DeepSynergy: predicting anti-cancer drug synergy with Deep Learning
暂无分享,去创建一个
Andreas Bender | Sepp Hochreiter | Günter Klambauer | Krishna C. Bulusu | Kristina Preuer | Richard P. I. Lewis | S. Hochreiter | A. Bender | G. Klambauer | K. Bulusu | Kristina Preuer
[1] Christian Melander,et al. Combination approaches to combat multidrug-resistant bacteria. , 2013, Trends in biotechnology.
[2] Knut Baumann,et al. Reliable estimation of prediction errors for QSAR models under model uncertainty using double cross-validation , 2014, Journal of Cheminformatics.
[3] César López-Camarillo,et al. Oncogenomics and Cancer Proteomics - Novel Approaches in Biomarkers Discovery and Therapeutic Targets in Cancer , 2013 .
[4] S. Ng,et al. Bexarotene (LGD1069, Targretin), a selective retinoid X receptor agonist, prevents and reverses gemcitabine resistance in NSCLC cells by modulating gene amplification. , 2007, Cancer research.
[5] B. Al-Lazikani,et al. Combinatorial drug therapy for cancer in the post-genomic era , 2012, Nature Biotechnology.
[6] Y. Miura,et al. Schedule-dependent interaction between paclitaxel and doxorubicin in human cancer cell lines in vitro. , 1995, European journal of cancer.
[7] H. Zou,et al. Regularization and variable selection via the elastic net , 2005 .
[8] Xin Chen,et al. DCDB 2.0: a major update of the drug combination database , 2014, Database J. Biol. Databases Curation.
[9] Lawrence A. Donehower,et al. Combinatorial therapy discovery using mixed integer linear programming , 2014, Bioinform..
[10] Peter J. Park,et al. Systematic Identification of Synergistic Drug Pairs Targeting HIV , 2012, Nature Biotechnology.
[11] Jürgen Bajorath,et al. Integration of virtual and high-throughput screening , 2002, Nature Reviews Drug Discovery.
[12] Ting-Chao Chou,et al. Theoretical Basis, Experimental Design, and Computerized Simulation of Synergism and Antagonism in Drug Combination Studies , 2006, Pharmacological Reviews.
[13] Sam Michael,et al. High-throughput combinatorial screening identifies drugs that cooperate with ibrutinib to kill activated B-cell–like diffuse large B-cell lymphoma cells , 2014, Proceedings of the National Academy of Sciences.
[14] Krister Wennerberg,et al. Corrigendum to “Searching for drug synergy in complex dose–response landscapes using an interaction potency model” [Comput. Struct. Biotechnol. J. 13 (2015) 504–513] , 2017, Computational and structural biotechnology journal.
[15] Yang Xie,et al. A community computational challenge to predict the activity of pairs of compounds , 2014, Nature Biotechnology.
[16] Geoffrey E. Hinton,et al. ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.
[17] Sepp Hochreiter,et al. Self-Normalizing Neural Networks , 2017, NIPS.
[18] Yair Benita,et al. An Unbiased Oncology Compound Screen to Identify Novel Combination Strategies , 2016, Molecular Cancer Therapeutics.
[19] C. I. Bliss. THE TOXICITY OF POISONS APPLIED JOINTLY1 , 1939 .
[20] Krister Wennerberg,et al. Methods for High-Throughput Drug Combination Screening and Synergy Scoring , 2016, bioRxiv.
[21] Y. Kano,et al. In vitro schedule-dependent interaction between paclitaxel and SN-38 (the active metabolite of irinotecan) in human carcinoma cell lines , 1998, Cancer Chemotherapy and Pharmacology.
[22] K. Adachi,et al. Schedule-dependent interaction between paclitaxel and 5-fluorouracil in human carcinoma cell lines in vitro. , 1996, British Journal of Cancer.
[23] Pranita D. Tamma,et al. Combination Therapy for Treatment of Infections with Gram-Negative Bacteria , 2012, Clinical Microbiology Reviews.
[24] Geoffrey E. Hinton,et al. Deep Learning , 2015, Nature.
[25] Xin Chen,et al. DCDB: Drug combination database , 2010, Bioinform..
[26] Dong-Sheng Cao,et al. ChemoPy: freely available python package for computational biology and chemoinformatics , 2013, Bioinform..
[27] S. Guichard,et al. Sequence‐dependent activity of the irinotecan‐5FU combination in human colon‐cancer model HT‐29 in vitro and in vivo , 1997 .
[28] Corinna Cortes,et al. Support-Vector Networks , 1995, Machine Learning.
[29] S. Loewe. The problem of synergism and antagonism of combined drugs. , 1953, Arzneimittel-Forschung.
[30] Mike Tyers,et al. Prediction of Synergism from Chemical-Genetic Interactions by Machine Learning. , 2015, Cell systems.
[31] L Li,et al. A New Drug Combinatory Effect Prediction Algorithm on the Cancer Cell Based on Gene Expression and Dose–Response Curve , 2015, CPT: pharmacometrics & systems pharmacology.
[32] E. De Clercq,et al. The design of drugs for HIV and HCV. , 2007, Nature reviews. Drug discovery.
[33] S. Guichard,et al. Combination of oxaliplatin and irinotecan on human colon cancer cell lines: activity in vitro and in vivo , 2001, Anti-cancer drugs.
[34] Andreas Bender,et al. Modelling of compound combination effects and applications to efficacy and toxicity: state-of-the-art, challenges and perspectives. , 2016, Drug discovery today.
[35] Raquel Chaves,et al. The Importance of Cancer Cell Lines as in vitro Models in Cancer Methylome Analysis and Anticancer Drugs Testing , 2013 .
[36] Tara N. Sainath,et al. Deep convolutional neural networks for LVCSR , 2013, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing.
[37] Seta Shahin,et al. A randomized phase IIIB trial of chemotherapy, bevacizumab, and panitumumab compared with chemotherapy and bevacizumab alone for metastatic colorectal cancer. , 2009, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.
[38] Emanuel J. V. Gonçalves,et al. A Landscape of Pharmacogenomic Interactions in Cancer , 2016, Cell.
[39] Ruth Nussinov,et al. Structure and dynamics of molecular networks: A novel paradigm of drug discovery. A comprehensive review , 2012, Pharmacology & therapeutics.
[40] Weimin Fan,et al. Fulvestrant reverses doxorubicin resistance in multidrug-resistant breast cell lines independent of estrogen receptor expression , 2016, Oncology reports.
[41] Jorge Cortes,et al. Systems approaches and algorithms for discovery of combinatorial therapies. , 2009, Wiley interdisciplinary reviews. Systems biology and medicine.
[42] Y Kano,et al. Schedule-dependent synergism and antagonism between paclitaxel and methotrexate in human carcinoma cell lines. , 1998, Oncology research.
[43] S. Hochreiter,et al. DeepTox: Toxicity prediction using deep learning , 2017 .
[44] Linda Mol,et al. Chemotherapy, bevacizumab, and cetuximab in metastatic colorectal cancer. , 2009, The New England journal of medicine.
[45] Y. Kano,et al. Schedule-dependent interactions between paclitaxel and etoposide in human carcinoma cell lines in vitro , 1999, Cancer Chemotherapy and Pharmacology.
[46] Xing Chen,et al. NLLSS: Predicting Synergistic Drug Combinations Based on Semi-supervised Learning , 2016, PLoS Comput. Biol..
[47] Jun Cui,et al. Modeling of signaling crosstalk-mediated drug resistance and its implications on drug combination , 2016, Oncotarget.
[48] David Rogers,et al. Extended-Connectivity Fingerprints , 2010, J. Chem. Inf. Model..
[49] STC Wong,et al. DIGRE: Drug-Induced Genomic Residual Effect Model for Successful Prediction of Multidrug Effects , 2015, CPT: pharmacometrics & systems pharmacology.
[50] Gaël Varoquaux,et al. Scikit-learn: Machine Learning in Python , 2011, J. Mach. Learn. Res..
[51] Klaus Obermayer,et al. A new summarization method for affymetrix probe level data , 2006, Bioinform..
[52] Jianxin Chen,et al. Large-scale exploration and analysis of drug combinations , 2015, Bioinform..
[53] L. Siu,et al. Approaches to modernize the combination drug development paradigm , 2016, Genome Medicine.
[54] Tara N. Sainath,et al. FUNDAMENTAL TECHNOLOGIES IN MODERN SPEECH RECOGNITION Digital Object Identifier 10.1109/MSP.2012.2205597 , 2012 .
[55] Anne E Carpenter,et al. Repurposing High-Throughput Image Assays Enables Biological Activity Prediction for Drug Discovery. , 2018, Cell chemical biology.
[56] J. Schellens,et al. Increased oral bioavailability of topotecan in combination with the breast cancer resistance protein and P-glycoprotein inhibitor GF120918. , 2002, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.
[57] Xiaohua Ma,et al. Mechanisms of drug combinations: interaction and network perspectives , 2009, Nature Reviews Drug Discovery.
[58] Dennis Wang,et al. Combenefit: an interactive platform for the analysis and visualization of drug combinations , 2016, Bioinform..
[59] Camille Couprie,et al. Learning Hierarchical Features for Scene Labeling , 2013, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[60] Y. Kano,et al. Schedule‐dependent interactions between vinorelbine and paclitaxel in human carcinoma cell lines in vitro , 1999, Breast Cancer Research and Treatment.
[61] Chris Morley,et al. Open Babel: An open chemical toolbox , 2011, J. Cheminformatics.
[62] Hans-Joachim Böhm,et al. A guide to drug discovery: Hit and lead generation: beyond high-throughput screening , 2003, Nature Reviews Drug Discovery.
[63] Geoffrey E. Hinton,et al. Rectified Linear Units Improve Restricted Boltzmann Machines , 2010, ICML.
[64] Robert P. Sheridan,et al. Deep Neural Nets as a Method for Quantitative Structure-Activity Relationships , 2015, J. Chem. Inf. Model..
[65] A. Groll,et al. Recent advances in antifungal prevention and treatment. , 2009, Seminars in hematology.
[66] MK Morris,et al. Systematic Analysis of Quantitative Logic Model Ensembles Predicts Drug Combination Effects on Cell Signaling Networks , 2016, CPT: pharmacometrics & systems pharmacology.
[67] R. E. White,et al. High-throughput screening in drug metabolism and pharmacokinetic support of drug discovery. , 2000, Annual review of pharmacology and toxicology.
[68] Leo Breiman,et al. Random Forests , 2001, Machine Learning.
[69] Erik De Clercq,et al. The design of drugs for HIV and HCV , 2007, Nature Reviews Drug Discovery.
[70] Sepp Hochreiter,et al. Toxicity Prediction using Deep Learning , 2015, ArXiv.
[71] Hinrich W. H. Göhlmann,et al. I/NI-calls for the exclusion of non-informative genes: a highly effective filtering tool for microarray data , 2007, Bioinform..
[72] Pawan Kumar Gupta,et al. Toxicophore exploration as a screening technology for drug design and discovery: techniques, scope and limitations , 2015, Archives of Toxicology.
[73] Xing Chen,et al. ASDCD: Antifungal Synergistic Drug Combination Database , 2014, PloS one.
[74] J. Friedman. Greedy function approximation: A gradient boosting machine. , 2001 .
[75] Andreas Zell,et al. jCompoundMapper: An open source Java library and command-line tool for chemical fingerprints , 2011, J. Cheminformatics.
[76] Tapio Pahikkala,et al. Toward more realistic drug^target interaction predictions , 2014 .