A community computational challenge to predict the activity of pairs of compounds

Scikit-learn: Machine Learning in Python

Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Müller, A., Nothman, J., Louppe, G., et al. (2012) · arXiv · doi:10.48550/arxiv.1201.0490 · Cited by 71,016 · see citing works

The meaning and use of the area under a receiver operating characteristic (ROC) curve.

Hanley, J. A., & McNeil, B. J. (1982) · Radiology · doi:10.1148/radiology.143.1.7063747 · Cited by 21,856 · see citing works

Gene selection for cancer classification using support vector machines

Guyon, I., Weston, J., Barnhill, S., & Vapnik, V. (2002) · Machine Learning · doi:10.1023/a:1012487302797 · Cited by 9,954 · see citing works

Platinum-Based Chemotherapy plus Cetuximab in Head and Neck Cancer

Vermorken, J. B., Mesia, R., Rivera, F., Remenar, E., Kawecki, A., Rottey, S., Erfan, J., Zabolotnyy, D., Kienzer, H.-R., Cupissol, D., et al. (2008) · New England Journal of Medicine · doi:10.1056/nejmoa0802656 · Cited by 3,726 · see citing works

Circular binary segmentation for the analysis of array-based DNA copy number data

Olshen, A. B., Venkatraman, E. S., Lucito, R., & Wigler, M. H. (2004) · Biostatistics · doi:10.1093/biostatistics/kxh008 · Cited by 2,256 · see citing works

Addiction to Oncogenes--the Achilles Heal of Cancer

Weinstein, I. (2002) · Science · doi:10.1126/science.1073096 · Cited by 1,753 · see citing works

Wisdom of crowds for robust gene network inference

The DREAM5 Consortium, Marbach, D., Costello, J. C., Küffner, R., Vega, N. M., Prill, R. J., Camacho, D. M., Allison, K. R., Kellis, M., Collins, J. J., et al. (2012) · Nature Methods · doi:10.1038/nmeth.2016 · Cited by 1,667 · see citing works

Fluorouracil, Leucovorin, and Oxaliplatin With and Without Cetuximab in the First-Line Treatment of Metastatic Colorectal Cancer

Bokemeyer, C., Bondarenko, I., Makhson, A. M., Hartmann, J. T., Aparicio, J., de Braud, F., Donea, S., Ludwig, H., Schuch, G., Stroh, C., et al. (2008) · Journal of Clinical Oncology · doi:10.1200/jco.2008.20.8397 · Cited by 1,629 · see citing works

Integrative analysis reveals selective 9p24.1 amplification, increased PD-1 ligand expression, and further induction via JAK2 in nodular sclerosing Hodgkin lymphoma and primary mediastinal large B-cell lymphoma

Green, M. R., Monti, S., Rodig, S. J., Juszczyński, P., Currie, T., O'Donnell, E., Chapuy, B., Takeyama, K., Neuberg, D. S., Golub, T. R., et al. (2010) · Blood · doi:10.1182/blood-2010-05-282780 · Cited by 1,307 · see citing works

The transcriptional network for mesenchymal transformation of brain tumours

Carro, M. S., Lim, W. K., Alvarez, M. J., Bollo, R. J., Zhao, X., Snyder, E. Y., Sulman, E. P., Anne, S. L., Doetsch, F. K., Colman, H., et al. (2009) · Nature · doi:10.1038/nature08712 · Cited by 1,242 · see citing works

Combinatorial drug therapy for cancer in the post-genomic era

Al-Lazikani, B., Banerji, U., & Workman, P. (2012) · Nature Biotechnology · doi:10.1038/nbt.2284 · Cited by 1,070 · see citing works

Mechanisms of drug combinations: interaction and network perspectives

Jia, J., Zhu, F., Ma, X., Cao, Z., Li, Y. X., & Chen, Y. (2009) · Nature Reviews Drug Discovery · doi:10.1038/nrd2683 · Cited by 975 · see citing works

Synergistic drug combinations tend to improve therapeutically relevant selectivity

Lehár, J., Krueger, A., Avery, W., Heilbut, A. M., Johansen, L. M., Price, E. R., Rickles, R. J., Short, G. F., Staunton, J. E., Jin, X., et al. (2009) · Nature Biotechnology · doi:10.1038/nbt.1549 · Cited by 941 · see citing works

PREDICT: a method for inferring novel drug indications with application to personalized medicine

Gottlieb, A., Stein, G. Y., Ruppin, E., & Sharan, R. (2011) · Molecular Systems Biology · doi:10.1038/msb.2011.26 · Cited by 892 · see citing works

Multicomponent therapeutics for networked systems

Keith, C. T., Borisy, A. A., & Stockwell, B. R. (2005) · Nature Reviews Drug Discovery · doi:10.1038/nrd1609 · Cited by 769 · see citing works

Activation of ERBB2 signaling causes resistance to the EGFR-directed therapeutic antibody cetuximab

Yonesaka, K., Zejnullahu, K., Okamoto, I., Satoh, T., Cappuzzo, F., Souglakos, J., Ercan, D., Rogers, A., Roncalli, M., Takeda, M., et al. (2011) · Science Translational Medicine · doi:10.1126/scitranslmed.3002442 · Cited by 663 · see citing works

Systematic discovery of multicomponent therapeutics

Borisy, A. A., Elliott, P. J., Hurst, N. W., Lee, M. S., Lehár, J., Price, E. R., Serbedzija, G., Zimmermann, G. R., Foley, M. A., Stockwell, B. R., et al. (2003) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1337088100 · Cited by 620 · see citing works

PI3K Inhibition Impairs BRCA1/2 Expression and Sensitizes BRCA-Proficient Triple-Negative Breast Cancer to PARP Inhibition

Ibrahim, Y. H., García-García, C., Serra, V., He, L., Torres‐Lockhart, K., Prat, A., Antón, P., Cozar, P., Guzmán, M., Grueso, J., et al. (2012) · Cancer Discovery · doi:10.1158/2159-8290.cd-11-0348 · Cited by 609 · see citing works

Systems biology and combination therapy in the quest for clinical efficacy

Fitzgerald, J. B., Schoeberl, B., Nielsen, U. B., & Sorger, P. K. (2006) · Nature Chemical Biology · doi:10.1038/nchembio817 · Cited by 570 · see citing works

Genetic heterogeneity of diffuse large B-cell lymphoma

Zhang, J., Grubor, V. D., Love, C., Banerjee, A., Richards, K. L., Mieczkowski, P. A., Dunphy, C. H., Choi, W., Au, W. Y., Srivastava, G., et al. (2013) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1205299110 · Cited by 527 · see citing works

Towards a rigorous assessment of systems biology models: the DREAM3 challenges

Prill, R. J., Marbach, D., Saez-Rodriguez, J., Sorger, P. K., Alexopoulos, L. G., Xue, X., Clarke, N. D., Altan-Bonnet, G., & Stolovitzky, G. (2010) · PLoS ONE · doi:10.1371/journal.pone.0009202 · Cited by 439 · see citing works

Cell cycle-mediated drug resistance: an emerging concept in cancer therapy.

Shah, M. A., & Schwartz, G. K. (2001) · PubMed · Cited by 437 · see citing works

The Sequencing of Chemotherapy and Radiation Therapy after Conservative Surgery for Early-Stage Breast Cancer

Recht, A., Come, S. E., Henderson, I. C., Gelman, R. S., Silver, B., Hayes, D. F., Shulman, L. N., & Harris, J. R. (1996) · New England Journal of Medicine · doi:10.1056/nejm199605233342102 · Cited by 436 · see citing works

Nearest Template Prediction: A Single-Sample-Based Flexible Class Prediction with Confidence Assessment

Hoshida, Y. (2010) · PLoS ONE · doi:10.1371/journal.pone.0015543 · Cited by 382 · see citing works

A human B‐cell interactome identifies MYB and FOXM1 as master regulators of proliferation in germinal centers

Lefèbvre, C., Rajbhandari, P., Alvarez, M. J., Bandaru, P., Lim, W. K., Sato, M., Wang, K., Sumazin, P., Kustagi, M., Bisikirska, B. C., et al. (2010) · Molecular Systems Biology · doi:10.1038/msb.2010.31 · Cited by 372 · see citing works

Cross-Species Regulatory Network Analysis Identifies a Synergistic Interaction between FOXM1 and CENPF that Drives Prostate Cancer Malignancy

Aytés, À., Mitrofanova, A., Lefèbvre, C., Alvarez, M. J., Castillo-Martín, M., Zheng, T., Eastham, J. A., Gopalan, A., Pienta, K. J., Shen, M. M., et al. (2014) · Cancer Cell · doi:10.1016/j.ccr.2014.03.017 · Cited by 319 · see citing works

Systematic exploration of synergistic drug pairs

Cokol, M., Chua, H. N., Tasan, M., Mutlu, B., Weinstein, Z., Suzuki, Y., Nergiz, M. E., Costanzo, M., Baryshnikova, A., Giaever, G. N., et al. (2011) · Molecular Systems Biology · doi:10.1038/msb.2011.71 · Cited by 308 · see citing works

Drug—drug interaction through molecular structure similarity analysis

Vilar, S., Harpaz, R., Uriarte, E., Santana, L., Rabadán, R., & Friedman, C. (2012) · Journal of the American Medical Informatics Association · doi:10.1136/amiajnl-2012-000935 · Cited by 290 · see citing works

Direct reversal of glucocorticoid resistance by AKT inhibition in acute lymphoblastic leukemia

Piovan, E., Yu, J.-Y., Tosello, V., Herranz, D., Ambesi-Impiombato, A., da Silva, A. D., Sanchez-Martin, M., Pérez-García, A., Rigo, I., Castillo, M., et al. (2013) · Cancer Cell · doi:10.1016/j.ccr.2013.10.022 · Cited by 259 · see citing works

Synergism of EGFR and c-Met pathways, cross-talk and inhibition, in non-small cell lung cancer

Puri, N., & Salgia, R. (2008) · Journal of Carcinogenesis · doi:10.4103/1477-3163.44372 · Cited by 210 · see citing works

Prediction of Drug Combinations by Integrating Molecular and Pharmacological Data

Zhao, X., Iskar, M., Zeller, G., Kuhn, M., van Noort, V., & Peer Bork, B. (2011) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1002323 · Cited by 201 · see citing works

Improved Disorder Prediction by Combination of Orthogonal Approaches

Schlessinger, A., Punta, M., Yachdav, G., Kaján, L., & Rost, B. (2009) · PLoS ONE · doi:10.1371/journal.pone.0004433 · Cited by 177 · see citing works

ZFHX4 Interacts with the NuRD Core Member CHD4 and Regulates the Glioblastoma Tumor-Initiating Cell State

Chudnovsky, Y., Kim, D., Zheng, S., Whyte, W. A., Bansal, M., Bray, M.-A., Gopal, S., Theisen, M. A., Bilodeau, S., Thiru, P., et al. (2014) · Cell Reports · doi:10.1016/j.celrep.2013.12.032 · Cited by 136 · see citing works

Systems Pharmacology of Adverse Event Mitigation by Drug Combinations

Zhao, S., Nishimura, T., Chen, Y., Azeloglu, E. U., Gottesman, O., Giannarelli, C., Zafar, M. U., Bénard, L., Badimón, J. J., Hajjar, R. J., et al. (2013) · Science Translational Medicine · doi:10.1126/scitranslmed.3006548 · Cited by 135 · see citing works

Suppression of Signal Transducer and Activator of Transcription 3 Activation by Butein Inhibits Growth of Human Hepatocellular Carcinoma In Vivo

Rajendran, P., Ong, T. H., Chen, L.-X., Li, F., Shanmugam, M. K., Vali, S., Abbasi, T., Kapoor, S., Sharma, A., Kumar, A., et al. (2010) · Clinical Cancer Research · doi:10.1158/1078-0432.ccr-10-1123 · Cited by 135 · see citing works

Systematic Analysis of Challenge-Driven Improvements in Molecular Prognostic Models for Breast Cancer

Margolin, A. A., Bilal, E., Huang, E. S., Norman, T., Ottestad, L., Mecham, B. H., Sauerwine, B., Kellen, M., Mangravite, L. M., Furia, M. D., et al. (2013) · Science Translational Medicine · doi:10.1126/scitranslmed.3006112 · Cited by 122 · see citing works

A mathematical model of combination therapy using the EGFR signaling network

Araujo, R. P., Petricoin, E. F., & Liotta, L. A. (2004) · Biosystems · doi:10.1016/j.biosystems.2004.10.002 · Cited by 95 · see citing works

The self‐assessment trap: can we all be better than average?

Norel, R., Rice, J. J., & Stolovitzky, G. A. (2011) · Molecular Systems Biology · doi:10.1038/msb.2011.70 · Cited by 93 · see citing works

Advair: Combination treatment with fluticasone propionate/salmeterol in the treatment of asthma

Nelson, H. S. (2001) · Journal of Allergy and Clinical Immunology · doi:10.1067/mai.2001.112939 · Cited by 87 · see citing works

5-fluorouracil-methotrexate synergy: enhancement of 5-fluorodeoxyridylate binding to thymidylate synthase by dihydropteroylpolyglutamates.

Fernandes, D., & Bertino, J. R. (1980) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.77.10.5663 · Cited by 70 · see citing works

Combining QSAR classification models for predictive modeling of human monoamine oxidase inhibitors

Helguera, A. M., Pérez-Garrido, A., Gaspar, A., Reis, J., Cagide, F., Viña, D., Cordeiro, M. N. D. S., & Borges, F. (2012) · European Journal of Medicinal Chemistry · doi:10.1016/j.ejmech.2012.10.035 · Cited by 62 · see citing works

An enhanced Petri-net model to predict synergistic effects of pairwise drug combinations from gene microarray data

Jin, G., Zhao, H., Zhou, X., & Wong, S. T. C. (2011) · Bioinformatics · doi:10.1093/bioinformatics/btr202 · Cited by 51 · see citing works

γH2AX and Chk1 phosphorylation as predictive pharmacodynamic biomarkers of Chk1 inhibitor-chemotherapy combination treatments

Rawlinson, R., & Massey, A. J. (2014) · BMC Cancer · doi:10.1186/1471-2407-14-483 · Cited by 34 · see citing works

Amoxicillin-potassium clavulanate, a β-lactamase-resistant antibiotic combination

Stein, G. E., & Gurwith, M. (1984) · American Journal of Health-System Pharmacy · doi:10.1093/ajhp/41.12.2685 · Cited by 26 · see citing works

A ligand's-eye view of protein similarity

van Westen, G. J. P., & Overington, J. P. (2013) · Nature Methods · doi:10.1038/nmeth.2339 · Cited by 20 · see citing works

Function-Based Discovery of Significant Transcriptional Temporal Patterns in Insulin Stimulated Muscle Cells

Camillo, B. D., Irving, B. A., Schimke, J. M., Sanavia, T., TOFFOLO, G. M., Cobelli, C., & Nair, K. S. (2012) · PLoS ONE · doi:10.1371/journal.pone.0032391 · Cited by 12 · see citing works

A robust meta-classification strategy for cancer diagnosis from gene expression data

Alexe, G., Bhanot, G. V., Venkataraghavan, B., Ramaswamy, R., Lepre, J., Levine, A. J., & Stolovitzky, G. A. (2005) · IEEE Computational Systems Bioinformatics Conference (CSB) · doi:10.1109/csb.2005.7 · Cited by 10 · see citing works

Untitled work

Harrell, F., Jr. (1996) · doi:10.1002/(sici)1097-0258(19960229)15:4

Modeling polypharmacy side effects with graph convolutional networks

Zitnik, M., Agrawal, M., & Leskovec, J. (2018) · Bioinformatics · doi:10.1093/bioinformatics/bty294 · Cited by 1,501 · see citing works

Next-Generation Machine Learning for Biological Networks

Camacho, D. M., Collins, K. M., Powers, R. K., Costello, J. C., & Collins, J. J. (2018) · Cell · doi:10.1016/j.cell.2018.05.015 · Cited by 974 · see citing works

Drug combinations: a strategy to extend the life of antibiotics in the 21st century

Tyers, M., & Wright, G. D. (2019) · Nature Reviews Microbiology · doi:10.1038/s41579-018-0141-x · Cited by 825 · see citing works

Network-based prediction of drug combinations

Cheng, F., Kovács, I. A., & Barabási, A.-Ĺ. (2019) · Nature Communications · doi:10.1038/s41467-019-09186-x · Cited by 812 · see citing works

Toward understanding and exploiting tumor heterogeneity

Alizadeh, A. A., Aranda, V., Bardelli, A., Blanpain, C., Bock, C., Borowski, C., Caldas, C., Califano, A., Doherty, M., Elsner, M., et al. (2015) · Nature Medicine · doi:10.1038/nm.3915 · Cited by 742 · see citing works

Machine learning for integrating data in biology and medicine: principles, practice, and opportunities

Zitnik, M., Nguyen, F., Wang, B., Leskovec, J., Goldenberg, A., & Hoffman, M. M. (2019) · Information Fusion · doi:10.1016/j.inffus.2018.09.012 · Cited by 675 · see citing works

DeepSynergy: predicting anti-cancer drug synergy with Deep Learning

Preuer, K., Lewis, R. P. I., Hochreiter, S., Bender, A., Bulusu, K. C., & Klambauer, G. (2017) · Bioinformatics · doi:10.1093/bioinformatics/btx806 · Cited by 651 · see citing works

Community assessment to advance computational prediction of cancer drug combinations in a pharmacogenomic screen

Menden, M. P., Wang, D. Y. Q., Mason, M. J., Szalai, B., Bulusu, K. C., Guan, Y., Yu, T. V., Kang, J., Jeon, M., Wolfinger, R. D., et al. (2019) · Nature Communications · doi:10.1038/s41467-019-09799-2 · Cited by 360 · see citing works

Elucidating Compound Mechanism of Action by Network Perturbation Analysis

Woo, J. H., Shimoni, Y., Yang, W. S., Subramaniam, P., Iyer, A., Nicoletti, P., Rodríguez Martínez, M., López, G., Mattioli, M., Realubit, R., et al. (2015) · Cell · doi:10.1016/j.cell.2015.05.056 · Cited by 351 · see citing works

Independent Drug Action in Combination Therapy: Implications for Precision Oncology

Plana, D., Palmer, A. C., & Sorger, P. K. (2022) · Cancer Discovery · doi:10.1158/2159-8290.cd-21-0212 · Cited by 322 · see citing works

Tumor-Derived Cell Lines as Molecular Models of Cancer Pharmacogenomics

Goodspeed, A. E., Heiser, L. M., Gray, J. W., & Costello, J. C. (2015) · Molecular Cancer Research · doi:10.1158/1541-7786.mcr-15-0189 · Cited by 271 · see citing works

Big data in basic and translational cancer research

Jiang, P., Sinha, S., Aldape, K., Hannenhalli, S., Sahinalp, C., & Ruppin, E. (2022) · Nature Reviews Cancer · doi:10.1038/s41568-022-00502-0 · Cited by 254 · see citing works

Deep learning for drug response prediction in cancer

Baptista, D., Ferreira, P. G., & Rocha, M. (2019) · Briefings in Bioinformatics · doi:10.1093/bib/bbz171 · Cited by 239 · see citing works

Exploiting interconnected synthetic lethal interactions between PARP inhibition and cancer cell reversible senescence

Fleury, H., Malaquin, N., Tu, V., Gilbert, S., Martinez, A., Olivier, M.-A., Sauriol, S. A., Communal, L., Leclerc-Desaulniers, K., Carmona, E., et al. (2019) · Nature Communications · doi:10.1038/s41467-019-10460-1 · Cited by 228 · see citing works

Computational network biology: Data, models, and applications

Liu, C., Ma, Y., Zhao, J., Nussinov, R., Zhang, Y., Cheng, F., & Zhang, Z. (2019) · Physics Reports · doi:10.1016/j.physrep.2019.12.004 · Cited by 226 · see citing works

Combinatorial strategies for combating invasive fungal infections

Spitzer, M., Robbins, N., & Wright, G. D. (2016) · Virulence · doi:10.1080/21505594.2016.1196300 · Cited by 218 · see citing works

The recurrent architecture of tumour initiation, progression and drug sensitivity

Califano, A., & Alvarez, M. J. (2016) · Nature reviews. Cancer · doi:10.1038/nrc.2016.124 · Cited by 198 · see citing works

MatchMaker: A Deep Learning Framework for Drug Synergy Prediction

Kuru, H. İ., Taştan, Ö., & Çiçek, A. E. (2021) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2021.3086702 · Cited by 188 · see citing works

Crowdsourcing biomedical research: leveraging communities as innovation engines

Sáez-Rodríguez, J., Costello, J. C., Friend, S. H., Kellen, M., Mangravite, L. M., Meyer, P., Norman, T., & Stolovitzky, G. A. (2016) · Nature Reviews Genetics · doi:10.1038/nrg.2016.69 · Cited by 165 · see citing works

Computational models for predicting drug responses in cancer research

Azuaje, F. J. (2016) · Briefings in Bioinformatics · doi:10.1093/bib/bbw065 · Cited by 162 · see citing works

Leveraging big data to transform target selection and drug discovery

Chen, B., & Butte, A. J. (2015) · Clinical Pharmacology & Therapeutics · doi:10.1002/cpt.318 · Cited by 159 · see citing works

Machine learning in the prediction of cancer therapy

Rafique, R., Islam, S. M. R., & Kazi, J. U. (2021) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2021.07.003 · Cited by 154 · see citing works

Bioinformatics Approaches for Anti-cancer Drug Discovery

Li, K., Du, Y., Li, L., & Wei, D. (2019) · Current Drug Targets · doi:10.2174/1389450120666190923162203 · Cited by 143 · see citing works

Rethinking Drug Repositioning and Development with Artificial Intelligence, Machine Learning, and Omics

Koromina, M., Pandi, M.-T., & Patrinos, G. P. (2019) · OMICS A Journal of Integrative Biology · doi:10.1089/omi.2019.0151 · Cited by 140 · see citing works

An Improved Anticancer Drug-Response Prediction Based on an Ensemble Method Integrating Matrix Completion and Ridge Regression

Liu, C., Dong, W. G., Xiang, J., Ren, F., Huang, L., Lang, J., Tian, G., Li, Y., & Yang, J. (2020) · Molecular Therapy — Nucleic Acids · doi:10.1016/j.omtn.2020.07.003 · Cited by 138 · see citing works

Machine learning for perturbational single-cell omics

Ji, Y., Lotfollahi, M., Wolf, F. A., & Theis, F. J. (2021) · Cell Systems · doi:10.1016/j.cels.2021.05.016 · Cited by 137 · see citing works

High-throughput metabolomic analysis predicts mode of action of uncharacterized antimicrobial compounds

Zampieri, M., Szappanos, B., Buchieri, M. V., Trauner, A., Piazza, I., Picotti, P., Gagneux, S., Borrell, S., Gicquel, B., Lelièvre, J., et al. (2018) · Science Translational Medicine · doi:10.1126/scitranslmed.aal3973 · Cited by 133 · see citing works

Prediction of overall survival for patients with metastatic castration-resistant prostate cancer: development of a prognostic model through a crowdsourced challenge with open clinical trial data

Guinney, J., Wang, T., Laajala, T. D., Winner, K. K., Bare, J. C., Neto, E. C., Khan, S. A., Peddinti, G., Airola, A., Pahikkala, T., et al. (2016) · The Lancet Oncology · doi:10.1016/s1470-2045(16)30560-5 · Cited by 136 · see citing works

Combining genomic and network characteristics for extended capability in predicting synergistic drugs for cancer

Sun, Y., Sheng, Z., Ma, C., Tang, K., Zhu, R., Wu, Z., Shen, R., Feng, J. H., Wu, D., Huang, D., et al. (2015) · Nature Communications · doi:10.1038/ncomms9481 · Cited by 134 · see citing works

Discovery of Drug Synergies in Gastric Cancer Cells Predicted by Logical Modeling

Flobak, Å., Baudot, A., Rémy, É., Thommesen, L., Thieffry, D., Kuiper, M. T. R., & Lægreid, A. (2015) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1004426 · Cited by 126 · see citing works

A survey of graph neural networks in various learning paradigms: methods, applications, and challenges

Waikhom, L., & Patgiri, R. (2022) · Artificial Intelligence Review · doi:10.1007/s10462-022-10321-2 · Cited by 203 · see citing works

Harnessing synthetic lethality to predict the response to cancer treatment

Lee, J. S., Das, A., Jerby‐Arnon, L., Arafeh, R., Auslander, N., Davidson, M. A., McGarry, L., James, D. T., Amzallag, A., Park, S. G., et al. (2018) · Nature Communications · doi:10.1038/s41467-018-04647-1 · Cited by 120 · see citing works

Predicting tumor cell line response to drug pairs with deep learning

Xia, F., Shukla, M. P., Brettin, T. S., García–Cardona, C., Cohn, J. D., Allen, J., Maslov, S., Holbeck, S. L., Doroshow, J. H., Evrard, Y. A., et al. (2018) · BMC Bioinformatics · doi:10.1186/s12859-018-2509-3 · Cited by 128 · see citing works

Prediction of synergistic anti-cancer drug combinations based on drug target network and drug induced gene expression profiles

Li, X., Xu, Y., Cui, H. S., Huang, T., Wang, D., Lian, B., Li, W., Qin, G., Chen, L., & Xie, L. (2017) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2017.05.008 · Cited by 127 · see citing works

Leveraging multi-way interactions for systematic prediction of pre-clinical drug combination effects

Julkunen, H., Cichońska, A., Gautam, P., Szedmák, S., Douat, J., Pahikkala, T., Aittokallio, T. A., & Rousu, J. (2020) · Nature Communications · doi:10.1038/s41467-020-19950-z · Cited by 124 · see citing works

Machine learning methods, databases and tools for drug combination prediction

Wu, L., Wen, Y., Leng, D., Zhang, Q., Dai, C., Wang, Z., Liu, Z., Yan, B., Zhang, Y., Wang, J., et al. (2021) · Briefings in Bioinformatics · doi:10.1093/bib/bbab355 · Cited by 131 · see citing works

Causal Mechanistic Regulatory Network for Glioblastoma Deciphered Using Systems Genetics Network Analysis

Plaisier, C. L., O’Brien, S. A., Bernard, B. M., Reynolds, S. M., Simon, Z., Toledo, C. M., Ding, Y., Reiss, D. J., Paddison, P. J., & Baliga, N. S. (2016) · Cell Systems · doi:10.1016/j.cels.2016.06.006 · Cited by 116 · see citing works

Molecular signatures of long-term hepatocellular carcinoma risk in nonalcoholic fatty liver disease

Fujiwara, N., Kubota, N., Crouchet, É., Koneru, B., Marquez, C. A., Jajoriya, A. K., Panda, G., Qian, T., Zhu, S., Goossens, N., et al. (2022) · Science Translational Medicine · doi:10.1126/scitranslmed.abo4474 · Cited by 124 · see citing works

Prediction of Synergism from Chemical-Genetic Interactions by Machine Learning

Wildenhain, J., Spitzer, M., Dolma, S., Jarvik, N., White, R., Roy, M. M., Griffiths, E., Bellows, D. S., Wright, G. D., & Tyers, M. (2015) · Cell Systems · doi:10.1016/j.cels.2015.12.003 · Cited by 122 · see citing works

Artificial intelligence in drug combination therapy

Tsigelny, I. F. (2018) · Briefings in Bioinformatics · doi:10.1093/bib/bby004 · Cited by 120 · see citing works

In-silico Prediction of Synergistic Anti-Cancer Drug Combinations Using Multi-omics Data

Celebi, R., Bear Don’t Walk, O., Movva, R., Alpsoy, S., & Dumontier, M. (2019) · Scientific Reports · doi:10.1038/s41598-019-45236-6 · Cited by 120 · see citing works

Chemogenomics and orthology‐based design of antibiotic combination therapies

Chandrasekaran, S., Çakmak, M. Ç., Sahin, N., Yılancıoğlu, K., Kazan, H., Collins, J. J., & Cokol, M. (2016) · Molecular Systems Biology · doi:10.15252/msb.20156777 · Cited by 117 · see citing works

Predictive approaches for drug combination discovery in cancer

Tonekaboni, S. A. M., Ghoraie, L. S., Manem, V. S., & Haibe‐Kains, B. (2016) · Briefings in Bioinformatics · doi:10.1093/bib/bbw104 · Cited by 118 · see citing works

Comprehensive evaluation of deep and graph learning on drug–drug interactions prediction

Lin, X., Dai, L., Zhou, Y., Yu, Z., Zhang, W., Shi, J., Cao, D., Li, Z., Chen, H., Song, B., et al. (2023) · Briefings in Bioinformatics · doi:10.1093/bib/bbad235 · Cited by 121 · see citing works

Combination therapeutics in complex diseases

He, B., Lu, C., Zheng, G., He, X., Wang, M., Chen, G., Zhang, G., & Lu, A. (2016) · Journal of Cellular and Molecular Medicine · doi:10.1111/jcmm.12930 · Cited by 112 · see citing works

Additivity and Interactions in Ecotoxicity of Pollutant Mixtures: Some Patterns, Conclusions, and Open Questions

Rodea‐Palomares, I., González-Pleiter, M., Martín-Betancor, K., Rosal, R., & Fernández‐Piñas, F. (2015) · Toxics · doi:10.3390/toxics3040342 · Cited by 110 · see citing works

PLATE-Seq for genome-wide regulatory network analysis of high-throughput screens

Bush, E. C., Ray, F., Alvarez, M. J., Realubit, R. B., Hai, L., Karan, C., Califano, A., & Sims, P. A. (2017) · Nature Communications · doi:10.1038/s41467-017-00136-z · Cited by 107 · see citing works

Pamiparib is a potent and selective PARP inhibitor with unique potential for the treatment of brain tumor

Xiong, Y., Guo, Y., Liu, Y., Wang, H., Gong, W., Liu, Y., Wang, X., Gao, Y., Yu, F., Su, D., et al. (2020) · Neoplasia · doi:10.1016/j.neo.2020.06.009 · Cited by 102 · see citing works

Machine learning approaches for drug combination therapies

Paltun, B. G., Samuel Kaski, A., & Mamitsuka, H. (2021) · Briefings in Bioinformatics · doi:10.1093/bib/bbab293 · Cited by 103 · see citing works

Harnessing machine learning to find synergistic combinations for FDA-approved cancer drugs

El‐Hafeez, T. A., Shams, M. Y., Elshaier, Y. A. M. M., Farghaly, H. M., & Hassanien, A. E. (2024) · Scientific Reports · doi:10.1038/s41598-024-52814-w · Cited by 102 · see citing works

Systems Pharmacology: Defining the Interactions of Drug Combinations

van Hasselt, J. G. C., & Iyengar, R. (2018) · The Annual Review of Pharmacology and Toxicology · doi:10.1146/annurev-pharmtox-010818-021511 · Cited by 101 · see citing works

Machine Learning and Network Analyses Reveal Disease Subtypes of Pancreatic Cancer and their Molecular Characteristics

Sinkala, M., Mulder, N., & Martin, D. P. (2020) · Scientific Reports · doi:10.1038/s41598-020-58290-2 · Cited by 100 · see citing works

Mining molecular structure databases: Identification of small molecules based on fragmentation mass spectrometry data

Hufsky, F., & Böcker, S. (2016) · Mass Spectrometry Reviews · doi:10.1002/mas.21489 · Cited by 100 · see citing works

Mapping the functional landscape of T cell receptor repertoires by single-T cell transcriptomics

Zhang, Z., Xiong, D., Wang, X., Liu, H., & Wang, T. (2021) · Nature Methods · doi:10.1038/s41592-020-01020-3 · Cited by 99 · see citing works

A Computational Approach for Identifying Synergistic Drug Combinations

Gayvert, K. M., Aly, O., Platt, J. T., Bosenberg, M., Stern, D. F., & Elemento, O. (2017) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1005308 · Cited by 97 · see citing works

MuSyC is a consensus framework that unifies multi-drug synergy metrics for combinatorial drug discovery

Wooten, D. J., Meyer, C. T., Lubbock, A. L. R., Quaranta, V., & Lopez, C. F. (2021) · Nature Communications · doi:10.1038/s41467-021-24789-z · Cited by 97 · see citing works

Common and cell-type specific responses to anti-cancer drugs revealed by high throughput transcript profiling

Niepel, M., Hafner, M. A., Duan, Q., Wang, Z., Paull, E., Chung, M., Lü, X., Stuart, J. M., Golub, T. R., Subramanian, A., et al. (2017) · Nature Communications · doi:10.1038/s41467-017-01383-w · Cited by 93 · see citing works

A New View of Pathway-Driven Drug Resistance in Tumor Proliferation

Nussinov, R., Tsai, C., & Jang, H. (2017) · Trends in Pharmacological Sciences · doi:10.1016/j.tips.2017.02.001 · Cited by 93 · see citing works

Improving the efficacy-safety balance of polypharmacology in multi-target drug discovery

Ravikumar, B., & Aittokallio, T. A. (2017) · Expert Opinion on Drug Discovery · doi:10.1080/17460441.2018.1413089 · Cited by 93 · see citing works

Anticancer drug synergy prediction in understudied tissues using transfer learning

Kim, Y., Zheng, S., Tang, J. P., Zheng, W. J., Li, Z., & Jiang, X. (2020) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocaa212 · Cited by 87 · see citing works

A cancer drug atlas enables synergistic targeting of independent drug vulnerabilities

Narayan, R. S., Molenaar, P., Teng, J., Cornelissen, F. M. G., Roelofs, I., de Menezes, R. X., Dik, R., Lagerweij, T., Broersma, Y., Petersen, N., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-16735-2 · Cited by 85 · see citing works

Network analysis identifies Rv0324 and Rv0880 as regulators of bedaquiline tolerance in Mycobacterium tuberculosis

Peterson, E. J. R., Ma, S., Sherman, D. R., & Baliga, N. S. (2016) · Nature Microbiology · doi:10.1038/nmicrobiol.2016.78 · Cited by 87 · see citing works

Boolean network modeling in systems pharmacology

Bloomingdale, P., Nguyen, V. A., Niu, J., & Mager, D. E. (2018) · Journal of Pharmacokinetics and Pharmacodynamics · doi:10.1007/s10928-017-9567-4 · Cited by 86 · see citing works

Systems medicine: evolution of systems biology from bench to bedside

Wang, R., Maron, B. A., & Loscalzo, J. (2015) · WIREs Systems Biology and Medicine · doi:10.1002/wsbm.1297 · Cited by 80 · see citing works

In silico drug combination discovery for personalized cancer therapy

Jeon, M., Kim, S., Park, S., Lee, H., & Kang, J. (2018) · BMC Systems Biology · doi:10.1186/s12918-018-0546-1 · Cited by 79 · see citing works

Charting the Fragmented Landscape of Drug Synergy

Meyer, C. T., Wooten, D. J., Lopez, C. F., & Quaranta, V. (2020) · Trends in Pharmacological Sciences · doi:10.1016/j.tips.2020.01.011 · Cited by 79 · see citing works

Metabolomics-Driven Exploration of the Chemical Drug Space to Predict Combination Antimicrobial Therapies

Campos, A. I., & Zampieri, M. (2019) · Molecular Cell · doi:10.1016/j.molcel.2019.04.001 · Cited by 79 · see citing works

Deep learning of pharmacogenomics resources: moving towards precision oncology

Chiu, Y., Chen, H.-I. H., Gorthi, A., Mostavi, M., Zheng, S., Huang, Y., & Chen, Y. (2019) · Briefings in Bioinformatics · doi:10.1093/bib/bbz144 · Cited by 77 · see citing works

Patient-Customized Drug Combination Prediction and Testing for T-cell Prolymphocytic Leukemia Patients

He, L., Tang, J. P., Andersson, E. I., Timonen, S., Koschmieder, S., Wennerberg, K., Mustjoki, S. M., & Aittokallio, T. A. (2018) · Cancer Research · doi:10.1158/0008-5472.can-17-3644 · Cited by 77 · see citing works

Driver network as a biomarker: systematic integration and network modeling of multi-omics data to derive driver signaling pathways for drug combination prediction

Huang, L. F., Brunell, D. J., STEPHAN, C. C., Mancuso, J. J., Yu, X., He, B., Thompson, T. C., Zinner, R., Kim, J., Davies, P. J., et al. (2019) · Bioinformatics · doi:10.1093/bioinformatics/btz109 · Cited by 71 · see citing works

Drug-Drug Adverse Effect Prediction with Graph Co-AttentionPreprint

Deac, A., Huang, Y.-H. J., Veličković, P., Lió, P., & Tang, J. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1905.00534 · Cited by 70 · see citing works

A community challenge for a pancancer drug mechanism of action inference from perturbational profile data

Douglass, E. F., Allaway, R. J., Szalai, B., Wang, W., Tian, T., Fernández-Torras, A., Realubit, R., Karan, C., Zheng, S., Pessia, A., et al. (2022) · Cell Reports Medicine · doi:10.1016/j.xcrm.2021.100492 · Cited by 68 · see citing works

Multi-Relational Contrastive Learning Graph Neural Network for Drug-Drug Interaction Event Prediction

Xiong, Z., Liu, S., Huang, F., Wang, Z., Liu, X., Zhang, Z. M., & Zhang, W. (2023) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v37i4.25665 · Cited by 68 · see citing works

A review of machine learning approaches for drug synergy prediction in cancer

Torkamannia, A., Omidi, Y., & Ferdousi, R. (2022) · Briefings in Bioinformatics · doi:10.1093/bib/bbac075 · Cited by 64 · see citing works

MDDI-SCL: predicting multi-type drug-drug interactions via supervised contrastive learning

Lin, S., Chen, W., Chen, G., Zhou, S., Wei, D., & Xiong, Y. (2022) · Journal of Cheminformatics · doi:10.1186/s13321-022-00659-8 · Cited by 64 · see citing works

Drug Combinations: Mathematical Modeling and Networking Methods

Vakil, V., & Trappe, W. K. (2019) · Pharmaceutics · doi:10.3390/pharmaceutics11050208 · Cited by 58 · see citing works

KekuleScope: prediction of cancer cell line sensitivity and compound potency using convolutional neural networks trained on compound images

Cortés‐Ciriano, I., & Bender, A. (2019) · Journal of Cheminformatics · doi:10.1186/s13321-019-0364-5 · Cited by 63 · see citing works

Synergy from gene expression and network mining (SynGeNet) method predicts synergistic drug combinations for diverse melanoma genomic subtypes

Regan, K., Xu, J., DiVincenzo, M. J., Duggan, M. C., Shakya, R., Na, R., Carson, W. E., Payne, P., & Li, F. (2019) · npj Systems Biology and Applications · doi:10.1038/s41540-019-0085-4 · Cited by 57 · see citing works

SmileGNN: Drug–Drug Interaction Prediction Based on the SMILES and Graph Neural Network

Han, X., Xie, R., Li, X., & Li, J. (2022) · Life · doi:10.3390/life12020319 · Cited by 56 · see citing works

DeepDrug: A general graph‐based deep learning framework for drug‐drug interactions and drug‐target interactions prediction

Yin, Q., Cao, X., Fan, R., Liu, Q., Jiang, R., & Zeng, W. (2023) · Quantitative Biology · doi:10.15302/j-qb-022-0320 · Cited by 56 · see citing works

Advances in systems biology modeling: 10 years of crowdsourcing DREAM challenges

Meyer, P., & Sáez-Rodríguez, J. (2021) · Cell Systems · doi:10.1016/j.cels.2021.05.015 · Cited by 57 · see citing works

Stratification and prediction of drug synergy based on target functional similarity

Yang, M., Jaaks, P., Dry, J. R., Garnett, M. J., Menden, M. P., & Sáez-Rodríguez, J. (2020) · npj Systems Biology and Applications · doi:10.1038/s41540-020-0136-x · Cited by 55 · see citing works

Synthetic Lethality in Cancer Therapeutics

Beijersbergen, R. L., Wessels, L. F. A., & Bernards, R. (2016) · Annual Review of Cancer Biology · doi:10.1146/annurev-cancerbio-042016-073434 · Cited by 53 · see citing works

Network perturbation analysis of gene transcriptional profiles reveals protein targets and mechanism of action of drugs and influenza A viral infection

Noh, H., Shoemaker, J. E., & Gunawan, R. (2017) · Nucleic Acids Research · doi:10.1093/nar/gkx1314 · Cited by 50 · see citing works

Pre-clinical validation of an RNA-based precision oncology platform for patient-therapy alignment in a diverse set of human malignancies resistant to standard treatments

Mundi, P. S., Dela Cruz, F. S., Grunn, A., Diolaiti, D., Mauguen, A., Rainey, A. R., Guillan, K. C., Siddiquee, A., You, D., Realubit, R., et al. (2021) · doi:10.1101/2021.10.03.462951 · Cited by 51 · see citing works

Advancing Targeted Protein Degradation via Multiomics Profiling and Artificial Intelligence

Duran-Frigola, M., Cigler, M., & Winter, G. E. (2023) · Journal of the American Chemical Society · doi:10.1021/jacs.2c11098 · Cited by 50 · see citing works

Advances in computational approaches in identifying synergistic drug combinations

Sheng, Z., Sun, Y., Yin, Z., Tang, K., & Cao, Z. (2017) · Briefings in Bioinformatics · doi:10.1093/bib/bbx047 · Cited by 50 · see citing works

Senolytic Targeting of Bcl-2 Anti-Apoptotic Family Increases Cell Death in Irradiated Sarcoma Cells

Lafontaine, J., Cardin, G. B., Malaquin, N., Boisvert, J.-S., Rodier, F., & Wong, P. (2021) · Cancers · doi:10.3390/cancers13030386 · Cited by 52 · see citing works

DeSIDE-DDI: interpretable prediction of drug-drug interactions using drug-induced gene expressions

Kim, E., & Nam, H. (2022) · Journal of Cheminformatics · doi:10.1186/s13321-022-00589-5 · Cited by 48 · see citing works

MARSY: a multitask deep-learning framework for prediction of drug combination synergy scores

Khili, M. R. E., Memon, S. A., & Emad, A. (2023) · Bioinformatics · doi:10.1093/bioinformatics/btad177 · Cited by 48 · see citing works

Statistical Hypothesis Testing versus Machine Learning Binary Classification: Distinctions and Guidelines

Li, J. J., & Tong, X. (2020) · Patterns · doi:10.1016/j.patter.2020.100115 · Cited by 49 · see citing works

StratMed: Relevance stratification between biomedical entities for sparsity on medication recommendation

Li, X., Liang, S., Hou, Y., & Ma, T. (2023) · Knowledge-Based Systems · doi:10.1016/j.knosys.2023.111239 · Cited by 44 · see citing works

Using Machine Learning to Predict Synergistic Antimalarial Compound Combinations With Novel Structures

Mason, D. J., Eastman, R. T., Lewis, R. P. I., Stott, I. P., Guha, R., & Bender, A. (2018) · Frontiers in Pharmacology · doi:10.3389/fphar.2018.01096 · Cited by 43 · see citing works

Chemogenomic model identifies synergistic drug combinations robust to the pathogen microenvironment

Cokol, M., Li, C., & Chandrasekaran, S. (2018) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1006677 · Cited by 43 · see citing works

DeepInsight-3D architecture for anti-cancer drug response prediction with deep-learning on multi-omics

Sharma, A. K., Lysenko, A., Boroevich, K. A., & Tsunoda, T. (2023) · Scientific Reports · doi:10.1038/s41598-023-29644-3 · Cited by 42 · see citing works

SYNDEEP: a deep learning approach for the prediction of cancer drugs synergy

Torkamannia, A., Omidi, Y., & Ferdousi, R. (2023) · Scientific Reports · doi:10.1038/s41598-023-33271-3 · Cited by 41 · see citing works

Predicting ligand-dependent tumors from multi-dimensional signaling features

Hass, H., Masson, K., Wohlgemuth, S., Paragas, V. B., Allen, J. E., Sevecka, M., Pace, E. A., Timmer, J., Stelling, J., MacBeath, G., et al. (2017) · npj Systems Biology and Applications · doi:10.1038/s41540-017-0030-3 · Cited by 41 · see citing works

Oncobox Bioinformatical Platform for Selecting Potentially Effective Combinations of Target Cancer Drugs Using High-Throughput Gene Expression Data

Sorokin, M., Kholodenko, R. V., Suntsova, M., Malakhova, G., Garazha, A., Kholodenko, I. V., Poddubskaya, E. V., Lantsov, D., Stilidi, I. S., Arhiri, P. P., et al. (2018) · Cancers · doi:10.3390/cancers10100365 · Cited by 40 · see citing works

The transcriptomic response of cells to a drug combination is more than the sum of the responses to the monotherapies

Diaz, J. E. L., Ahsen, M. E., Schaffter, T., Chen, X., Realubit, R. B., Karan, C., Califano, A., Losic, B., & Stolovitzky, G. A. (2020) · eLife · doi:10.7554/elife.52707 · Cited by 36 · see citing works

Graph Neural Networks: Methods, Applications, and OpportunitiesPreprint

Waikhom, L., & Patgiri, R. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2108.10733 · Cited by 40 · see citing works

Community assessment to advance computational prediction of cancer drug combinations in a pharmacogenomic screen

Menden, M. P., Wang, D., Mason, M. J., Szalai, B., Bulusu, K. C., Guan, Y., Yu, T., Kang, J., Jeon, M., Wolfinger, R., et al. (2019) · Greater South Information System · doi:10.60692/3f1af-xf873

Predicting synergistic effects between compounds through their structural similarity and effects on transcriptomes

Liu, Y., & Zhao, H. (2016) · Bioinformatics · doi:10.1093/bioinformatics/btw509 · Cited by 38 · see citing works

A simple gene set-based method accurately predicts the synergy of drug pairs

Hsu, Y.-C., Chiu, Y., Chen, Y., Hsiao, T.-H., & Chuang, E. Y.-Y. (2016) · BMC Systems Biology · doi:10.1186/s12918-016-0310-3 · Cited by 15 · see citing works

DIGREM: an integrated web-based platform for detecting effective multi-drug combinations

Zhang, M., Lee, S., Yao, B., Xiao, G., Xu, L., & Xie, Y. (2018) · Bioinformatics · doi:10.1093/bioinformatics/bty860 · Cited by 9 · see citing works

MatchMaker: A Deep Learning Framework for Drug Synergy Prediction

Kuru, H. İ., Taştan, Ö., & Çiçek, A. E. (2021) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2021.3086702 · Cited by 188 · see citing works