-
1
-
-
78650373804
-
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
-
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
-
3
-
-
84895516704
-
Similarity network fusion for aggregating data types on a genomic scale
-
Wang, B., Mezlini, A.M., Demir, F., Fiume, M., Tu, Z., Brudno, M., Haibe-Kains, B., Goldenberg, A., Similarity network fusion for aggregating data types on a genomic scale. Nat Methods 11 (2014), 333–337.
-
(2014)
Nat Methods
, vol.11
, pp. 333-337
-
-
Wang, B.1
Mezlini, A.M.2
Demir, F.3
Fiume, M.4
Tu, Z.5
Brudno, M.6
Haibe-Kains, B.7
Goldenberg, A.8
-
4
-
-
0036500993
-
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
-
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
-
6
-
-
84941076570
-
Prediction of human population responses to toxic compounds by a collaborative competition
-
Eduati, F., Mangravite, L.M., Wang, T., Tang, H., Bare, J.C., Huang, R., Norman, T., Kellen, M., Menden, M.P., Yang, J., et al. Prediction of human population responses to toxic compounds by a collaborative competition. Nat Biotechnol 33 (2015), 933–940.
-
(2015)
Nat Biotechnol
, vol.33
, pp. 933-940
-
-
Eduati, F.1
Mangravite, L.M.2
Wang, T.3
Tang, H.4
Bare, J.C.5
Huang, R.6
Norman, T.7
Kellen, M.8
Menden, M.P.9
Yang, J.10
-
7
-
-
85064813122
-
Transcriptional organization of autism spectrum disorder and its connection to ASD risk genes and phenotypic variation
-
Gazestani, V.H., Pramparo, T., Nalabolu, S., Kellman, B.P., Murry, S., Lopez, L., Pierce, K., Courchesne, E., Lewis, N.E., Transcriptional organization of autism spectrum disorder and its connection to ASD risk genes and phenotypic variation. bioRxiv, 2018, 435917.
-
(2018)
bioRxiv
, pp. 435917
-
-
Gazestani, V.H.1
Pramparo, T.2
Nalabolu, S.3
Kellman, B.P.4
Murry, S.5
Lopez, L.6
Pierce, K.7
Courchesne, E.8
Lewis, N.E.9
-
8
-
-
84980022857
-
Deep learning for computational biology
-
Angermueller, C., Pärnamaa, T., Parts, L., Stegle, O., Deep learning for computational biology. Mol Syst Biol, 12, 2016, 878.
-
(2016)
Mol Syst Biol
, vol.12
, pp. 878
-
-
Angermueller, C.1
Pärnamaa, T.2
Parts, L.3
Stegle, O.4
-
9
-
-
85053083782
-
Deep learning in biomedicine
-
Wainberg, M., Merico, D., Delong, A., Frey, B.J., Deep learning in biomedicine. Nat Biotechnol, 36, 2018, 829.
-
(2018)
Nat Biotechnol
, vol.36
, pp. 829
-
-
Wainberg, M.1
Merico, D.2
Delong, A.3
Frey, B.J.4
-
10
-
-
85050595396
-
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
-
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
-
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
-
13
-
-
85045190865
-
Opportunities and obstacles for deep learning in biology and medicine
-
Ching, T., Himmelstein, D.S., Beaulieu-Jones, B.K., Kalinin, A.A., Do, B.T., Way, G.P., Ferrero, E., Agapow, P.-M., Zietz, M., Hoffman, M.M., Opportunities and obstacles for deep learning in biology and medicine. J R Soc Interface, 15, 2018, 20170387.
-
(2018)
J R Soc Interface
, vol.15
, pp. 20170387
-
-
Ching, T.1
Himmelstein, D.S.2
Beaulieu-Jones, B.K.3
Kalinin, A.A.4
Do, B.T.5
Way, G.P.6
Ferrero, E.7
Agapow, P.-M.8
Zietz, M.9
Hoffman, M.M.10
-
14
-
-
85048162030
-
Visible machine learning for biomedicine
-
Michael, K.Y., Ma, J., Fisher, J., Kreisberg, J.F., Raphael, B.J., Ideker, T.J.C., Visible machine learning for biomedicine. Cell 173 (2018), 1562–1565.
-
(2018)
Cell
, vol.173
, pp. 1562-1565
-
-
Michael, K.Y.1
Ma, J.2
Fisher, J.3
Kreisberg, J.F.4
Raphael, B.J.5
Ideker, T.J.C.6
-
15
-
-
85044948558
-
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
-
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
-
17
-
-
84944735469
-
-
MIT press Cambridge
-
Goodfellow, I., Bengio, Y., Courville, A., Bengio, Y., Deep learning, vol. 1, 2016, MIT press, Cambridge.
-
(2016)
Deep learning
, vol.1
-
-
Goodfellow, I.1
Bengio, Y.2
Courville, A.3
Bengio, Y.4
-
18
-
-
69349090197
-
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
-
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
-
20
-
-
0000494466
-
Handwritten digit recognition with a back-propagation network
-
LeCun, Y., Boser, B.E., Denker, J.S., Henderson, D., Howard, R.E., Hubbard, W.E., Jackel, L.D., Handwritten digit recognition with a back-propagation network. Advances in neural information processing systems, 1990, 396–404.
-
(1990)
Advances in neural information processing systems
, pp. 396-404
-
-
LeCun, Y.1
Boser, B.E.2
Denker, J.S.3
Henderson, D.4
Howard, R.E.5
Hubbard, W.E.6
Jackel, L.D.7
-
21
-
-
84938888109
-
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
-
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
-
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
-
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
-
25
-
-
85019257780
-
Convolutional neural networks on graphs with fast localized spectral filtering
-
Defferrard, M., Bresson, X., Vandergheynst, P., Convolutional neural networks on graphs with fast localized spectral filtering. Advances in neural information processing systems, 2016, 3844–3852.
-
(2016)
Advances in neural information processing systems
, pp. 3844-3852
-
-
Defferrard, M.1
Bresson, X.2
Vandergheynst, P.3
-
26
-
-
58649113008
-
The graph neural network model
-
Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G., The graph neural network model. IEEE Trans Neural Netw 20 (2009), 61–80.
-
(2009)
IEEE Trans Neural Netw
, vol.20
, pp. 61-80
-
-
Scarselli, F.1
Gori, M.2
Tsoi, A.C.3
Hagenbuchner, M.4
Monfardini, G.5
-
27
-
-
33745967023
-
A new model for learning in graph domains
-
Gori, M., Monfardini, G., Scarselli, F., A new model for learning in graph domains. Neural networks, 2005. IJCNN'05. Proceedings. 2005 IEEE international joint conference on: IEEE, 2005, 729–734.
-
(2005)
Neural networks, 2005. IJCNN'05. Proceedings. 2005 IEEE international joint conference on: IEEE
, pp. 729-734
-
-
Gori, M.1
Monfardini, G.2
Scarselli, F.3
-
28
-
-
85046897776
-
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
-
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
-
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
-
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
-
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
-
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
-
34
-
-
84991380039
-
Dimension reduction techniques for the integrative analysis of multi-omics data
-
Meng, C., Zeleznik, O.A., Thallinger, G.G., Kuster, B., Gholami, A.M., Culhane, A.C., Dimension reduction techniques for the integrative analysis of multi-omics data. Briefings Bioinf 17 (2016), 628–641.
-
(2016)
Briefings Bioinf
, vol.17
, pp. 628-641
-
-
Meng, C.1
Zeleznik, O.A.2
Thallinger, G.G.3
Kuster, B.4
Gholami, A.M.5
Culhane, A.C.6
-
35
-
-
27344435774
-
Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles
-
Subramanian, A., Tamayo, P., Mootha, V.K., Mukherjee, S., Ebert, B.L., Gillette, M.A., Paulovich, A., Pomeroy, S.L., Golub, T.R., Lander, E.S., et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci U S A 102 (2005), 15545–15550.
-
(2005)
Proc Natl Acad Sci U S A
, vol.102
, pp. 15545-15550
-
-
Subramanian, A.1
Tamayo, P.2
Mootha, V.K.3
Mukherjee, S.4
Ebert, B.L.5
Gillette, M.A.6
Paulovich, A.7
Pomeroy, S.L.8
Golub, T.R.9
Lander, E.S.10
-
36
-
-
84861123691
-
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
-
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
-
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
-
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
-
40
-
-
85013880960
-
A survey of transfer learning
-
Weiss, K., Khoshgoftaar, T.M., Wang, D., A survey of transfer learning. J Big Data, 3, 2016, 9.
-
(2016)
J Big Data
, vol.3
, pp. 9
-
-
Weiss, K.1
Khoshgoftaar, T.M.2
Wang, D.3
-
41
-
-
85046023666
-
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
-
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
-
43
-
-
84954117526
-
Heterogeneous network embedding via deep architectures
-
ACM
-
Chang, S., Han, W., Tang, J., Qi, G.-J., Aggarwal, C.C., Huang, T.S., Heterogeneous network embedding via deep architectures. Proceedings of the 21th ACM SIGKDD international conference on knowledge discovery and data mining, 2015, ACM, 119–128.
-
(2015)
Proceedings of the 21th ACM SIGKDD international conference on knowledge discovery and data mining
, pp. 119-128
-
-
Chang, S.1
Han, W.2
Tang, J.3
Qi, G.-J.4
Aggarwal, C.C.5
Huang, T.S.6
|