リケラボ論文検索は、全国の大学リポジトリにある学位論文・教授論文を一括検索できる論文検索サービスです。

リケラボ 全国の大学リポジトリにある学位論文・教授論文を一括検索するならリケラボ論文検索大学・研究所にある論文を検索できる

リケラボ 全国の大学リポジトリにある学位論文・教授論文を一括検索するならリケラボ論文検索大学・研究所にある論文を検索できる

大学・研究所にある論文を検索できる 「Establishing advanced deep learning models for predicting drug side effects」の論文概要。リケラボ論文検索は、全国の大学リポジトリにある学位論文・教授論文を一括検索できる論文検索サービスです。

コピーが完了しました

URLをコピーしました

論文の公開元へ論文の公開元へ
書き出し

Establishing advanced deep learning models for predicting drug side effects

NGUYEN, DUC ANH 京都大学 DOI:10.14989/doctor.k24559

2023.03.23

概要

Computational needs for predicting drug side effects
According to WHO, an adverse drug reaction (ADR) or a drug side effect
(side effect for short) is a response to a medicine which is noxious and unintended, and which occurs at doses normally used in humans [WHO, 1972]. In
reports of 2011, drug side effects accounted for nearly 6% of total hospitalizations in the USA, which cost billions of dollars and was responsible for significant patient morbidity and mortality [Poudel et al., 2017, Weiss et al., 2013].
Therefore, studies of drug side effects are important in drug discovery.
The traditional methods for obtaining drug side effects often use clinical trials or post-marketing surveillance reports [Hoots et al., 2018]. However, these
methods are costly and time-consuming, leading to the need for developing
methods to support the process of determining drug side effects.
Nowadays, with the development of technologies and standardization, there
exist numerous databases related to drugs and side effects, for example, more
than 7 million electronic health records of patients [FDA, 2019], 113 million
chemical substances [Kim et al., 2016], biological knowledge for mechanisms of
more than 15 thousand drugs [Kanehisa and Goto, 2000, Wishart et al., 2018].
By integrating these various kinds of data, computational methods, especially
deep learning, can be used to make highly accurate, inexpensive, and fast drug
side effect predictions. These results not only provide potential drug side effects but also potential mechanisms for further clinical verification to enhance
drug side effect studies. ...

参考文献

Canada

Vigilance

Program

Canada

vigilance

adverse

reaction

online

database.

https://www.canada.ca/en/

health-canada/services/drugs-health-products/medeffect-canada/

adverse-reaction-database.html, 2021. Online; accessed 25 May 2021.

Pharmaceutical and Medical Devices Agency . The japanese adverse drug

event report.

https://www.pmda.go.jp/safety/info-services/drugs/

adr-info/suspected-adr/0003.html, 2021. Online; accessed 15 March 2021.

Alan Agresti. A survey of exact inference for contingency tables. Statistical

science, 7(1):131–153, 1992.

Paola Alberti and G Cavaletti. Management of side effects in the personalized

medicine era: chemotherapy-induced peripheral neuropathy. In Pharmacogenomics in Drug Discovery and Development, pages 301–322. Springer, 2014.

Anima Anandkumar, Rong Ge, Daniel Hsu, and Sham M Kakade. A tensor approach to learning mixed membership community models. arxiv. org, 2013.

Song Bai, Feihu Zhang, and Philip HS Torr. Hypergraph convolution and hypergraph attention. arXiv preprint arXiv:1901.08150, 2019.

Brian A Baldo. Opioid analgesic drugs and serotonin toxicity (syndrome):

mechanisms, animal models, and links to clinical effects. Archives of toxicology, 92(8):2457–2473, 2018.

Juan M Banda, Lee Evans, Rami S Vanguri, Nicholas P Tatonetti, Patrick B Ryan,

and Nigam H Shah. A curated and standardized adverse drug event resource

to accelerate drug safety research. Scientific data, 3:160026, 2016.

83

Yujia Bao, Zhaobin Kuang, Peggy Peissig, et al. Hawkes process modeling

of adverse drug reactions with longitudinal observational data. In Machine

learning for healthcare conference, volume 2017, pages 177–190. Proceedings of

Machine Learning Research, 2017.

François Belleau, Marc-Alexandre Nolin, Nicole Tourigny, Philippe Rigault,

and Jean Morissette. Bio2rdf: towards a mashup to build bioinformatics

knowledge systems. Journal of biomedical informatics, 41(5):706–716, 2008.

Helen M Berman, John Westbrook, Zukang Feng, Gary Gilliland, Talapady N

Bhat, Helge Weissig, Ilya N Shindyalov, and Philip E Bourne. The protein

data bank. Nucleic acids research, 28(1):235–242, 2000.

Evan E Bolton, Sunghwan Kim, and Stephen H Bryant. Pubchem3d: conformer

generation. Journal of cheminformatics, 3(1):4, 2011.

Mei-Chun Cai, Quan Xu, Yan-Jing Pan, Wen Pan, Nan Ji, Yin-Bo Li, Hai-Jing Jin,

Ke Liu, and Zhi-Liang Ji. Adrecs: an ontology database for aiding standardization and hierarchical classification of adverse drug reaction terms. Nucleic

acids research, 43(D1):D907–D913, 2014.

Aurel Cami, Alana Arnold, Shannon Manzi, and Ben Reis. Predicting adverse

drug events using pharmacological network models. Science translational

medicine, 3(114):114ra127–114ra127, 2011.

D.S Cao, N Xiao, Y.J Li, et al. Integrating multiple evidence sources to predict adverse drug reactions based on a systems pharmacology model. CPT:

pharmacometrics & systems pharmacology, 4(9):498–506, 2015.

Carlos M Carvalho, Nicholas G Polson, and James G Scott. Handling sparsity

via the horseshoe. In Artificial Intelligence and Statistics, pages 73–80. PMLR,

2009.

T-H Hubert Chan and Zhibin Liang. Generalizing the hypergraph laplacian via

a diffusion process with mediators. Theoretical Computer Science, 806:416–428,

2020.

84

Andy W Chen. Predicting adverse drug reaction outcomes with machine learning. International Journal Of Community Medicine And Public Health, 5(3):901–

904, 2018.

Xin Chen, Zhi Liang Ji, and Yu Zong Chen. Ttd: therapeutic target database.

Nucleic acids research, 30(1):412–415, 2002.

Xiujie Chen, Xiangqiong Liu, Xiaodong Jia, Fujian Tan, Ruizhi Yang, Sheng

Chen, Lei Liu, Yunfeng Wang, and Yuelong Chen. Network characteristic

analysis of adr-related proteins and identification of adr-adr associations. Scientific reports, 3:1744, 2013.

Yun Gu Chen, Yin Ying Wang, and Xing Ming Zhao. A survey on computational approaches to predicting adverse drug reactions. Current topics in

medicinal chemistry, 16(30):3629–3635, 2016.

Xu Chu, Yang Lin, Yasha Wang, Leye Wang, Jiangtao Wang, and Jingyue Gao.

Mlrda: A multi-task semi-supervised learning framework for drug-drug interaction prediction. In Proceedings of the 28th International Joint Conference on

Artificial Intelligence, pages 4518–4524. AAAI Press, 2019.

Kathryn Corrie and Jonathan G Hardman. Mechanisms of drug interactions: pharmacodynamics and pharmacokinetics. Anaesthesia & Intensive Care

Medicine, 12(4):156–159, 2011.

Patrizia Crivori, Gabriele Cruciani, Pierre Alain Carrupt, et al. Predicting

blood- brain barrier permeation from three-dimensional molecular structure.

Journal of medicinal chemistry, 43(11):2204–2216, 2000.

Gabriele Cruciani, Emanuele Carosati, Benoit De Boeck, Kantharaj Ethirajulu,

Claire Mackie, Trevor Howe, and Riccardo Vianello. Metasite: understanding metabolism in human cytochromes from the perspective of the chemist.

Journal of medicinal chemistry, 48(22):6970–6979, 2005.

Behrooz Davazdahemami and Dursun Delen. A chronological pharmacovigilance network analytics approach for predicting adverse drug events. Journal

of the American Medical Informatics Association, 25(10):1311–1321, 2018.

85

Allan Peter Davis, Cynthia G Murphy, Cynthia A Saraceni-Richards, Michael C

Rosenstein, Thomas C Wiegers, and Carolyn J Mattingly. Comparative toxicogenomics database: a knowledgebase and discovery tool for chemical–

gene–disease networks. Nucleic acids research, 37(suppl_1):D786–D792, 2008.

Sanjoy Dey, Heng Luo, Achille Fokoue, Jianying Hu, and Ping Zhang. Predicting adverse drug reactions through interpretable deep learning framework.

BMC bioinformatics, 19(21):1–13, 2018.

Giovanna Maria Dimitri and Pietro Lió. Drugclust: a machine learning approach for drugs side effects prediction. Computational biology and chemistry,

68:204–210, 2017.

David W Dodick, Vincent T Martin, Timothy Smith, and Stephen Silberstein.

Cardiovascular tolerability and safety of triptans: a review of clinical data.

Headache: The Journal of Head and Face Pain, 44:S20–S30, 2004.

Anthony M Downs, Xueliang Fan, Christine Donsante, HA Jinnah, and Ellen J

Hess. Trihexyphenidyl rescues the deficit in dopamine neurotransmission in

a mouse model of dyt1 dystonia. Neurobiology of disease, 125:115–122, 2019.

Drugs.com. Drug interactions checker. 2021. Online; accessed 25 Dec 2021.

Joseph L Durant, Burton A Leland, Douglas R Henry, and James G Nourse.

Reoptimization of mdl keys for use in drug discovery. Journal of chemical

information and computer sciences, 42(6):1273–1280, 2002.

Ehsan Emadzadeh, Abeed Sarker, Azadeh Nikfarjam, and Graciela Gonzalez.

Hybrid semantic analysis for mapping adverse drug reaction mentions in

tweets to medical terminology. In AMIA Annual Symposium Proceedings, volume 2017, page 679. American Medical Informatics Association, PubMed

Central, 2017.

Michela Fagiolini, Jean-Marc Fritschy, Karin Löw, Hanns Möhler, Uwe

Rudolph, and Takao K Hensch. Specific gabaa circuits for visual cortical

plasticity. Science, 303(5664):1681–1683, 2004.

86

Haoyi Fan, Fengbin Zhang, Yuxuan Wei, Zuoyong Li, Changqing Zou, Yue

Gao, and Qionghai Dai. Heterogeneous hypergraph variational autoencoder

for link prediction. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021.

Yifan Feng, Haoxuan You, Zizhao Zhang, Rongrong Ji, and Yue Gao. Hypergraph neural networks. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 33, pages 3558–3565, 2019.

Yue-Hua Feng, Shao-Wu Zhang, and Jian-Yu Shi. Dpddi: A deep predictor for

drug-drug interactions. BMC bioinformatics, 21(1):1–15, 2020.

Justin Gilmer, Samuel S Schoenholz, Patrick F Riley, Oriol Vinyals, and

George E Dahl. Neural message passing for quantum chemistry. In Proceedings of the 34th International Conference on Machine Learning-Volume 70, pages

1263–1272. JMLR. org, 2017.

Peter J Goodford. A computational procedure for determining energetically

favorable binding sites on biologically important macromolecules. Journal of

medicinal chemistry, 28(7):849–857, 1985.

Francesca Grisoni, Davide Ballabio, Roberto Todeschini, et al. Molecular descriptors for structure–activity applications: A hands-on approach. In Computational Toxicology, pages 3–53. Springer, 2018.

Stefan Günther, Michael Kuhn, Mathias Dunkel, Monica Campillos, Christian

Senger, Evangelia Petsalaki, Jessica Ahmed, Eduardo Garcia Urdiales, Andreas Gewiess, Lars Juhl Jensen, et al. Supertarget and matador: resources

for exploring drug-target relationships. Nucleic acids research, 36(suppl_1):

D919–D922, 2007.

Lowell H Hall and Lemont B Kier. Electrotopological state indices for atom

types: a novel combination of electronic, topological, and valence state information. Journal of Chemical Information and Computer Sciences, 35(6):1039–1045,

1995.

87

Trevor Hastie. Statistical learning with sparsity : the lasso and generalizations.

Chapman and Hall/CRC monographs on statistics and applied probability ;

143. CRC Press, Boca Raton, FL, 2015. ISBN 9781498712163.

Tu Bao Ho, Ly Le, Dang T Thai, et al. Data-driven approach to detect and

predict adverse drug reactions. Current pharmaceutical design, 22(23):3498–

3526, 2016.

Jennifer A Hoeting, David Madigan, Adrian E Raftery, and Chris T Volinsky.

Bayesian model averaging: a tutorial (with comments by m. clyde, david

draper and ei george, and a rejoinder by the authors. Statistical science, 14(4):

382–417, 1999.

Brooke E Hoots, Likang Xu, Mbabazi Kariisa, et al. 2018 annual surveillance

report of drug-related risks and outcomes–united states. CDC National Center

for Injury Prevention and Control, 2018.

George Hripcsak, Jon D Duke, Nigam H Shah, Christian G Reich, Vojtech

Huser, Martijn J Schuemie, Marc A Suchard, Rae Woong Park, Ian Chi Kei

Wong, Peter R Rijnbeek, et al. Observational health data sciences and informatics (ohdsi): opportunities for observational researchers. Studies in health

technology and informatics, 216:574, 2015.

GJ Huba, Joseph A Wingard, and Peter M Bentler. A comparison of two latent

variable causal models for adolescent drug use. Journal of Personality and

Social Psychology, 40(1):180, 1981.

Trung Huynh, Yulan He, Alistair Willis, et al. Adverse drug reaction classification with deep neural networks. In Proceedings of COLING. Coling, COLING,

2016.

Md Jamiul Jahid and Jianhua Ruan. An ensemble approach for drug side effect

prediction. In Bioinformatics and Biomedicine (BIBM), 2013 IEEE International

Conference on, volume 2013, pages 440–445. IEEE, IEEE, 2013.

Zhi Liang Ji, Lian Yi Han, Chun Wei Yap, Li Zhi Sun, Xin Chen, and Yu Zong

Chen. Drug adverse reaction target database (dart). Drug safety, 26(10):685–

690, 2003.

88

Yanping Jiang, Yizhou Li, Qifan Kuang, Ling Ye, Yiming Wu, Lijun Yang, and

Menglong Li. Predicting putative adverse drug reaction related proteins

based on network topological properties. Analytical Methods, 6(8):2692–2698,

2014.

Minoru Kanehisa and Susumu Goto. Kegg: kyoto encyclopedia of genes and

genomes. Nucleic acids research, 28(1):27–30, 2000.

TS Keshava Prasad, Renu Goel, Kumaran Kandasamy, Shivakumar Keerthikumar, Sameer Kumar, Suresh Mathivanan, Deepthi Telikicherla, Rajesh Raju,

Beema Shafreen, Abhilash Venugopal, et al. Human protein reference

database—2009 update. Nucleic acids research, 37(suppl_1):D767–D772, 2008.

Sunghwan Kim, Paul A Thiessen, Evan E Bolton, Jie Chen, Gang Fu, Asta

Gindulyte, Lianyi Han, Jane He, Siqian He, Benjamin A Shoemaker, et al.

Pubchem substance and compound databases. Nucleic acids research, 44(D1):

D1202–D1213, 2015.

Sunghwan Kim, Paul A Thiessen, Evan E Bolton, Jie Chen, Gang Fu, Asta

Gindulyte, Lianyi Han, Jane He, Siqian He, Benjamin A Shoemaker, et al.

Pubchem substance and compound databases. Nucleic acids research, 44(D1):

D1202–D1213, 2016.

Thomas N Kipf and Max Welling. Semi-supervised classification with graph

convolutional networks. arXiv preprint arXiv:1609.02907, 2016.

Justin Klekota and Frederick P Roth. Chemical substructures that enrich for

biological activity. Bioinformatics, 24(21):2518–2525, 2008.

Hugo Kubinyi. Comparative molecular field analysis (comfa). The encyclopedia

of computational chemistry, 1:448–460, 1998.

Michael Kuhn, Monica Campillos, Ivica Letunic, Lars Juhl Jensen, and Peer

Bork. A side effect resource to capture phenotypic effects of drugs. Molecular

systems biology, 6(1):343, 2010.

Michael Kuhn, Ivica Letunic, Lars Juhl Jensen, et al. The sider database of drugs

and side effects. Nucleic acids research, 44(D1):D1075–D1079, 2015.

89

Kathy Lee, Ashequl Qadir, Sadid A Hasan, Vivek Datla, Aaditya Prakash, Joey

Liu, and Oladimeji Farri. Adverse drug event detection in tweets with semisupervised convolutional neural networks. In Proceedings of the 26th International Conference on World Wide Web, pages 705–714. International World Wide

Web Conferences Steering Committee, WWW, 2017.

Chih-Jen Lin. Projected gradient methods for nonnegative matrix factorization.

Neural computation, 19(10):2756–2779, 2007.

Jiao Lin, Qifan Kuang, Yizhou Li, and et al. Prediction of adverse drug reactions

by a network based external link prediction method. Analytical Methods, 5

(21):6120–6127, 2013.

Christopher A Lipinski, Franco Lombardo, Beryl W Dominy, et al. Experimental and computational approaches to estimate solubility and permeability in

drug discovery and development settings. Advanced drug delivery reviews, 23

(1-3):3–25, 1997.

Mei Liu, Yonghui Wu, Yukun Chen, Jingchun Sun, Zhongming Zhao, Xue-wen

Chen, Michael Edwin Matheny, and Hua Xu. Large-scale prediction of adverse drug reactions using chemical, biological, and phenotypic properties of

drugs. Journal of the American Medical Informatics Association, 19(e1):e28–e35,

2012.

Tiqing Liu, Yuhmei Lin, Xin Wen, Robert N Jorissen, and Michael K

Gilson. Bindingdb: a web-accessible database of experimentally determined

protein–ligand binding affinities. Nucleic acids research, 35(suppl_1):D198–

D201, 2006.

Lara Magro, Ugo Moretti, and Roberto Leone. Epidemiology and characteristics of adverse drug reactions caused by drug–drug interactions. Expert

opinion on drug safety, 11(1):83–94, 2012.

R Mahadevan and CH Schilling. The effects of alternate optimal solutions in

constraint-based genome-scale metabolic models. Metabolic engineering, 5(4):

264–276, 2003.

90

Ronald D Mann and Elizabeth B Andrews. Pharmacovigilance. John Wiley &

Sons, 2007.

Jean-Louis Montastruc, Agnès Sommet, Haleh Bagheri, and Maryse LapeyreMestre. Benefits and strengths of the disproportionality analysis for identification of adverse drug reactions in a pharmacovigilance database. British

journal of clinical pharmacology, 72(6):905–908, 2011.

Emir Muñoz, Vít Nováˇcek, and Pierre-Yves Vandenbussche. Using drug similarities for discovery of possible adverse reactions. In AMIA Annual Symposium Proceedings, volume 2016, page 924. American Medical Informatics

Association, 2016.

Emir Muñoz, Vít Nováˇcek, and Pierre-Yves Vandenbussche. Facilitating prediction of adverse drug reactions by using knowledge graphs and multi-label

learning models. Briefings in bioinformatics, 20(1):190–202, 2017.

Duc Anh Nguyen, Canh Hao Nguyen, and Hiroshi Mamitsuka. A survey on

adverse drug reaction studies: data, tasks and machine learning methods.

Briefings in bioinformatics, 22(1):164–177, 2021.

Duc Anh Nguyen, Canh Hao Nguyen, and Hiroshi Mamitsuka. Centsmoothie:

Central-smoothing hypergraph neural networks for predicting drug-drug interactions. arXiv preprint arXiv:2112.07837, 2022a.

Duc Anh Nguyen, Canh Hao Nguyen, Peter Petschner, and Hiroshi Mamitsuka. Sparse: a sparse hypergraph neural network for learning multiple

types of latent combinations to accurately predict drug-drug interactions.

Bioinformatics, 38(Supplement_1):i333–i341, 2022b.

Hao Canh Nguyen and Hiroshi Mamitsuka. Learning on hypergraphs with

sparsity. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2020.

William W Ogden, Donald M Bradburn II, and James D Rives. Panniculitis of

the mesentery. Annals of surgery, 151(5):659, 1960.

Soumik Pal and Yizhe Zhu. Community detection in the sparse hypergraph

stochastic block model. Random Structures & Algorithms, 2021.

91

Edouard Pauwels, Véronique Stoven, and Yoshihiro Yamanishi. Predicting

drug side-effect profiles: a chemical fragment-based approach. BMC bioinformatics, 12(1):169, 2011.

Juho Piironen and Aki Vehtari. Sparsity information and regularization in the

horseshoe and other shrinkage priors. Electronic Journal of Statistics, 11(2):

5018–5051, 2017.

Aleksandar Poleksic and Lei Xie. Predicting serious rare adverse reactions of

novel chemicals. Bioinformatics, 1:8, 2018.

Naresh Poloju and Purushotham Muniganti. Adverse drug reaction detection

using data mining approaches: A survey. Internatinal journal of recent trends

in engineering and research, 2018, 2018.

Dilli Ram Poudel, Prakash Acharya, Sushil Ghimire, Rashmi Dhital, and Rajani

Bharati. Burden of hospitalizations related to adverse drug events in the usa:

a retrospective analysis from large inpatient database. Pharmacoepidemiology

and drug safety, 26(6):635–641, 2017.

Hossein Rahmani, Gerhard Weiss, Oscar Méndez-Lucio, et al. Arwar: A network approach for predicting adverse drug reactions. Computers in biology

and medicine, 68:101–108, 2016.

Jong M Rho, Sean D Donevan, and Michael A Rogawski. Barbiturate-like actions of the propanediol dicarbamates felbamate and meprobamate. Journal

of Pharmacology and Experimental Therapeutics, 280(3):1383–1391, 1997.

Michael J Rieder. Mechanisms of unpredictable adverse drug reactions. Drug

Safety, 11(3):196–212, 1994.

Johannes Ring and Knut Brockow. Adverse drug reactions: mechanisms and

assessment. European surgical research, 34(1-2):170–175, 2002.

James Ritter, Rod J Flower, Graeme Henderson, Yoon Kong Loke, David J

MacEwan, and Humphrey P Rang. Rang and dale’s pharmacology. 2019.

92

Narjes Rohani and Changiz Eslahchi. Drug-drug interaction predicting by neural network using integrated similarity. Scientific reports, 9(1):1–11, 2019.

Takaya Saito and Marc Rehmsmeier. The precision-recall plot is more informative than the roc plot when evaluating binary classifiers on imbalanced

datasets. PloS one, 10(3):e0118432, 2015.

Itay Shaked, Matthew A Oberhardt, Nir Atias, Roded Sharan, and Eytan Ruppin. Metabolic network prediction of drug side effects. Cell systems, 2(3):

209–213, 2016.

Shawn E Simpson, David Madigan, Ivan Zorych, Martijn J Schuemie, Patrick B

Ryan, and Marc A Suchard. Multiple self-controlled case series for large-scale

longitudinal observational databases. Biometrics, 69(4):893–902, 2013.

Paul E Stang, Patrick B Ryan, Judith A Racoosin, J Marc Overhage, Abraham G

Hartzema, Christian Reich, Emily Welebob, Thomas Scarnecchia, and Janet

Woodcock. Advancing the science for active surveillance: rationale and design for the observational medical outcomes partnership. Annals of internal

medicine, 153(9):600–606, 2010.

Christoph Steinbeck, Yongquan Han, Stefan Kuhn, Oliver Horlacher, Edgar

Luttmann, and Egon Willighagen. The chemistry development kit (cdk): An

open-source java library for chemo-and bioinformatics. Journal of chemical

information and computer sciences, 43(2):493–500, 2003.

Marco Stieger, Jean-Paul Schmid, Nikhil Yawalkar, and Thomas Hunziker. Extracorporeal shock wave therapy for injection site panniculitis in multiple

sclerosis patients. Dermatology, 230(1):82–86, 2015.

Halis Suleyman, Abdulmecit Albayrak, Mehmet Bilici, Elif Cadirci, and Zekai

Halici. Different mechanisms in formation and prevention of indomethacininduced gastric ulcers. Inflammation, 33(4):224–234, 2010.

Nicholas P Tatonetti, P Ye Patrick, Roxana Daneshjou, and Russ B Altman.

Data-driven prediction of drug effects and interactions. Science translational

medicine, 4(125):125ra31–125ra31, 2012.

93

Bernard Testa and Lemont B Kier. The concept of molecular structure in

structure–activity relationship studies and drug design. Medicinal research

reviews, 11(1):35–48, 1991.

Bernard Testa, Giulia Caron, Patrizia Crivori, Sébastien Rey, Marianne Reist,

and Pierre Alain Carrupt. Lipophilicity and related molecular properties as

determinants of pharmacokinetic behaviour. CHIMIA International Journal for

Chemistry, 54(11):672–677, 2000.

Daylight. Daylight theory: Fingerprint, 2018. http://www.daylight.com/

dayhtml/doc/theory/theory.finger.html (10 Sep 2019, date last accessed).

FDA.

Questions and answers on fda’s adverse event reporting system (faers),

2019.

https://www.fda.gov/drugs/surveillance/

fda-adverse-event-reporting-system-faers, 10 Sep 2019, date last

accessed.

Openeye scientific software. OMEGA, 2018.

omega (10 Nov 2018, date last accessed).

https://www.eyesopen.com/

WHO. Definitions, 1972. http://www.who.int/medicines/areas/quality_

safety/safety_efficacy/trainingcourses/definitions.pdf (10 Sep 2019,

date last accessed).

WHO. Guidelines for atc classification and ddd assignment, 2019. https://

www.whocc.no/filearchive/publications/2019_guidelines_web.pdf, (10

Sep 2019, date last accessed).

Mike Thelwall, Kayvan Kousha, and Mahshid Abdoli. Is medical research informing professional practice more highly cited? evidence from ahfs di essentials in drugs. com. Scientometrics, 112(1):509–527, 2017.

Robert Tibshirani. Regression shrinkage and selection via the lasso. Journal of

the Royal Statistical Society: Series B (Methodological), 58(1):267–288, 1996.

Roberto Todeschini and Viviana Consonni. Handbook of molecular descriptors,

volume 11. John Wiley & Sons, 2008.

94

Hanghang Tong, Christos Faloutsos, and Jia-Yu Pan. Fast random walk with

restart and its applications. In Sixth International Conference on Data Mining

(ICDM’06), pages 613–622. IEEE, IEEE, 2006.

Han van de Waterbeemd and Manfred Kansy. Hydrogen-bonding capacity and

brain penetration. CHIMIA International Journal for Chemistry, 46(7-8):299–

303, 1992.

Harini Venkataraman, Michiel W Den Braver, Nico PE Vermeulen, and Jan NM

Commandeur. Cytochrome p450-mediated bioactivation of mefenamic acid

to quinoneimine intermediates and inactivation by human glutathione stransferases. Chemical research in toxicology, 27(12):2071–2081, 2014.

Izhar Wallach, Navdeep Jaitly, and Ryan Lilien. A structure-based approach for

mapping adverse drug reactions to the perturbation of underlying biological

pathways. PloS one, 5(8):e12063, 2010.

Chi-Shiang Wang, Pei-Ju Lin, Ching-Lan Cheng, Shu-Hua Tai, Yea-Huei Kao

Yang, and Jung-Hsien Chiang. Detecting potential adverse drug reactions

using a deep neural network model. Journal of medical Internet research, 21(2):

e11016, 2019.

Juan Wang, Zhi-xin Li, Cheng-xiang Qiu, Dong Wang, and Qing-hua Cui. The

relationship between rational drug design and drug side effects. Briefings in

bioinformatics, 13(3):377–382, 2011.

Xiujuan Wang, Bram Thijssen, and Haiyuan Yu. Target essentiality and centrality characterize drug side effects. PLoS computational biology, 9(7):e1003119,

2013.

AJ Weiss, A Elixhauser, J Bae, et al. Origin of adverse drug events in us hospitals, 2011. HCUP Statistical Brief, 158, 2013.

Camille Georges Wermuth. The practice of medicinal chemistry. Academic Press,

2011.

95

David S Wishart, Craig Knox, An Chi Guo, Dean Cheng, Savita Shrivastava,

Dan Tzur, Bijaya Gautam, and Murtaza Hassanali. Drugbank: a knowledgebase for drugs, drug actions and drug targets. Nucleic acids research, 36

(suppl_1):D901–D906, 2007.

David S Wishart, Yannick D Feunang, An C Guo, Elvis J Lo, Ana Marcu, Jason R

Grant, Tanvir Sajed, Daniel Johnson, Carin Li, Zinat Sayeeda, et al. Drugbank

5.0: a major update to the drugbank database for 2018. Nucleic acids research,

46(D1):D1074–D1082, 2018.

Zonghan Wu, Shirui Pan, Fengwen Chen, Guodong Long, Chengqi Zhang, and

S Yu Philip. A comprehensive survey on graph neural networks. IEEE Transactions on Neural Networks and Learning Systems, 2020.

Cao Xiao, Ping Zhang, W Art Chaowalitwongse, Jianying Hu, and Fei Wang.

Adverse drug reaction prediction with symbolic latent dirichlet allocation. In

Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence. AAAI

Press, 2017.

Nuo Xu, Pinghui Wang, Long Chen, Jing Tao, and Junzhou Zhao. Mr-gnn:

Multi-resolution and dual graph neural network for predicting structured

entity interactions. arXiv preprint arXiv:1905.09558, 2019.

Naganand Yadati. Neural message passing for multi-relational ordered and

recursive hypergraphs. Advances in Neural Information Processing Systems, 33,

2020.

Yoshihiro Yamanishi, Edouard Pauwels, and Masaaki Kotera. Drug side-effect

prediction based on the integration of chemical and biological spaces. Journal

of chemical information and modeling, 52(12):3284–3292, 2012.

Fan Yang, Xiaohui Yu, and George Karypis. Signaling adverse drug reactions

with novel feature-based similarity model. In Bioinformatics and Biomedicine

(BIBM), 2014 IEEE International Conference on, pages 593–596. IEEE, IEEE,

2014.

96

Rodney C Young, Robert C Mitchell, Thomas H Brown, C Robin Ganellin,

Robin Griffiths, Martin Jones, Kishore K Rana, David Saunders, and Ian R

Smith. Development of a new physicochemical model for brain penetration

and its application to the design of centrally acting h2 receptor histamine

antagonists. Journal of medicinal chemistry, 31(3):656–671, 1988.

Chuxu Zhang, Dongjin Song, Chao Huang, Ananthram Swami, and Nitesh V

Chawla. Heterogeneous graph neural network. In Proceedings of the 25th

ACM SIGKDD International Conference on Knowledge Discovery & Data Mining,

pages 793–803, 2019a.

Wen Zhang, Feng Liu, Longqiang Luo, et al. Predicting drug side effects by

multi-label learning and ensemble learning. BMC bioinformatics, 16(1):365,

2015.

Wen Zhang, Yanlin Chen, Shikui Tu, Feng Liu, and Qianlong Qu. Drug side

effect prediction through linear neighborhoods and multiple data source integration. In 2016 IEEE international conference on bioinformatics and biomedicine

(BIBM), pages 427–434. IEEE, 2016.

Yun Zhang, Kehui Chen, Allan Sampson, Kai Hwang, and Beatriz Luna. Node

features adjusted stochastic block model. Journal of Computational and Graphical Statistics, 28(2):362–373, 2019b.

Huiru Zheng, Haiying Wang, Hua Xu, Yonghui Wu, Zhongming Zhao, and

Francisco Azuaje. Linking biochemical pathways and networks to adverse

drug reactions. IEEE transactions on nanobioscience, 13(2):131–137, 2014.

Marinka Zitnik, Monica Agrawal, and Jure Leskovec. Modeling polypharmacy

side effects with graph convolutional networks. Bioinformatics, 34(13):i457–

i466, 2018.

97

...

参考文献をもっと見る

全国の大学の
卒論・修論・学位論文

一発検索!

この論文の関連論文を見る