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Volumn 15, Issue , 2019, Pages 68-73

From genotype to phenotype: augmenting deep learning with networks and systems biology

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHM; ARTIFICIAL NEURAL NETWORK; DEEP LEARNING; GENE MUTATION; GENOTYPE; HUMAN; KNOWLEDGE; LEARNING ALGORITHM; PHENOTYPE; PREDICTION; REVIEW; SYSTEMS BIOLOGY;

EID: 85064803940     PISSN: None     EISSN: 24523100     Source Type: Journal    
DOI: 10.1016/j.coisb.2019.04.001     Document Type: Review
Times cited : (28)

References (43)
  • 1
    • 78650373804 scopus 로고    scopus 로고
    • Network medicine: a network-based approach to human disease
    • Barabási, A.-L., Gulbahce, N., Loscalzo, J., Network medicine: a network-based approach to human disease. Nat Rev Genet, 12, 2011, 56.
    • (2011) Nat Rev Genet , vol.12 , pp. 56
    • Barabási, A.-L.1    Gulbahce, N.2    Loscalzo, J.3
  • 2
    • 79952674000 scopus 로고    scopus 로고
    • Interactome networks and human disease
    • Vidal, M., Cusick, M.E., Barabási, A.-L., Interactome networks and human disease. Cell 144 (2011), 986–998.
    • (2011) Cell , vol.144 , pp. 986-998
    • Vidal, M.1    Cusick, M.E.2    Barabási, A.-L.3
  • 4
    • 0036500993 scopus 로고    scopus 로고
    • Systems biology: a brief overview
    • Kitano, H., Systems biology: a brief overview. Science 295 (2002), 1662–1664.
    • (2002) Science , vol.295 , pp. 1662-1664
    • Kitano, H.1
  • 5
    • 85027491351 scopus 로고    scopus 로고
    • Network propagation: a universal amplifier of genetic associations
    • Cowen, L., Ideker, T., Raphael, B.J., Sharan, R., Network propagation: a universal amplifier of genetic associations. Nat Rev Genet 18 (2017), 551–562.
    • (2017) Nat Rev Genet , vol.18 , pp. 551-562
    • Cowen, L.1    Ideker, T.2    Raphael, B.J.3    Sharan, R.4
  • 10
    • 85050595396 scopus 로고    scopus 로고
    • Deep learning for healthcare: review, opportunities and challenges
    • Miotto, R., Wang, F., Wang, S., Jiang, X., Dudley, J.T., Deep learning for healthcare: review, opportunities and challenges. Briefings Bioinf 19 (2018), 1236–1246.
    • (2018) Briefings Bioinf , vol.19 , pp. 1236-1246
    • Miotto, R.1    Wang, F.2    Wang, S.3    Jiang, X.4    Dudley, J.T.5
  • 11
    • 85032586119 scopus 로고    scopus 로고
    • Deep learning in bioinformatics
    • Min, S., Lee, B., Yoon, S., Deep learning in bioinformatics. Briefings Bioinf 18 (2017), 851–869.
    • (2017) Briefings Bioinf , vol.18 , pp. 851-869
    • Min, S.1    Lee, B.2    Yoon, S.3
  • 12
    • 84968861400 scopus 로고    scopus 로고
    • Applications of deep learning in biomedicine
    • Mamoshina, P., Vieira, A., Putin, E., Zhavoronkov, A., Applications of deep learning in biomedicine. Mol Pharm 13 (2016), 1445–1454.
    • (2016) Mol Pharm , vol.13 , pp. 1445-1454
    • Mamoshina, P.1    Vieira, A.2    Putin, E.3    Zhavoronkov, A.4
  • 15
    • 85044948558 scopus 로고    scopus 로고
    • Using deep learning to model the hierarchical structure and function of a cell
    • This paper encodes the hierarchical structure of gene ontology tree in a DNN architecture to sucessfully predict effect of gene mutations on cell proliferation.
    • Ma, J., Yu, M.K., Fong, S., Ono, K., Sage, E., Demchak, B., Sharan, R., Ideker, T., Using deep learning to model the hierarchical structure and function of a cell. Nat Methods 15 (2018), 290–298 This paper encodes the hierarchical structure of gene ontology tree in a DNN architecture to sucessfully predict effect of gene mutations on cell proliferation.
    • (2018) Nat Methods , vol.15 , pp. 290-298
    • Ma, J.1    Yu, M.K.2    Fong, S.3    Ono, K.4    Sage, E.5    Demchak, B.6    Sharan, R.7    Ideker, T.8
  • 16
    • 84930630277 scopus 로고    scopus 로고
    • Deep learning
    • This paper provides intuitive technical insights on the DNN models.
    • LeCun, Y., Bengio, Y., Hinton, G., Deep learning. Nature 521 (2015), 436–444 This paper provides intuitive technical insights on the DNN models.
    • (2015) Nature , vol.521 , pp. 436-444
    • LeCun, Y.1    Bengio, Y.2    Hinton, G.3
  • 18
    • 69349090197 scopus 로고    scopus 로고
    • Learning deep architectures for AI
    • Bengio, Y., Learning deep architectures for AI. Trends in Machine Learn 2 (2009), 1–127.
    • (2009) Trends in Machine Learn , vol.2 , pp. 1-127
    • Bengio, Y.1
  • 19
    • 0025627940 scopus 로고
    • Universal approximation of an unknown mapping and its derivatives using multilayer feedforward networks
    • Hornik, K., Stinchcombe, M., White, H., Universal approximation of an unknown mapping and its derivatives using multilayer feedforward networks. Neural Network 3 (1990), 551–560.
    • (1990) Neural Network , vol.3 , pp. 551-560
    • Hornik, K.1    Stinchcombe, M.2    White, H.3
  • 21
    • 84938888109 scopus 로고    scopus 로고
    • Predicting the sequence specificities of DNA- and RNA-binding proteins by deep learning
    • Alipanahi, B., Delong, A., Weirauch, M.T., Frey, B.J., Predicting the sequence specificities of DNA- and RNA-binding proteins by deep learning. Nat Biotechnol 33 (2015), 831–838.
    • (2015) Nat Biotechnol , vol.33 , pp. 831-838
    • Alipanahi, B.1    Delong, A.2    Weirauch, M.T.3    Frey, B.J.4
  • 22
    • 33947252154 scopus 로고    scopus 로고
    • Network-based prediction of protein function
    • Sharan, R., Ulitsky, I., Shamir, R., Network-based prediction of protein function. Mol Syst Biol, 3, 2007, 88.
    • (2007) Mol Syst Biol , vol.3 , pp. 88
    • Sharan, R.1    Ulitsky, I.2    Shamir, R.3
  • 23
    • 47549107689 scopus 로고    scopus 로고
    • GeneMANIA: a real-time multiple association network integration algorithm for predicting gene function
    • Mostafavi, S., Ray, D., Warde-Farley, D., Grouios, C., Morris, Q., GeneMANIA: a real-time multiple association network integration algorithm for predicting gene function. Genome Biol, 9, 2008, S4.
    • (2008) Genome Biol , vol.9 , pp. S4
    • Mostafavi, S.1    Ray, D.2    Warde-Farley, D.3    Grouios, C.4    Morris, Q.5
  • 24
    • 85021804857 scopus 로고    scopus 로고
    • Semi-supervised classification with graph convolutional networks. arXiv
    • This paper introduces a simple, yet effective approach for encoding network structure in the architecture of DNNs.
    • Kipf, T.N., Welling, M., Semi-supervised classification with graph convolutional networks. arXiv. 2016 This paper introduces a simple, yet effective approach for encoding network structure in the architecture of DNNs.
    • (2016)
    • Kipf, T.N.1    Welling, M.2
  • 28
    • 85046897776 scopus 로고    scopus 로고
    • Inductive representation learning on large graphs
    • This paper introduces GraphSage in which a DNN is trained to predict the role of a gene based on its network neighbors
    • Hamilton, W., Ying, Z., Leskovec, J., Inductive representation learning on large graphs. Advances in neural information processing systems, 2017, 1024–1034 This paper introduces GraphSage in which a DNN is trained to predict the role of a gene based on its network neighbors.
    • (2017) Advances in neural information processing systems , pp. 1024-1034
    • Hamilton, W.1    Ying, Z.2    Leskovec, J.3
  • 29
    • 85031687901 scopus 로고    scopus 로고
    • Using neural networks for reducing the dimensions of single-cell RNA-Seq data
    • This paper encodes the netowrk propagation layers in a DNN architecture to accurately predict a cell type and its state based on gene expression data.
    • Lin, C., Jain, S., Kim, H., Bar-Joseph, Z., Using neural networks for reducing the dimensions of single-cell RNA-Seq data. Nucleic Acids Res, 45, 2017, e156 This paper encodes the netowrk propagation layers in a DNN architecture to accurately predict a cell type and its state based on gene expression data.
    • (2017) Nucleic Acids Res , vol.45 , pp. e156
    • Lin, C.1    Jain, S.2    Kim, H.3    Bar-Joseph, Z.4
  • 30
    • 85058375534 scopus 로고    scopus 로고
    • Comprehensive functional genomic resource and integrative model for the human brain
    • This paper encodes a gene regulatory netowrk in the architecture of DNN to incorporate intermediate phenotypes (e.g., gene expression, tissue composition) in the learning procedure, resulting in an increased accuracy in predicting tissue status from genetic information.
    • Wang, D., Liu, S., Warrell, J., Won, H., Shi, X., Navarro, F.C.P., Clarke, D., Gu, M., Emani, P., Yang, Y.T., et al. Comprehensive functional genomic resource and integrative model for the human brain. Science, 2018, 362 This paper encodes a gene regulatory netowrk in the architecture of DNN to incorporate intermediate phenotypes (e.g., gene expression, tissue composition) in the learning procedure, resulting in an increased accuracy in predicting tissue status from genetic information.
    • (2018) Science , pp. 362
    • Wang, D.1    Liu, S.2    Warrell, J.3    Won, H.4    Shi, X.5    Navarro, F.C.P.6    Clarke, D.7    Gu, M.8    Emani, P.9    Yang, Y.T.10
  • 31
    • 0036500834 scopus 로고    scopus 로고
    • Reverse engineering of biological complexity
    • Csete, M.E., Doyle, J.C., Reverse engineering of biological complexity. Science 295 (2002), 1664–1669.
    • (2002) Science , vol.295 , pp. 1664-1669
    • Csete, M.E.1    Doyle, J.C.2
  • 32
    • 75449094838 scopus 로고    scopus 로고
    • Sequences and consequences
    • Brenner, S., Sequences and consequences. Phil Trans Biol Sci 365 (2010), 207–212.
    • (2010) Phil Trans Biol Sci , vol.365 , pp. 207-212
    • Brenner, S.1
  • 33
    • 0346061723 scopus 로고    scopus 로고
    • High-dimensional data analysis: the curses and blessings of dimensionality
    • Donoho, D.L., High-dimensional data analysis: the curses and blessings of dimensionality. AMS math chall lect, 1, 2000, 32.
    • (2000) AMS math chall lect , vol.1 , pp. 32
    • Donoho, D.L.1
  • 36
    • 84861123691 scopus 로고    scopus 로고
    • Ten years of pathway analysis: current approaches and outstanding challenges
    • Khatri, P., Sirota, M., Butte, A.J., Ten years of pathway analysis: current approaches and outstanding challenges. PLoS Comput Biol, 8, 2012, e1002375.
    • (2012) PLoS Comput Biol , vol.8
    • Khatri, P.1    Sirota, M.2    Butte, A.J.3
  • 37
    • 85015184566 scopus 로고    scopus 로고
    • Why and when can deep-but not shallow-networks avoid the curse of dimensionality: a review
    • Poggio, T., Mhaskar, H., Rosasco, L., Miranda, B., Liao, Q., Why and when can deep-but not shallow-networks avoid the curse of dimensionality: a review. Int J Autom Comput 14 (2017), 503–519.
    • (2017) Int J Autom Comput , vol.14 , pp. 503-519
    • Poggio, T.1    Mhaskar, H.2    Rosasco, L.3    Miranda, B.4    Liao, Q.5
  • 38
    • 85030477873 scopus 로고    scopus 로고
    • When and why are deep networks better than shallow ones?
    • Mhaskar, H., Liao, Q., Poggio, T.A., When and why are deep networks better than shallow ones?. AAAI, 2017, 2343–2349.
    • (2017) AAAI , pp. 2343-2349
    • Mhaskar, H.1    Liao, Q.2    Poggio, T.A.3
  • 39
    • 85042523872 scopus 로고    scopus 로고
    • DeepGO: predicting protein functions from sequence and interactions using a deep ontology-aware classifier
    • Kulmanov, M., Khan, M.A., Hoehndorf, R., DeepGO: predicting protein functions from sequence and interactions using a deep ontology-aware classifier. Bioinformatics 34 (2017), 660–668.
    • (2017) Bioinformatics , vol.34 , pp. 660-668
    • Kulmanov, M.1    Khan, M.A.2    Hoehndorf, R.3
  • 41
    • 85046023666 scopus 로고    scopus 로고
    • An overview of multi-task learning in deep neural networks. arXiv
    • Ruder, S., An overview of multi-task learning in deep neural networks. arXiv. 2017.
    • (2017)
    • Ruder, S.1
  • 42
    • 85031924228 scopus 로고    scopus 로고
    • Learning important features through propagating activation differences. arXiv
    • Shrikumar, A., Greenside, P., Kundaje, A., Learning important features through propagating activation differences. arXiv. 2017.
    • (2017)
    • Shrikumar, A.1    Greenside, P.2    Kundaje, A.3


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.