Predicting human microbe-drug associations via graph convolutional network with conditional random field.
TLDR
A novel Graph Convolutional Network (GCN) based framework for predicting human Microbe-Drug Associations, named GCNMDA is proposed, which consistently achieved better performance than seven state-of-the-art methods.Abstract:
MOTIVATION: Human microbes play critical roles in drug development and precision medicine How to systematically understand the complex interaction mechanism between human microbes and drugs remains a challenge nowadays Identifying microbe-drug associations can not only provide great insights into understanding the mechanism, but also boost the development of drug discovery and repurposing Considering the high cost and risk of biological experiments, the computational approach is an alternative choice However, at present, few computational approaches have been developed to tackle this task RESULTS: In this work, we leveraged rich biological information to construct a heterogeneous network for drugs and microbes, including a microbe similarity network, a drug similarity network and a microbe-drug interaction network We then proposed a novel graph convolutional network (GCN)-based framework for predicting human Microbe-Drug Associations, named GCNMDA In the hidden layer of GCN, we further exploited the Conditional Random Field (CRF), which can ensure that similar nodes (ie microbes or drugs) have similar representations To more accurately aggregate representations of neighborhoods, an attention mechanism was designed in the CRF layer Moreover, we performed a random walk with restart-based scheme on both drug and microbe similarity networks to learn valuable features for drugs and microbes, respectively Experimental results on three different datasets showed that our GCNMDA model consistently achieved better performance than seven state-of-the-art methods Case studies for three microbes including SARS-CoV-2 and two antimicrobial drugs (ie Ciprofloxacin and Moxifloxacin) further confirmed the effectiveness of GCNMDA in identifying potential microbe-drug associations AVAILABILITY AND IMPLEMENTATION: Python codes and dataset are available at: https://githubcom/longyahui/GCNMDA SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics onlineread more
Citations
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Predicting the potential human lncRNA-miRNA interactions based on graph convolution network with conditional random field
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Association predictions of genomics, proteinomics, transcriptomics, microbiome, metabolomics, pathomics, radiomics, drug, symptoms, environment factor, and disease networks: A comprehensive approach
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References
More filters
Proceedings Article
Adam: A Method for Stochastic Optimization
Diederik P. Kingma,Jimmy Ba +1 more
TL;DR: This work introduces Adam, an algorithm for first-order gradient-based optimization of stochastic objective functions, based on adaptive estimates of lower-order moments, and provides a regret bound on the convergence rate that is comparable to the best known results under the online convex optimization framework.
Posted Content
Semi-Supervised Classification with Graph Convolutional Networks
Thomas Kipf,Max Welling +1 more
TL;DR: A scalable approach for semi-supervised learning on graph-structured data that is based on an efficient variant of convolutional neural networks which operate directly on graphs which outperforms related methods by a significant margin.
Journal ArticleDOI
SARS-CoV-2 Cell Entry Depends on ACE2 and TMPRSS2 and Is Blocked by a Clinically Proven Protease Inhibitor
Markus Hoffmann,Hannah Kleine-Weber,Simon Schroeder,Nadine Krüger,Tanja Herrler,Sandra Erichsen,Tobias S. Schiergens,Georg Herrler,Nai Huei Wu,Andreas Nitsche,Marcel A. Müller,Christian Drosten,Christian Drosten,Stefan Pöhlmann +13 more
TL;DR: It is demonstrated that SARS-CoV-2 uses the SARS -CoV receptor ACE2 for entry and the serine protease TMPRSS2 for S protein priming, and it is shown that the sera from convalescent SARS patients cross-neutralized Sars-2-S-driven entry.
Journal ArticleDOI
Structure, function and diversity of the healthy human microbiome
Curtis Huttenhower,Curtis Huttenhower,Dirk Gevers,Rob Knight,Rob Knight,Sahar Abubucker,Jonathan H. Badger,Asif T. Chinwalla,Heather Huot Creasy,Ashlee M. Earl,Michael Fitzgerald,Robert S. Fulton,Michelle G. Giglio,Kymberlie Hallsworth-Pepin,Elizabeth A. Lobos,Ramana Madupu,Vincent Magrini,John Martin,Makedonka Mitreva,Donna M. Muzny,Erica Sodergren,James Versalovic,Aye Wollam,Kim C. Worley,Jennifer R. Wortman,Sarah Young,Qiandong Zeng,Kjersti Aagaard,Olukemi O. Abolude,Emma Allen-Vercoe,Eric J. Alm,Eric J. Alm,Lucia Alvarado,Gary L. Andersen,Scott Anderson,Elizabeth L. Appelbaum,Harindra Arachchi,Gary C. Armitage,Cesar Arze,Tulin Ayvaz,Carl C. Baker,Lisa Begg,Tsegahiwot Belachew,Veena Bhonagiri,Monika Bihan,Martin J. Blaser,Toby Bloom,Vivien Bonazzi,J. Paul Brooks,Gregory A. Buck,Christian J. Buhay,Dana A. Busam,Joseph L. Campbell,Shane Canon,Brandi L. Cantarel,Patrick S. G. Chain,Patrick S. G. Chain,I. Min A. Chen,Lei Chen,Shaila Chhibba,Ken Chu,Dawn Ciulla,Jose C. Clemente,Sandra W. Clifton,Sean Conlan,Jonathan Crabtree,Mary A. Cutting,Noam J. Davidovics,Catherine C. Davis,Todd Z. DeSantis,Carolyn Deal,Kimberley D. Delehaunty,Floyd E. Dewhirst,Elena Deych,Yan Ding,David J. Dooling,Shannon Dugan,Wm. Michael Dunne,Wm. Michael Dunne,A. Scott Durkin,Robert C. Edgar,Rachel L. Erlich,Candace N. Farmer,Ruth M. Farrell,Karoline Faust,Michael Feldgarden,Victor Felix,Sheila Fisher,Anthony A. Fodor,Larry J. Forney,Leslie Foster,Valentina Di Francesco,Jonathan Friedman,Dennis C. Friedrich,Catrina Fronick,Lucinda Fulton,Hongyu Gao,Nathalia Garcia,Georgia Giannoukos,Christina Giblin,Maria Y. Giovanni,Jonathan M. Goldberg,Johannes B. Goll,Antonio Gonzalez,Allison D. Griggs,Sharvari Gujja,Susan Kinder Haake,Brian J. Haas,Holli A. Hamilton,Emily L. Harris,Theresa A. Hepburn,Brandi Herter,Diane E. Hoffmann,Michael Holder,Clinton Howarth,Katherine H. Huang,Susan M. Huse,Jacques Izard,Janet K. Jansson,Huaiyang Jiang,Catherine Jordan,Vandita Joshi,James A. Katancik,Wendy A. Keitel,Scott T. Kelley,Cristyn Kells,Nicholas B. King,Dan Knights,Heidi H. Kong,Omry Koren,Sergey Koren,Karthik Kota,Christie Kovar,Nikos C. Kyrpides,Patricio S. La Rosa,Sandra L. Lee,Katherine P. Lemon,Niall J. Lennon,Cecil M. Lewis,Lora Lewis,Ruth E. Ley,Kelvin Li,Konstantinos Liolios,Bo Liu,Yue Liu,Chien Chi Lo,Catherine A. Lozupone,R. Dwayne Lunsford,Tessa Madden,Anup Mahurkar,Peter J. Mannon,Elaine R. Mardis,Victor M. Markowitz,Victor M. Markowitz,Konstantinos Mavromatis,Jamison McCorrison,Daniel McDonald,Jean E. McEwen,Amy L. McGuire,Pamela McInnes,Teena Mehta,Kathie A. Mihindukulasuriya,Jason R. Miller,Patrick Minx,Irene Newsham,Chad Nusbaum,Michelle Oglaughlin,Joshua Orvis,Ioanna Pagani,Krishna Palaniappan,Shital M. Patel,Matthew D. Pearson,Jane Peterson,Mircea Podar,Craig Pohl,Katherine S. Pollard,Mihai Pop,Margaret Priest,Lita M. Proctor,Xiang Qin,Jeroen Raes,Jacques Ravel,Jeffrey G. Reid,Mina Rho,Rosamond Rhodes,Kevin Riehle,Maria C. Rivera,Beltran Rodriguez-Mueller,Yu-Hui Rogers,Matthew C. Ross,Carsten Russ,Ravi Sanka,Pamela Sankar,J. Fah Sathirapongsasuti,Jeffery A. Schloss,Patrick D. Schloss,Thomas M. Schmidt,Matthew B. Scholz,Lynn M. Schriml,Alyxandria M. Schubert,Nicola Segata,Julia A. Segre,William D. Shannon,Richard R. Sharp,Thomas J. Sharpton,Narmada Shenoy,Nihar U. Sheth,Gina A. Simone,Indresh Singh,Christopher Smillie,Jack D. Sobel,Daniel D. Sommer,Paul Spicer,Granger G. Sutton,Sean M. Sykes,Diana Tabbaa,Mathangi Thiagarajan,Chad Tomlinson,Manolito Torralba,Todd J. Treangen,Rebecca Truty,Tatiana A. Vishnivetskaya,Jason Walker,Lu Wang,Zhengyuan Wang,Doyle V. Ward,Wesley C. Warren,Mark A. Watson,Christopher Wellington,Kris A. Wetterstrand,James R. White,Katarzyna Wilczek-Boney,Yuanqing Wu,Kristine M. Wylie,Todd Wylie,Chandri Yandava,Liang Ye,Yuzhen Ye,Shibu Yooseph,Bonnie P. Youmans,Lan Zhang,Yanjiao Zhou,Yiming Zhu,Laurie Zoloth,Jeremy Zucker,Bruce W. Birren,Richard A. Gibbs,Sarah K. Highlander,Barbara A. Methé,Karen E. Nelson,Joseph F. Petrosino,George M. Weinstock,Richard K. Wilson,Owen White +253 more
TL;DR: The Human Microbiome Project Consortium reported the first results of their analysis of microbial communities from distinct, clinically relevant body habitats in a human cohort; the insights into the microbial communities of a healthy population lay foundations for future exploration of the epidemiology, ecology and translational applications of the human microbiome as discussed by the authors.