Predicting adverse drug reactions through interpretable deep learning framework

[1]  Inci M. Baytas,et al.  An MCEM Framework for Drug Safety Signal Detection and Combination from Heterogeneous Real World Evidence , 2018, Scientific Reports.

[2]  Alán Aspuru-Guzik,et al.  Automatic Chemical Design Using a Data-Driven Continuous Representation of Molecules , 2016, ACS central science.

[3]  Avi Ma'ayan,et al.  Drug-induced adverse events prediction with the LINCS L1000 data , 2016, Bioinform..

[4]  Alán Aspuru-Guzik,et al.  Convolutional Networks on Graphs for Learning Molecular Fingerprints , 2015, NIPS.

[5]  Gang Fu,et al.  PubChem Substance and Compound databases , 2015, Nucleic Acids Res..

[6]  Dongsheng Cao,et al.  Integrating Multiple Evidence Sources to Predict Adverse Drug Reactions Based on a Systems Pharmacology Model , 2015, CPT: pharmacometrics & systems pharmacology.

[7]  Jimmy Ba,et al.  Adam: A Method for Stochastic Optimization , 2014, ICLR.

[8]  Quan Xu,et al.  ADReCS: an ontology database for aiding standardization and hierarchical classification of adverse drug reaction terms , 2014, Nucleic Acids Res..

[9]  Ping Zhang,et al.  Exploring the associations between drug side-effects and therapeutic indications , 2014, J. Biomed. Informatics.

[10]  Sergio E. Wong,et al.  Adverse Drug Reaction Prediction Using Scores Produced by Large-Scale Drug-Protein Target Docking on High-Performance Computing Machines , 2014, PloS one.

[11]  Yoshua Bengio,et al.  Neural Machine Translation by Jointly Learning to Align and Translate , 2014, ICLR.

[12]  Jun Hu,et al.  Determining molecular predictors of adverse drug reactions with causality analysis based on structure learning , 2014, J. Am. Medical Informatics Assoc..

[13]  Ping Zhang,et al.  Exploring the Relationship Between Drug Side-Effects and Therapeutic Indications , 2013, AMIA.

[14]  Pierre Baldi,et al.  Deep Architectures and Deep Learning in Chemoinformatics: The Prediction of Aqueous Solubility for Drug-Like Molecules , 2013, J. Chem. Inf. Model..

[15]  N. Shah,et al.  Performance of Pharmacovigilance Signal‐Detection Algorithms for the FDA Adverse Event Reporting System , 2013, Clinical pharmacology and therapeutics.

[16]  Patrick Aloy,et al.  Analysis of chemical and biological features yields mechanistic insights into drug side effects. , 2013, Chemistry & biology.

[17]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[18]  Michael J. Keiser,et al.  Large Scale Prediction and Testing of Drug Activity on Side-Effect Targets , 2012, Nature.

[19]  R. Altman,et al.  Data-Driven Prediction of Drug Effects and Interactions , 2012, Science Translational Medicine.

[20]  P. Bork,et al.  Network Neighbors of Drug Targets Contribute to Drug Side-Effect Similarity , 2011, PloS one.

[21]  A. Butte,et al.  Predicting Adverse Drug Reactions Using Publicly Available PubChem BioAssay Data , 2011, Clinical pharmacology and therapeutics.

[22]  Yoshihiro Yamanishi,et al.  Predicting drug side-effect profiles: a chemical fragment-based approach , 2011, BMC Bioinformatics.

[23]  Eunok Paek,et al.  High-throughput peptide quantification using mTRAQ reagent triplex , 2011, BMC Bioinformatics.

[24]  C Helma,et al.  Prediction of Adverse Drug Reactions Using Decision Tree Modeling , 2010, Clinical pharmacology and therapeutics.

[25]  David Rogers,et al.  Extended-Connectivity Fingerprints , 2010, J. Chem. Inf. Model..

[26]  P. Bork,et al.  A side effect resource to capture phenotypic effects of drugs , 2010, Molecular systems biology.

[27]  Bin Chen,et al.  Gaining Insight into Off-Target Mediated Effects of Drug Candidates with a Comprehensive Systems Chemical Biology Analysis , 2009, J. Chem. Inf. Model..

[28]  Christopher D. Manning,et al.  Introduction to Information Retrieval , 2008, J. Assoc. Inf. Sci. Technol..

[29]  A. Bender,et al.  Analysis of Pharmacology Data and the Prediction of Adverse Drug Reactions and Off‐Target Effects from Chemical Structure , 2007, ChemMedChem.

[30]  Rajarshi Guha,et al.  Chemical Informatics Functionality in R , 2007 .

[31]  D. Bojanic,et al.  Keynote review: in vitro safety pharmacology profiling: an essential tool for successful drug development. , 2005, Drug discovery today.

[32]  Daniel T. Larose,et al.  Discovering Knowledge in Data: An Introduction to Data Mining , 2005 .

[33]  A. Grizzle,et al.  Drug-related morbidity and mortality: updating the cost-of-illness model. , 2001, Journal of the American Pharmaceutical Association.

[34]  I. Edwards,et al.  Adverse drug reactions: definitions, diagnosis, and management , 2000, The Lancet.

[35]  George M. Church,et al.  Biclustering of Expression Data , 2000, ISMB.

[36]  William DuMouchel,et al.  Bayesian Data Mining in Large Frequency Tables, with an Application to the FDA Spontaneous Reporting System , 1999 .

[37]  E. Brown,et al.  The Medical Dictionary for Regulatory Activities (MedDRA) , 1999, Drug safety.

[38]  J. Sibley,et al.  Avascular necrosis of the hips following longterm use of clobetasol propionate. , 1986, Journal of the American Academy of Dermatology.

[39]  H. L. Morgan.  The Generation of a Unique Machine Description for Chemical Structures-A Technique Developed at Chemical Abstracts Service. , 1965 .

[40]  Vít Novácek,et al.  Using Drug Similarities for Discovery of Possible Adverse Reactions , 2016, AMIA.

[41]  Adrià Cereto-Massagué,et al.  Molecular fingerprint similarity search in virtual screening. , 2015, Methods.

[42]  Matthew Crosby,et al.  Association for the Advancement of Artificial Intelligence , 2014 .

[43]  Arlindo L. Oliveira,et al.  Biclustering algorithms for biological data analysis: a survey , 2004, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

[44]  Trevor Hastie,et al.  The Elements of Statistical Learning , 2001 .

[45]  Ah Chung Tsoi,et al.  Face recognition: a convolutional neural-network approach , 1997, IEEE Trans. Neural Networks.

[46]  Alexander J. Smola,et al.  Neural Information Processing Systems , 1997, NIPS 1997.