-layer networks and machine learning: The case of prostate cancer
Picard, M., Scott‐Boyer, M., Bodein, A., Leclercq, M., Prunier, J., Périn, O., & Droit, A. (2024) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2024.06.012 · Cited by 5 · see citing works
Transcriptional changes in response to ketamine ester-analogs SN 35210 and SN 35563 in the rat brain
Jacobson, G. M., Voss, L. J., Klockars, A., Bird, S., Dimitrov, I., Denny, W. A., Olszewski, P. K., Sleigh, J. W., & Harvey, M. G. (2019) · BMC Genomics · doi:10.1186/s12864-019-5649-6 · Cited by 5 · see citing works
Networks and Pathways in Systems Pharmacology
Bezerianos, A., Dragomir, A., & Balomenos, P. (2017) · SpringerBriefs in computer science · doi:10.1007/978-3-319-53868-6_2 · Cited by 4 · see citing works
Kim, S. (2020) · Processes · doi:10.3390/pr8101230 · Cited by 4 · see citing works
Yadav, A. J., Bhagat, K., Sharma, A., & Padhi, A. K. (2025) · Advances in protein chemistry and structural biology · doi:10.1016/bs.apcsb.2024.10.011 · Cited by 3 · see citing works
Hart, C. E., Ence, D., Healey, D., & Domingo‐Fernándéz, D. (2023) · BMC Bioinformatics · doi:10.1186/s12859-023-05337-6 · Cited by 3 · see citing works
Network neighborhood operates as a drug repositioning method for cancer treatment
Cüvitoğlu, A., & Işık, Z. (2023) · PeerJ · doi:10.7717/peerj.15624 · Cited by 3 · see citing works
van Heerden, A., van Wyk, R., & Birkholtz, L. (2021) · Frontiers in Cellular and Infection Microbiology · doi:10.3389/fcimb.2021.688256 · Cited by 3 · see citing works
Existing Drug Repurposing for Glioblastoma to Discover Candidate Drugs as a New a Approach
Yang, B., Wang, X., Dong, D., Pan, Y., Wu, J., & Liu, J. (2021) · Letters in Drug Design & Discovery · doi:10.2174/1570180818666210509141735 · Cited by 3 · see citing works
Yue, Z., Yan, D., Guo, G.-M., & Chen, J. (2023) · Methods in molecular biology · doi:10.1007/978-1-0716-1534-8_8 · Cited by 3 · see citing works
Knowledge-guided gene prioritization reveals new insights into the mechanisms of chemoresistancePreprint
Emad, A., Woese, C. R., Cairns, J., Kalari, K. R., Liewei Wang, M. D., & Sinha, S. (2016) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/090027 · Cited by 3 · see citing works
Studying the drug treatment pattern based on the action of drug and multi-layer network modelPreprint
Yu, L., Shi, Y., Zou, Q., & Gao, L. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/780858 · Cited by 3 · see citing works
Anticancer Target Combinations: Network- Informed Signaling-Based Approach to DiscoveryPreprint
Yavuz, B. R., Jang, H., & Nussinov, R. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.10.11.617918 · Cited by 3 · see citing works
Noman, M. A., Latif, A., Ahmed, M. F., Islam, M. S., Pappu, M. A. A., Hossain, M. S., Hossen, M. B., Noor, T., Mollah, M. M. H., Kabir, M. H., et al. (2025) · Scientific Reports · doi:10.1038/s41598-025-23000-3 · Cited by 3 · see citing works
TREAP: A New Topological Approach to Drug Target Inference
Wang, M., Luciani, L. L., Noh, H., Mochan, E., & Shoemaker, J. E. (2020) · Biophysical Journal · doi:10.1016/j.bpj.2020.10.021 · Cited by 2 · see citing works
Systemic Efficacy of Sirolimus via the ERBB Signaling Pathway in Breast Cancer
Kim, S. (2022) · Processes · doi:10.3390/pr10030552 · Cited by 2 · see citing works
Innovative target mining stratagems to navigate drug repurposing endeavours
Saravanan, K. S., Satish, K. S., Saraswathy, G. R., Kuri, U., Vastrad, S. J., Giri, R., Dsouza, P. L., Kumar, A. P., & Nair, G. (2024) · Progress in molecular biology and translational science · doi:10.1016/bs.pmbts.2024.03.025 · Cited by 2 · see citing works
Parisutham, N., Deep, B., & Aswin, G. (2024) · Network Modeling Analysis in Health Informatics and Bioinformatics · doi:10.1007/s13721-024-00492-z · Cited by 2 · see citing works
Shao, B., Cannistraci, C. V., & Conrad, T. (2017) · Genomics and Computational Biology · doi:10.18547/gcb.2017.vol3.iss3.e57 · Cited by 2 · see citing works
- Authors
- Isik, Z., Baldow, C., Cannistraci, C. V., & Schroeder, M.
- Affiliations
- Technische Universität Dresden (DE)
- Biotechnologisches Zentrum (DE)
- Corresponding author
- Schroeder, Michael
- Venue
- Scientific Reports
- Published
- 2015-11-30
- Volume
- 5
- Issue
- 1
- Pages
- 17417-17417
- ISSN
- 2045-2322 (ISSN-L)
- PMC
- PMC4663505
- OpenAlex
- W2410743230
- Cited by
- 135
- References
- 40
- Citations by year
- 2026: 3 · 2025: 7 · 2024: 11 · 2023: 23 · 2022: 11 · 2021: 12 · 2020: 22 · 2019: 16 · 2018: 9 · 2017: 15 · 2016: 6
- FWCI
- 4.48
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Scientific Reports (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Funders
- Deutsche Forschungsgemeinschaft
- Technische Universität Dresden
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Biomedical Text Mining and Ontologies
- Keywords
- drug target prioritization, Connectivity Map, drug-induced perturbation, gene expression, protein-protein interaction network, cancer pathways, drug repurposing, adverse drug reactions
- MeSH terms
- Algorithms, Cell Line, Gene Expression Regulation, Humans, ROC Curve, Reproducibility of Results, Signal Transduction, Computational Biology, Gene Expression Profiling, Gene Regulatory Networks, Drug Discovery, Protein Interaction Maps
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