Systems biology approaches for advancing the discovery of effective drug combinations
Published 2015 View Full Article
- Home
- Publications
- Publication Search
- Publication Details
Title
Systems biology approaches for advancing the discovery of effective drug combinations
Authors
Keywords
Drug combinations, Systems biology, Computational modeling, Cancer, Drug discovery
Journal
Journal of Cheminformatics
Volume 7, Issue 1, Pages 7
Publisher
Springer Nature
Online
2015-02-25
DOI
10.1186/s13321-015-0055-9
References
Ask authors/readers for more resources
Related references
Note: Only part of the references are listed.- Bioinformatics-driven discovery of rational combination for overcoming EGFR-mutant lung cancer resistance to EGFR therapy
- (2014) Jihye Kim et al. BIOINFORMATICS
- Combinatorial therapy discovery using mixed integer linear programming
- (2014) Kaifang Pang et al. BIOINFORMATICS
- DrugComboRanker: drug combination discovery based on target network analysis
- (2014) L. Huang et al. BIOINFORMATICS
- Network Pharmacology Strategies Toward Multi-Target Anticancer Therapies: From Computational Models to Experimental Design Principles
- (2014) Jing Tang et al. CURRENT PHARMACEUTICAL DESIGN
- Phenotypic screen quantifying differential regulation of cardiac myocyte hypertrophy identifies CITED4 regulation of myocyte elongation
- (2014) Karen A. Ryall et al. JOURNAL OF MOLECULAR AND CELLULAR CARDIOLOGY
- Machine learning-based prediction of drug–drug interactions by integrating drug phenotypic, therapeutic, chemical, and genomic properties
- (2014) Feixiong Cheng et al. JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION
- Synergistic and Antagonistic Drug Combinations Depend on Network Topology
- (2014) Ning Yin et al. PLoS One
- High-throughput combinatorial screening identifies drugs that cooperate with ibrutinib to kill activated B-cell–like diffuse large B-cell lymphoma cells
- (2014) Lesley A. Mathews Griner et al. PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
- Intratumor heterogeneity alters most effective drugs in designed combinations
- (2014) B. Zhao et al. PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
- Exploiting polypharmacology for drug target deconvolution
- (2014) Taranjit Singh Gujral et al. PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
- Computational Analyses of Synergism in Small Molecular Network Motifs
- (2014) Yili Zhang et al. PLoS Computational Biology
- A new approach for prediction of tumor sensitivity to targeted drugs based on functional data
- (2013) Noah Berlow et al. BMC BIOINFORMATICS
- Rational Combination of a MEK Inhibitor, Selumetinib, and the Wnt/Calcium Pathway Modulator, Cyclosporin A, in Preclinical Models of Colorectal Cancer
- (2013) A. Spreafico et al. CLINICAL CANCER RESEARCH
- From Bench to Bedside: Lessons Learned in Translating Preclinical Studies in Cancer Drug Development
- (2013) C. H. Lieu et al. JNCI-Journal of the National Cancer Institute
- High-Throughput Methods for Combinatorial Drug Discovery
- (2013) X. Sun et al. Science Translational Medicine
- Addressing Genetic Tumor Heterogeneity through Computationally Predictive Combination Therapy
- (2013) B. Zhao et al. Cancer Discovery
- Target Inhibition Networks: Predicting Selective Combinations of Druggable Targets to Block Cancer Survival Pathways
- (2013) Jing Tang et al. PLoS Computational Biology
- Perturbation Biology: Inferring Signaling Networks in Cellular Systems
- (2013) Evan J. Molinelli et al. PLoS Computational Biology
- The drug cocktail network
- (2012) Ke-Jia Xu et al. BMC Systems Biology
- Converting Cancer Therapies into Cures: Lessons from Infectious Diseases
- (2012) Michael S. Glickman et al. CELL
- Applications of Connectivity Map in drug discovery and development
- (2012) Xiaoyan A. Qu et al. DRUG DISCOVERY TODAY
- 27 NCI-60 Combination Screening Matrix of Approved Anticancer Drugs
- (2012) S. Holbeck et al. EUROPEAN JOURNAL OF CANCER
- Network Reconstruction and Systems Analysis of Cardiac Myocyte Hypertrophy Signaling
- (2012) Karen A. Ryall et al. JOURNAL OF BIOLOGICAL CHEMISTRY
- Retraction in Part: A Genomic Approach to Identify Molecular Pathways Associated with Chemotherapy Resistance
- (2012) MOLECULAR CANCER THERAPEUTICS
- Synthetic Lethal Screening with Small-Molecule Inhibitors Provides a Pathway to Rational Combination Therapies for Melanoma
- (2012) D. G. Roller et al. MOLECULAR CANCER THERAPEUTICS
- Unresponsiveness of colon cancer to BRAF(V600E) inhibition through feedback activation of EGFR
- (2012) Anirudh Prahallad et al. NATURE
- Systematic identification of synergistic drug pairs targeting HIV
- (2012) Xu Tan et al. NATURE BIOTECHNOLOGY
- Patient-derived tumour xenografts as models for oncology drug development
- (2012) John J. Tentler et al. Nature Reviews Clinical Oncology
- Defining principles of combination drug mechanisms of action
- (2012) J. R. Pritchard et al. PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
- Molecular signaling network complexity is correlated with cancer patient survivability
- (2012) D. Breitkreutz et al. PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
- Merging Systems Biology with Pharmacodynamics
- (2012) R. Iyengar et al. Science Translational Medicine
- Principles and Strategies for Developing Network Models in Cancer
- (2011) Dana Pe'er et al. CELL
- The productivity crisis in pharmaceutical R&D
- (2011) Fabio Pammolli et al. NATURE REVIEWS DRUG DISCOVERY
- Discovery and Preclinical Validation of Drug Indications Using Compendia of Public Gene Expression Data
- (2011) M. Sirota et al. Science Translational Medicine
- Logic-Based Models for the Analysis of Cell Signaling Networks
- (2010) Melody K. Morris et al. BIOCHEMISTRY
- Modeling cardiac β-adrenergic signaling with normalized-Hill differential equations: comparison with a biochemical model
- (2010) Matthew J Kraeutler et al. BMC Systems Biology
- Identification of Optimal Drug Combinations Targeting Cellular Networks: Integrating Phospho-Proteomics and Computational Network Analysis
- (2010) S. Iadevaia et al. CANCER RESEARCH
- A Chromatin-Mediated Reversible Drug-Tolerant State in Cancer Cell Subpopulations
- (2010) Sreenath V. Sharma et al. CELL
- Cardiovascular Networks
- (2010) Aldons J. Lusis et al. CIRCULATION
- DCDB: Drug combination database
- (2009) Y. Liu et al. BIOINFORMATICS
- Network analyses in systems pharmacology
- (2009) S. I. Berger et al. BIOINFORMATICS
- Modeling ERBB receptor-regulated G1/S transition to find novel targets for de novo trastuzumab resistance
- (2009) Özgür Sahin et al. BMC Systems Biology
- Systems Biology Reveals New Strategies for Personalizing Cancer Medicine and Confirms the Role of PTEN in Resistance to Trastuzumab
- (2009) D. Faratian et al. CANCER RESEARCH
- Cancer systems biology: a network modeling perspective
- (2009) P. K. Kreeger et al. CARCINOGENESIS
- Therapeutically Targeting ErbB3: A Key Node in Ligand-Induced Activation of the ErbB Receptor-PI3K Axis
- (2009) B. Schoeberl et al. Science Signaling
- Systems approaches and algorithms for discovery of combinatorial therapies
- (2009) Jacob D. Feala et al. Wiley Interdisciplinary Reviews-Systems Biology and Medicine
- Fuzzy Logic Analysis of Kinase Pathway Crosstalk in TNF/EGF/Insulin-Induced Signaling
- (2009) Bree B. Aldridge et al. PLoS Computational Biology
- Network modeling of signal transduction: establishing the global view
- (2008) Hans A. Kestler et al. BIOESSAYS
- Oncogene Addiction
- (2008) I. B. Weinstein et al. CANCER RESEARCH
- Network pharmacology: the next paradigm in drug discovery
- (2008) Andrew L Hopkins Nature Chemical Biology
- Network model of survival signaling in large granular lymphocyte leukemia
- (2008) R. Zhang et al. PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
Discover Peeref hubs
Discuss science. Find collaborators. Network.
Join a conversationCreate your own webinar
Interested in hosting your own webinar? Check the schedule and propose your idea to the Peeref Content Team.
Create Now