-
1
-
-
85030641836
-
Drawing the Map of Life: Inside the Human Genome Project
-
McElheny, V. K. (2010). Drawing the Map of Life: Inside the Human Genome Project. Basic Books. ISBN 978-0-465-03260-0.
-
(2010)
Basic Books
-
-
McElheny, V.K.1
-
2
-
-
79958268165
-
Integrating omics data for signaling pathways, interactome reconstruction, and functional analysis. (2011)
-
Tieri P, de la Fuente A, Termanini A, et al. Integrating omics data for signaling pathways, interactome reconstruction, and functional analysis. (2011). Methods Mol Biol. 2011;719:415-33. doi:10.1007/978-1-61779-027-0-19.
-
(2011)
Methods Mol Biol
, vol.719
, pp. 415-433
-
-
Tieri, P.1
De La Fuente, A.2
Termanini, A.3
-
3
-
-
84952988434
-
The "omics" of human male infertility: Integrating big data in a systems biology approach
-
Carrell DT, Aston KI, Oliva R, et al. The "omics" of human male infertility: integrating big data in a systems biology approach. Cell Tissue Res. 2016;363:295.
-
(2016)
Cell Tissue Res
, vol.363
, pp. 295
-
-
Carrell, D.T.1
Aston, K.I.2
Oliva, R.3
-
4
-
-
85016104570
-
The BIG Data Center: From deposition to integration to translation
-
BIG Data Center Members. The BIG Data Center: from deposition to integration to translation. Nucleic Acids Res. 2017;45:D18-24.
-
(2017)
Nucleic Acids Res
, vol.45
, pp. D18-D24
-
-
-
5
-
-
84929519805
-
Knowledge boosting: A graph-based integration approach with multi-omics data and genomic knowledge for cancer clinical outcome prediction
-
Kim D, Joung JG, Sohn KA, et al. Knowledge boosting: a graph-based integration approach with multi-omics data and genomic knowledge for cancer clinical outcome prediction. J Am Med Inform Assoc. 2015;22(1):109-20.
-
(2015)
J Am Med Inform Assoc
, vol.22
, Issue.1
, pp. 109-120
-
-
Kim, D.1
Joung, J.G.2
Sohn, K.A.3
-
6
-
-
78650373804
-
Network medicine: A network-based approach to human disease
-
Barabasi AL, Gulbahce N, Loscalzo J. Network medicine: a network-based approach to human disease. Nat Rev Genet. 2011;12:56-68.
-
(2011)
Nat Rev Genet
, vol.12
, pp. 56-68
-
-
Barabasi, A.L.1
Gulbahce, N.2
Loscalzo, J.3
-
8
-
-
84955606158
-
The new holism: P4 systems medicine and the medicalization of health and life itself
-
Vogt H, Hofmann B, Getz L. The new holism: P4 systems medicine and the medicalization of health and life itself. Med Health Care Philos. 2016;19(2):307-23.
-
(2016)
Med Health Care Philos
, vol.19
, Issue.2
, pp. 307-323
-
-
Vogt, H.1
Hofmann, B.2
Getz, L.3
-
9
-
-
85023779212
-
Network medicine: New paradigm in the omics Era
-
Guo NL. Network medicine: New paradigm in the omics Era. Anat Physiol. 2011;1(1):1000e106.
-
(2011)
Anat Physiol
, vol.1
, Issue.1
, pp. 1000e106
-
-
Guo, N.L.1
-
10
-
-
79958288494
-
Network inference from time-dependent omics data
-
Lecca P, Nguyen TP, Priami C, et al. Network inference from time-dependent omics data. Methods Mol Biol. 2011; 719:435-55.
-
(2011)
Methods Mol Biol
, vol.719
, pp. 435-455
-
-
Lecca, P.1
Nguyen, T.P.2
Priami, C.3
-
12
-
-
29544434311
-
Associating phenotypes with molecular events: Recent statistical advances and challenges underpinning microarray experiments
-
Liang Y, Kelemen A. Associating phenotypes with molecular events: recent statistical advances and challenges underpinning microarray experiments. J Funct Integr Genomics. 2006;6:1-13.
-
(2006)
J Funct Integr Genomics
, vol.6
, pp. 1-13
-
-
Liang, Y.1
Kelemen, A.2
-
13
-
-
85023782689
-
Big Data Science and its Applications in Health and Medical Research: Challenges and Opportunities
-
Liang, Y., Kelemen, A. (2016). Big Data Science and its Applications in Health and Medical Research: Challenges and Opportunities, Austin Journal of Biometrics & Biostatistics, 7(3). doi: 10.4172/2155-6180.1000307
-
(2016)
Austin Journal of Biometrics & Biostatistics
, vol.7
, Issue.3
-
-
Liang, Y.1
Kelemen, A.2
-
14
-
-
38349057585
-
Review of Computational Intelligence for Gene-Gene Interactions in Disease Mapping
-
(A. Kelemen, A. Abraham, Y. Chen, Eds.) in the Series in Studies in Computational Intelligence
-
Kelemen, A., Liang, Y., Vasilakos, A. (2008). Review of Computational Intelligence for Gene-Gene Interactions in Disease Mapping, in "Computational Intelligence in Medical Informatics" (A. Kelemen, A. Abraham, Y. Chen, Eds.) in the Series in Studies in Computational Intelligence, 1-16
-
(2008)
Computational Intelligence in Medical Informatics
, pp. 1-16
-
-
Kelemen, A.1
Liang, Y.2
Vasilakos, A.3
-
15
-
-
79955879494
-
Controllability of complex networks
-
Liu YY, Slotine JJ, Barabasi AL. Controllability of complex networks. Nature. 2011;473(7346):167-73.
-
(2011)
Nature
, vol.473
, Issue.7346
, pp. 167-173
-
-
Liu, Y.Y.1
Slotine, J.J.2
Barabasi, A.L.3
-
16
-
-
77949644952
-
Towards a rigorous assessment of systems biology models: The DREAM3 challenges
-
Prill RJ, Marbach D, Saez-Rodriguez J, Sorger PK, Alexopoulos LG, Xue X, Clarke ND, Altan-Bonnet G, Stolovitzky G. Towards a rigorous assessment of systems biology models: the DREAM3 challenges. PLoS One. 2010;5(2):e9202.
-
(2010)
PLoS One
, vol.5
, Issue.2
, pp. e9202
-
-
Prill, R.J.1
Marbach, D.2
Saez-Rodriguez, J.3
Sorger, P.K.4
Alexopoulos, L.G.5
Xue, X.6
Clarke, N.D.7
Altan-Bonnet, G.8
Stolovitzky, G.9
-
17
-
-
85023746506
-
The DREAM5 Consortium, Manolis Kellis, James J Collins, & Gustavo Stolovitzky
-
Daniel M, Costello JC, Robert K, Nicole V, Prill RJ, Camacho DM, Allison KR. The DREAM5 Consortium, Manolis Kellis, James J Collins, & Gustavo Stolovitzky. Nature Methods. 2012;9(8):796-804.
-
(2012)
Nature Methods
, vol.9
, Issue.8
, pp. 796-804
-
-
Daniel, M.1
Costello, J.C.2
Robert, K.3
Nicole, V.4
Prill, R.J.5
Camacho, D.M.6
Allison, K.R.7
-
18
-
-
79958277423
-
Analysis of time course omics datasets
-
Grigorov MG. Analysis of time course omics datasets. Methods Mol Biol. 2011;719:153-72.
-
(2011)
Methods Mol Biol
, vol.719
, pp. 153-172
-
-
Grigorov, M.G.1
-
20
-
-
84863887694
-
Studying and modelling dynamic biological processes using time-series gene expression data
-
Bar-Joseph Z, Gitter A, Simon I. Studying and modelling dynamic biological processes using time-series gene expression data. Nat Rev Genet. 2012;13(8):552-64.
-
(2012)
Nat Rev Genet
, vol.13
, Issue.8
, pp. 552-564
-
-
Bar-Joseph, Z.1
Gitter, A.2
Simon, I.3
-
21
-
-
0041346457
-
Continuous representations of time-series gene expression data
-
Bar-Joseph Z, Gerber GK, Gifford DK, et al. Continuous representations of time-series gene expression data. J Comput Biol. 2004;10(3-4):341-56.
-
(2004)
J Comput Biol
, vol.10
, Issue.3-4
, pp. 341-356
-
-
Bar-Joseph, Z.1
Gerber, G.K.2
Gifford, D.K.3
-
23
-
-
43849093137
-
Search for steady states of piecewise-linear differential equation models of genetic regulatory networks
-
de Jong H, Page M. Search for steady states of piecewise-linear differential equation models of genetic regulatory networks. IEEE/ACM Trans Comput Biol Bioinform. 2008;5(2):208-22.
-
(2008)
IEEE/ACM Trans Comput Biol Bioinform
, vol.5
, Issue.2
, pp. 208-222
-
-
De Jong, H.1
Page, M.2
-
24
-
-
55549143637
-
The transition from differential equations to Boolean networks: A case study in simplifying a regulatory network model
-
Davidich M, Bornholdt S. The transition from differential equations to Boolean networks: a case study in simplifying a regulatory network model. J Theor Biol. 2008;255(3):269-77.
-
(2008)
J Theor Biol
, vol.255
, Issue.3
, pp. 269-277
-
-
Davidich, M.1
Bornholdt, S.2
-
25
-
-
84923687677
-
Quantitative and logic modelling of molecular and gene networks
-
Le Novere N. Quantitative and logic modelling of molecular and gene networks. Nat Rev Genet. 2015;16:146-58.
-
(2015)
Nat Rev Genet
, vol.16
, pp. 146-158
-
-
Le Novere, N.1
-
27
-
-
77952851181
-
BoolNet-an R package for generation, reconstruction and analysis of Boolean networks
-
Mussel C, Hopfensitz M, Kestler HA. BoolNet-an R package for generation, reconstruction and analysis of Boolean networks. Bioinformatics. 2010;26(10):1378-80.
-
(2010)
Bioinformatics
, vol.26
, Issue.10
, pp. 1378-1380
-
-
Mussel, C.1
Hopfensitz, M.2
Kestler, H.A.3
-
28
-
-
49549083532
-
Temporal logic patterns for querying dynamic models of cellular interaction networks
-
Monteiro PT, Ropers D, Mateescu R, et al. Temporal logic patterns for querying dynamic models of cellular interaction networks. Bioinformatics. 2008;24(16):i227-33.
-
(2008)
Bioinformatics
, vol.24
, Issue.16
, pp. i227-i233
-
-
Monteiro, P.T.1
Ropers, D.2
Mateescu, R.3
-
29
-
-
79959362223
-
Asymptotic Conditional Singular Value Decomposition for High-Dimensional
-
Leek J, (2011) Asymptotic Conditional Singular Value Decomposition for High-Dimensional Genomic Data Biometrics. 67 (2), pp. 344-52.
-
(2011)
Genomic Data Biometrics
, vol.67
, Issue.2
, pp. 344-352
-
-
Leek, J.1
-
30
-
-
62549125109
-
High-dimensional sparse factor modelling: Applications in gene expression genomics
-
Carvalho CM, Chang J, Lucas JE, et al. High-dimensional sparse factor modelling: applications in gene expression genomics. J Am Stat Assoc. 2008;103(484):1438-56.
-
(2008)
J Am Stat Assoc
, vol.103
, Issue.484
, pp. 1438-1456
-
-
Carvalho, C.M.1
Chang, J.2
Lucas, J.E.3
-
31
-
-
34548527584
-
Dynamic matrix-variate graphical models
-
Carvalho CM, West M. Dynamic matrix-variate graphical models. Bayesian Anal. 2007;2(1):69-97.
-
(2007)
Bayesian Anal
, vol.2
, Issue.1
, pp. 69-97
-
-
Carvalho, C.M.1
West, M.2
-
33
-
-
84928251304
-
Bayesian inference of multiple Gaussian graphical models
-
Peterson C, Stingo F, Vannucci M. Bayesian inference of multiple Gaussian graphical models. J Am Stat Assoc. 2014;110(509):159-74.
-
(2014)
J Am Stat Assoc
, vol.110
, Issue.509
, pp. 159-174
-
-
Peterson, C.1
Stingo, F.2
Vannucci, M.3
-
34
-
-
84946916866
-
An equivalent measure of partial correlation coefficients for high dimensional Gaussian graphical models
-
Liang F, Song Q, Qiu P. An equivalent measure of partial correlation coefficients for high dimensional Gaussian graphical models. J Am Stat Assoc. 2015;110:1248.
-
(2015)
J Am Stat Assoc
, vol.110
, pp. 1248
-
-
Liang, F.1
Song, Q.2
Qiu, P.3
-
35
-
-
71549152223
-
Matrix factorization for recovery of biological processes from microarray data
-
Kossenkov AV, Ochs MF. Matrix factorization for recovery of biological processes from microarray data. Methods Enzymol. 2009;467:59-77.
-
(2009)
Methods Enzymol
, vol.467
, pp. 59-77
-
-
Kossenkov, A.V.1
Ochs, M.F.2
-
36
-
-
17644372713
-
Dizzy: Stochastic simulation of large-scale genetic regulatory networks
-
Ramsey S, Orrell D, Bolouri H. Dizzy: stochastic simulation of large-scale genetic regulatory networks. J Bioinform Comput Biol. 2005;3(2):415-36.
-
(2005)
J Bioinform Comput Biol
, vol.3
, Issue.2
, pp. 415-436
-
-
Ramsey, S.1
Orrell, D.2
Bolouri, H.3
-
37
-
-
84941993775
-
Stochastic S-system modeling of gene regulatory network
-
Chowdhury AR, Chetty M, Evans R. Stochastic S-system modeling of gene regulatory network. Cogn Neurodyn. 2015;9(5):535-47.
-
(2015)
Cogn Neurodyn
, vol.9
, Issue.5
, pp. 535-547
-
-
Chowdhury, A.R.1
Chetty, M.2
Evans, R.3
-
38
-
-
84962339458
-
Learning stochastic process-based models of dynamical systems from knowledge and data
-
Tanevski J, Todorovski L, Dzeroski S. Learning stochastic process-based models of dynamical systems from knowledge and data. BMC Syst Biol. 2016;22:10-30.
-
(2016)
BMC Syst Biol
, vol.22
, pp. 10-30
-
-
Tanevski, J.1
Todorovski, L.2
Dzeroski, S.3
-
39
-
-
20844452570
-
A stochastic differential equation model for quantifying transcriptional regulatory network in Saccharomyces cerevisiae
-
Chen KC, Wang TY, Tseng HH, et al. A stochastic differential equation model for quantifying transcriptional regulatory network in Saccharomyces cerevisiae. Bioinformatics. 2005;21(12):2883-90.
-
(2005)
Bioinformatics
, vol.21
, Issue.12
, pp. 2883-2890
-
-
Chen, K.C.1
Wang, T.Y.2
Tseng, H.H.3
-
40
-
-
77956481623
-
Comparative study of three commonly used continuous deterministic methods for modeling gene regulation networks
-
Swain MT, Mandel JJ, Dubitzky W. Comparative study of three commonly used continuous deterministic methods for modeling gene regulation networks. BMC Bioinform. 2010;11:459.
-
(2010)
BMC Bioinform
, vol.11
, pp. 459
-
-
Swain, M.T.1
Mandel, J.J.2
Dubitzky, W.3
-
41
-
-
3142744689
-
Modeling T-cell activation using gene expression profiling and state-space models
-
Rangel C, Angus J, Ghahramani Z, et al. Modeling T-cell activation using gene expression profiling and state-space models. Bioinformatics. 2004;20(9):1361-72.
-
(2004)
Bioinformatics
, vol.20
, Issue.9
, pp. 1361-1372
-
-
Rangel, C.1
Angus, J.2
Ghahramani, Z.3
-
42
-
-
33846036999
-
Hidden Markov models for microarray time course data in multiple biological conditions
-
Yuan M, Kendziorski C. Hidden Markov models for microarray time course data in multiple biological conditions. J Am Stat Assoc. 2006;101(476):1323-32.
-
(2006)
J Am Stat Assoc
, vol.101
, Issue.476
, pp. 1323-1332
-
-
Yuan, M.1
Kendziorski, C.2
-
43
-
-
4143058645
-
Gene networks inference using dynamic Bayesian networks
-
Perrin BE, Ralaivola L, Mazurie A, et al. Gene networks inference using dynamic Bayesian networks. Bioinformatics. 2003;19 Suppl 2:ii138-48.
-
(2003)
Bioinformatics
, vol.19
, pp. ii138-ii148
-
-
Perrin, B.E.1
Ralaivola, L.2
Mazurie, A.3
-
44
-
-
0034354798
-
Time series analysis for non-Gaussian observations based on state space models from both classical and Bayesian perspectives (with discussion)
-
Durbin J, Koopman SJ. Time series analysis for non-Gaussian observations based on state space models from both classical and Bayesian perspectives (with discussion), J. R Stat Soc, Series B. 2000;62:3-56.
-
(2000)
J. R Stat Soc, Series B
, vol.62
, pp. 3-56
-
-
Durbin, J.1
Koopman, S.J.2
-
45
-
-
0035687554
-
Assessing gene significance from cDNA microarray expression data via mixed models
-
Wolfinger RD, Gibson G, Wolfinger ED, et al. Assessing gene significance from cDNA microarray expression data via mixed models. J Comp Biol. 2001;8(6):625-37.
-
(2001)
J Comp Biol
, vol.8
, Issue.6
, pp. 625-637
-
-
Wolfinger, R.D.1
Gibson, G.2
Wolfinger, E.D.3
-
46
-
-
35748964479
-
Modeling gene expression regulatory networks with the sparse vector autoregressive model
-
Fujita A, Sato JR, Garay-Malpartida HM, Yamaguchi R, Miyano S, Sogayar MC, Ferreira CE. Modeling gene expression regulatory networks with the sparse vector autoregressive model. BMC Syst Biol. 2007;1:39.
-
(2007)
BMC Syst Biol
, vol.1
, pp. 39
-
-
Fujita, A.1
Sato, J.R.2
Garay-Malpartida, H.M.3
Yamaguchi, R.4
Miyano, S.5
Sogayar, M.C.6
Ferreira, C.E.7
-
47
-
-
28644452470
-
Clustering short time series gene expression data
-
Ernst J, Nau GJ, Bar-Joseph Z. Clustering short time series gene expression data. Bioinformatics. 2005;21 suppl 1:i159-68.
-
(2005)
Bioinformatics
, vol.21
, pp. i159-i168
-
-
Ernst, J.1
Nau, G.J.2
Bar-Joseph, Z.3
-
48
-
-
0036855903
-
Statistical analysis of a small set of time-ordered gene expression data using linear splines
-
de Hoon MJL, Imoto S, Miyano S. Statistical analysis of a small set of time-ordered gene expression data using linear splines. Bioinformatics. 2002;18(11):1477-85.
-
(2002)
Bioinformatics
, vol.18
, Issue.11
, pp. 1477-1485
-
-
De Hoon, M.J.L.1
Imoto, S.2
Miyano, S.3
-
49
-
-
79958115655
-
Analyzing time-course microarray data using functional data analysis - A review
-
Coffey N, Hinde J. Analyzing time-course microarray data using functional data analysis - a review. Stat Appl Genet Mol Biol. 2011;10:1544-6115.
-
(2011)
Stat Appl Genet Mol Biol
, vol.10
, pp. 1544-6115
-
-
Coffey, N.1
Hinde, J.2
-
50
-
-
84878252678
-
A Bayesian graphical model for chip-seq data on histone modifications
-
Mitra R, Müller P, Liang S, et al. A Bayesian graphical model for chip-seq data on histone modifications. J Am Stat Assoc. 2013;108:69-90.
-
(2013)
J Am Stat Assoc
, vol.108
, pp. 69-90
-
-
Mitra, R.1
Müller, P.2
Liang, S.3
-
51
-
-
34547788797
-
Bayesian approaches to reverse engineer cellular systems: A simulation study on nonlinear Gaussian networks
-
Ferrazzi F, Sebastiani P, Ramoni MF, et al. Bayesian approaches to reverse engineer cellular systems: a simulation study on nonlinear Gaussian networks. BMC Bioinform. 2007;8 Suppl 5:S2. doi:10.1186/1471-2105-8-S5-S2.
-
(2007)
BMC Bioinform
, vol.8
, pp. S2
-
-
Ferrazzi, F.1
Sebastiani, P.2
Ramoni, M.F.3
-
52
-
-
0038492417
-
A Bayesian framework for combining heterogeneous data sources for gene function prediction (in Saccharomyces cerevisiae)
-
Troyanskaya OG, Dolinski K, Owen AB, et al. A Bayesian framework for combining heterogeneous data sources for gene function prediction (in Saccharomyces cerevisiae). Proc Natl Acad Sci U S A. 2003;100(14):8348-53.
-
(2003)
Proc Natl Acad Sci U S A
, vol.100
, Issue.14
, pp. 8348-8353
-
-
Troyanskaya, O.G.1
Dolinski, K.2
Owen, A.B.3
-
53
-
-
60849117796
-
Bayesian finite Markov mixture model for temporal multi-tissue polygenic patterns
-
Liang Y, Kelemen A. Bayesian finite Markov mixture model for temporal multi-tissue polygenic patterns. Biom J. 2009;51(1):56-69.
-
(2009)
Biom J
, vol.51
, Issue.1
, pp. 56-69
-
-
Liang, Y.1
Kelemen, A.2
-
54
-
-
55449121996
-
Bayesian models and meta analysis for multiple tissue gene expression data following corticosteriod administration
-
Liang Y, Kelemen A. Bayesian models and meta analysis for multiple tissue gene expression data following corticosteriod administration. BMC Bioinform. 2008;9:354.
-
(2008)
BMC Bioinform
, vol.9
, pp. 354
-
-
Liang, Y.1
Kelemen, A.2
-
55
-
-
38049166170
-
Bayesian state space models for inferring and predicting temporal gene expression profiles
-
Liang Y, Kelemen A. Bayesian state space models for inferring and predicting temporal gene expression profiles. Biom J. 2007;49(6):801-14.
-
(2007)
Biom J
, vol.49
, Issue.6
, pp. 801-814
-
-
Liang, Y.1
Kelemen, A.2
-
56
-
-
84979781294
-
Bayesian state space models for dynamic genetic network construction across multiple tissues
-
Liang Y, Kelemen A. Bayesian state space models for dynamic genetic network construction across multiple tissues. J Stat Appl Genet Mol Biol. 2016;15(4):273-90.
-
(2016)
J Stat Appl Genet Mol Biol
, vol.15
, Issue.4
, pp. 273-290
-
-
Liang, Y.1
Kelemen, A.2
-
58
-
-
0037057374
-
Exploring the conditional coregulation of yeast gene expression through fuzzy kmeans clustering
-
Gasch, A. P., Eisen, M. B. (2002). Exploring the conditional coregulation of yeast gene expression through fuzzy kmeans clustering. Genome Biology 3(11).
-
(2002)
Genome Biology
, vol.3
, Issue.11
-
-
Gasch, A.P.1
Eisen, M.B.2
-
59
-
-
41649108679
-
Clustering analysis of SAGE transcription profiles using a Poisson approach
-
ed. K. L. Nielsen, Humana Press Inc
-
Huang, H., Cai, L., Wong, W. H. (2008). Clustering analysis of SAGE transcription profiles using a Poisson approach. in SAGE: Methods and Protocols, ed. K. L. Nielsen, Humana Press Inc.
-
(2008)
SAGE: Methods and Protocols
-
-
Huang, H.1
Cai, L.2
Wong, W.H.3
-
60
-
-
0032441150
-
Cluster analysis and display of genome-wide expression patterns
-
Eisen MB, Spellman PT, Brown PO, Botstein D. Cluster analysis and display of genome-wide expression patterns. Proc Natl Acad Sci. 1998;95(25):14863-8.
-
(1998)
Proc Natl Acad Sci
, vol.95
, Issue.25
, pp. 14863-14868
-
-
Eisen, M.B.1
Spellman, P.T.2
Brown, P.O.3
Botstein, D.4
-
61
-
-
28644449917
-
How does gene expression clustering work?
-
D'haeseleer P. How does gene expression clustering work? Nat Biotechnol. 2005;23:1499-501.
-
(2005)
Nat Biotechnol
, vol.23
, pp. 1499-1501
-
-
D'Haeseleer, P.1
-
62
-
-
0034800371
-
Principal component analysis for clustering gene expression data
-
Yeung KY, Ruzzo WL. Principal component analysis for clustering gene expression data. Bioinformatics. 2001;17(9):763-74.
-
(2001)
Bioinformatics
, vol.17
, Issue.9
, pp. 763-774
-
-
Yeung, K.Y.1
Ruzzo, W.L.2
-
63
-
-
0033027794
-
Interpreting patterns of gene expression with self-organizing maps: Methods and application to hematopoietic differentiation
-
Tamayo P, Slonim D, Mesirov J, et al. Interpreting patterns of gene expression with self-organizing maps: methods and application to hematopoietic differentiation. Proc Natl Acad Sci U S A. 1999;6:2907-12.
-
(1999)
Proc Natl Acad Sci U S A
, vol.6
, pp. 2907-2912
-
-
Tamayo, P.1
Slonim, D.2
Mesirov, J.3
-
65
-
-
0033736476
-
Genetic network inference: From co expression clustering to reverse engineering
-
D'haeseleer P, Liang S, Somogyi R. Genetic network inference: from co expression clustering to reverse engineering. Bioinformatics. 2000;16:707-26.
-
(2000)
Bioinformatics
, vol.16
, pp. 707-726
-
-
D'Haeseleer, P.1
Liang, S.2
Somogyi, R.3
-
66
-
-
0037709918
-
Supervised clustering of genes
-
research0069.1-0069.15
-
Dettleing, M. and Bühlmann, P. (2002). Supervised clustering of genes. Genome Biology. 3:research0069.1-0069.15.
-
(2002)
Genome Biology
, vol.3
-
-
Dettleing, M.1
Bühlmann, P.2
-
67
-
-
84871203254
-
Classification of patients from time-course gene expression
-
Zhang Y, Tibshirani R, Davis R. Classification of patients from time-course gene expression. Biostatistics. 2013;14(1):87-98.
-
(2013)
Biostatistics
, vol.14
, Issue.1
, pp. 87-98
-
-
Zhang, Y.1
Tibshirani, R.2
Davis, R.3
-
68
-
-
14644441659
-
Multidimensional support vector machines for visualization of gene expression data
-
Komura D, Nakamura H, Tsutsumi S, et al. Multidimensional support vector machines for visualization of gene expression data. Bioinformatics. 2005;21(4):439-44.
-
(2005)
Bioinformatics
, vol.21
, Issue.4
, pp. 439-444
-
-
Komura, D.1
Nakamura, H.2
Tsutsumi, S.3
-
69
-
-
77950379751
-
Time lagged recurrent neural network for temporal gene expression classification
-
Liang Y, Kelemen A. Time lagged recurrent neural network for temporal gene expression classification. Int J Comput Intell Bioinform Syst Biol. 2009;1(1):91-102.
-
(2009)
Int J Comput Intell Bioinform Syst Biol
, vol.1
, Issue.1
, pp. 91-102
-
-
Liang, Y.1
Kelemen, A.2
-
70
-
-
38049053725
-
Temporal gene expression classification with regularised neural network
-
Liang Y, Kelemen A. Temporal gene expression classification with regularised neural network. Int J Bioinform Res Appl. 2005;1(4):399-413.
-
(2005)
Int J Bioinform Res Appl
, vol.1
, Issue.4
, pp. 399-413
-
-
Liang, Y.1
Kelemen, A.2
-
71
-
-
84901018498
-
A semi-supervised pattern-learning approach to extract Pharmacogenomics-specific drug-gene pairs from biomedical literature
-
Xu R, Wang Q. A semi-supervised pattern-learning approach to extract Pharmacogenomics-specific drug-gene pairs from biomedical literature. J Pharmacogenom Pharmacoproteomics. 2013;4:117.
-
(2013)
J Pharmacogenom Pharmacoproteomics
, vol.4
, pp. 117
-
-
Xu, R.1
Wang, Q.2
-
72
-
-
80054895643
-
Semi-supervised learning improves gene expression-based prediction of cancer recurrence
-
Shi M, Zhang B. Semi-supervised learning improves gene expression-based prediction of cancer recurrence. Bioinformatics. 2011;27(21):3017-23.
-
(2011)
Bioinformatics
, vol.27
, Issue.21
, pp. 3017-3023
-
-
Shi, M.1
Zhang, B.2
-
74
-
-
18744398140
-
Hierarchical Bayesian neural network for gene expression temporal patterns
-
Liang Y, Kelemen A. Hierarchical Bayesian neural network for gene expression temporal patterns. J Stat Appl Genet Mol Biol. 2004;3(1):1-23.
-
(2004)
J Stat Appl Genet Mol Biol
, vol.3
, Issue.1
, pp. 1-23
-
-
Liang, Y.1
Kelemen, A.2
-
75
-
-
84926666920
-
Multi-scale compositionality: Identifying the compositional structures of social dynamics using deep learning
-
Peng H-K, Marculescu R. Multi-scale compositionality: identifying the compositional structures of social dynamics using deep learning. PLoS One. 2015;10(4):e0118309.
-
(2015)
PLoS One
, vol.10
, Issue.4
, pp. e0118309
-
-
Peng, H.-K.1
Marculescu, R.2
-
76
-
-
12344259602
-
Advances to Bayesian network inference for generating causal networks from observational biological data
-
Yu J, Smith VA, Wang PP, et al. Advances to Bayesian network inference for generating causal networks from observational biological data. Bioinformatics. 2004;20(18):3594-603.
-
(2004)
Bioinformatics
, vol.20
, Issue.18
, pp. 3594-3603
-
-
Yu, J.1
Smith, V.A.2
Wang, P.P.3
-
77
-
-
52649087274
-
Modelling and analysis of gene regulatory networks
-
Karlebach G, Shamir R. Modelling and analysis of gene regulatory networks. Nat Rev Mol Cell Biol. 2008;9(10):770-80.
-
(2008)
Nat Rev Mol Cell Biol
, vol.9
, Issue.10
, pp. 770-780
-
-
Karlebach, G.1
Shamir, R.2
-
78
-
-
84870305264
-
Wisdom of crowds for robust gene network inference
-
Marbach D, Costello JC, Küffner R, et al. Wisdom of crowds for robust gene network inference. Nat Methods. 2012; 9(8):796-804.
-
(2012)
Nat Methods
, vol.9
, Issue.8
, pp. 796-804
-
-
Marbach, D.1
Costello, J.C.2
Küffner, R.3
-
79
-
-
70349570737
-
Weighted gene co-expression network analysis of the peripheral blood from amyotrophic lateral sclerosis patients
-
Saris CGJ, Horvath S, van Vught PWJ, et al. Weighted gene co-expression network analysis of the peripheral blood from amyotrophic lateral sclerosis patients. BMC Genomics. 2009;10(1):405.
-
(2009)
BMC Genomics
, vol.10
, Issue.1
, pp. 405
-
-
Saris, C.G.J.1
Horvath, S.2
Van Vught, P.W.J.3
-
80
-
-
84255194439
-
Bayesian parameter estimation for nonlinear modeling of biological pathways
-
Ghasemi O, Lindsey ML, Yang T, et al. Bayesian parameter estimation for nonlinear modeling of biological pathways. BMC Syst Biol. 2011;5 Suppl 3:S9.
-
(2011)
BMC Syst Biol
, vol.5
, pp. S9
-
-
Ghasemi, O.1
Lindsey, M.L.2
Yang, T.3
-
81
-
-
84941344454
-
Causal biological network database: A comprehensive platform of causal biological network models focused on the pulmonary and vascular systems
-
Boué, S., Talikka, M., Westra, J. W., et al. (2015). Causal biological network database: a comprehensive platform of causal biological network models focused on the pulmonary and vascular systems. Database. Article ID bav030.
-
(2015)
Database
-
-
Boué, S.1
Talikka, M.2
Westra, J.W.3
-
82
-
-
77949446630
-
Automated network analysis identifies core pathways in glioblastoma
-
Cerami E, Demir E, Schultz N, et al. Automated network analysis identifies core pathways in glioblastoma. PLoS One. 2010;5(2):e8918.
-
(2010)
PLoS One
, vol.5
, Issue.2
, pp. e8918
-
-
Cerami, E.1
Demir, E.2
Schultz, N.3
-
83
-
-
84962784481
-
MONGKIE: An integrated tool for network analysis and visualization for multi-omics data
-
Jang Y, Yu N, Seo J, et al. MONGKIE: an integrated tool for network analysis and visualization for multi-omics data. Biol Direct. 2016;11:10.
-
(2016)
Biol Direct
, vol.11
, pp. 10
-
-
Jang, Y.1
Yu, N.2
Seo, J.3
-
84
-
-
61349180117
-
Gene regulatory network inference: Data integration in dynamic models-a review
-
Hecker M, Lambeck S, Toepfer S, et al. Gene regulatory network inference: data integration in dynamic models-a review. Biosystems. 2009;96(1):86-103.
-
(2009)
Biosystems
, vol.96
, Issue.1
, pp. 86-103
-
-
Hecker, M.1
Lambeck, S.2
Toepfer, S.3
-
85
-
-
33645241830
-
VANTED: A system for advanced data analysis and visualization in the context of biological networks
-
Junker BH, Klukas C, Schreiber F. VANTED: a system for advanced data analysis and visualization in the context of biological networks. BMC Bioinforma. 2006;7:109. doi:10.1186/1471-2105-7-109.
-
(2006)
BMC Bioinforma
, vol.7
, pp. 109
-
-
Junker, B.H.1
Klukas, C.2
Schreiber, F.3
-
86
-
-
84978888838
-
A Crowdsourcing approach to developing and assessing prediction algorithms for AML prognosis
-
Noren DP, Long BL, Norel R, Rrhissorrakrai K, Hess K, et al. A Crowdsourcing approach to developing and assessing prediction algorithms for AML prognosis. PLoS Comput Biol. 2016;12(6):e1004890.
-
(2016)
PLoS Comput Biol
, vol.12
, Issue.6
, pp. e1004890
-
-
Noren, D.P.1
Long, B.L.2
Norel, R.3
Rrhissorrakrai, K.4
Hess, K.5
-
87
-
-
84946889766
-
Anvi'o: An advanced analysis and visualization platform for 'omics data
-
Eren AM, Esen ÖC, Quince C, Vineis JH, Morrison HG, Sogin ML, Delmont TO. Anvi'o: an advanced analysis and visualization platform for 'omics data. PeerJ. 2015;3:e1319.
-
(2015)
PeerJ
, vol.3
, pp. e1319
-
-
Eren, A.M.1
Esen, Ö.C.2
Quince, C.3
Vineis, J.H.4
Morrison, H.G.5
Sogin, M.L.6
Delmont, T.O.7
-
88
-
-
84974587998
-
Wishbone identifies bifurcating developmental trajectories from single-cell data Nat
-
Setty M, Tadmor MD, Reich-Zeliger S, Angel O, Salame TM, Kathail P, Choi K, Bendall S, Friedman N, Pe'er D. Wishbone identifies bifurcating developmental trajectories from single-cell data Nat. Biotech. 2016;34:637-45.
-
(2016)
Biotech
, vol.34
, pp. 637-645
-
-
Setty, M.1
Tadmor, M.D.2
Reich-Zeliger, S.3
Angel, O.4
Salame, T.M.5
Kathail, P.6
Choi, K.7
Bendall, S.8
Friedman, N.9
Pe'Er, D.10
-
89
-
-
66149119826
-
Modularity and interactions in the genetics of gene expression
-
Litvin O, Causton H, Chen BJ, Pe'er D. Modularity and interactions in the genetics of gene expression. Proc Natl Acad Sci. 2009;106:6441-6.
-
(2009)
Proc Natl Acad Sci
, vol.106
, pp. 6441-6446
-
-
Litvin, O.1
Causton, H.2
Chen, B.J.3
Pe'Er, D.4
-
90
-
-
59649110273
-
Generating realistic in silico gene networks for performance assessment of reverse engineering methods
-
Marbach D, Schaffter T, Mattiussi C, et al. Generating realistic in silico gene networks for performance assessment of reverse engineering methods. J Comput Biol. 2009;16(2):229-39.
-
(2009)
J Comput Biol
, vol.16
, Issue.2
, pp. 229-239
-
-
Marbach, D.1
Schaffter, T.2
Mattiussi, C.3
-
91
-
-
77950910419
-
Revealing strengths and weaknesses of methods for gene network inference
-
Marbach D, Prill RJ, Schaffter T, Mattiussi C, Floreano D, Stolovitzky G. Revealing strengths and weaknesses of methods for gene network inference. Proc Natl Acad Sci U S A. 2010;107(14):6286-91.
-
(2010)
Proc Natl Acad Sci U S A
, vol.107
, Issue.14
, pp. 6286-6291
-
-
Marbach, D.1
Prill, R.J.2
Schaffter, T.3
Mattiussi, C.4
Floreano, D.5
Stolovitzky, G.6
-
92
-
-
59649110273
-
In silico" gene networks for performance assessment of reverse engineering methods
-
Marbach D, Schaffter T, Mattiussi C, Floreano D. Generating realistic "in silico" gene networks for performance assessment of reverse engineering methods. J Comput Biol. 2009;16(2):229-39.
-
(2009)
J Comput Biol
, vol.16
, Issue.2
, pp. 229-239
-
-
Marbach, D.1
Schaffter, T.2
Mattiussi, C.3
Realistic Generating, F.D.4
-
93
-
-
0037941585
-
Module networks: Identifying regulatory modules and their condition-specific regulators from gene expression data
-
Segal E, Shapira M, Regev A, et al. Module networks: identifying regulatory modules and their condition-specific regulators from gene expression data. Nat Genet. 2003;34(2):166-76.
-
(2003)
Nat Genet
, vol.34
, Issue.2
, pp. 166-176
-
-
Segal, E.1
Shapira, M.2
Regev, A.3
-
94
-
-
84861517244
-
Transient dynamics of reduced-order models of genetic regulatory networks
-
Pal R, Bhattacharya S. Transient dynamics of reduced-order models of genetic regulatory networks. IEEE/ACM Trans Comput Biol Bioinform. 2012;9(4):1230-44.
-
(2012)
IEEE/ACM Trans Comput Biol Bioinform
, vol.9
, Issue.4
, pp. 1230-1244
-
-
Pal, R.1
Bhattacharya, S.2
-
95
-
-
84881504354
-
Integration of steady-state and temporal gene expression data for the inference of gene regulatory networks
-
Wang YK, Hurley DG, Schnell S, et al. Integration of steady-state and temporal gene expression data for the inference of gene regulatory networks. PLoS One. 2013;8(8):e72103.
-
(2013)
PLoS One
, vol.8
, Issue.8
, pp. e72103
-
-
Wang, Y.K.1
Hurley, D.G.2
Schnell, S.3
-
96
-
-
84255194444
-
Integration of breast cancer gene signature based on graph centrality
-
Wang J, Chen G, Li M, et al. Integration of breast cancer gene signature based on graph centrality. BMC Syst Biol. 2011;5 Suppl 3:S10.
-
(2011)
BMC Syst Biol
, vol.5
, pp. S10
-
-
Wang, J.1
Chen, G.2
Li, M.3
-
97
-
-
33644994102
-
Network growth models and genetic regulatory networks
-
Foster DV, Kauffman SA, Socolar JES. Network growth models and genetic regulatory networks. Phys Rev E. 2006; 73:031912.
-
(2006)
Phys Rev e
, vol.73
, pp. 031912
-
-
Foster, D.V.1
Kauffman, S.A.2
Socolar, J.E.S.3
-
98
-
-
34247622363
-
The importance of bottlenecks in protein networks: Correlation with gene essentiality and expression dynamics
-
Yu H, Kim PM, Sprecher E, et al. The importance of bottlenecks in protein networks: correlation with gene essentiality and expression dynamics. PLoS Comput Biol. 2007;3(4):e59.
-
(2007)
PLoS Comput Biol
, vol.3
, Issue.4
, pp. e59
-
-
Yu, H.1
Kim, P.M.2
Sprecher, E.3
-
99
-
-
84856094560
-
Differential network biology
-
Ideker, T., Krogan, N. J. (2012). Differential network biology. Mol Syst Biol. 8(565). doi: 10.1038/msb.2011.99
-
(2012)
Mol Syst Biol
, vol.8
, pp. 565
-
-
Ideker, T.1
Krogan, N.J.2
-
100
-
-
78049507393
-
Rewiring of transcriptional regulatory networks: Hierarchy, rather than connectivity, better reflects the importance of regulators
-
Bhardwaj N, Kim PM, Gerstein MB. Rewiring of transcriptional regulatory networks: Hierarchy, rather than connectivity, better reflects the importance of regulators. Sci Signal. 2010;3(146):ra79. doi:10.1126/scisignal. 2001014.
-
(2010)
Sci Signal
, vol.3
, Issue.146
, pp. ra79
-
-
Bhardwaj, N.1
Kim, P.M.2
Gerstein, M.B.3
-
101
-
-
77949748403
-
Bayesian Markov random field analysis for protein function prediction based on network data
-
Kourmpetis YAI, van Dijk ADJ, Bink MCAM, et al. Bayesian Markov random field analysis for protein function prediction based on network data. PLoS One. 2010;5(2):e9293.
-
(2010)
PLoS One
, vol.5
, Issue.2
, pp. e9293
-
-
Kourmpetis, Y.A.I.1
Van Dijk, A.D.J.2
Bink, M.C.A.M.3
-
102
-
-
78049507670
-
Multi-level reproducibility of signature hubs in human interactome for breast cancer metastasis
-
Yao C, Li H, Zhou C, et al. Multi-level reproducibility of signature hubs in human interactome for breast cancer metastasis. BMC Syst Biol. 2010;4:151.
-
(2010)
BMC Syst Biol
, vol.4
, pp. 151
-
-
Yao, C.1
Li, H.2
Zhou, C.3
-
103
-
-
77957930628
-
Statistical inference of the time-varying structure of gene-regulation networks
-
Sophie Lèbre, Jennifer Becq, Frédéric Devaux, Michael PH Stumpf, Gaëlle Lelandais (2010) Statistical inference of the time-varying structure of gene-regulation networks BMC Systems Biology, 4 (1)
-
(2010)
BMC Systems Biology
, vol.4
, Issue.1
-
-
Lèbre, S.1
Becq, J.2
Devaux, F.3
Stumpf, M.Ph.4
Lelandais, G.5
-
104
-
-
5044245707
-
Gene co-expression network topology provides a framework for molecular characterization of cellular state
-
Carter SL, Brechbühler CM, Griffin M, et al. Gene co-expression network topology provides a framework for molecular characterization of cellular state. Bioinformatics. 2004;20(14):2242-50.
-
(2004)
Bioinformatics
, vol.20
, Issue.14
, pp. 2242-2250
-
-
Carter, S.L.1
Brechbühler, C.M.2
Griffin, M.3
-
105
-
-
60549111634
-
WGCNA: An R package for weighted correlation network analysis
-
Langfelder P, Horvath S. WGCNA: an R package for weighted correlation network analysis. BMC Bioinform. 2008;9:559.
-
(2008)
BMC Bioinform
, vol.9
, pp. 559
-
-
Langfelder, P.1
Horvath, S.2
-
106
-
-
84880569964
-
Non-homogeneous dynamic Bayesian networks with Bayesian regularization for inferring gene regulatory networks with gradually time-varying structure
-
Dondelinger F, Lèbre S, Husmeier D. Non-homogeneous dynamic Bayesian networks with Bayesian regularization for inferring gene regulatory networks with gradually time-varying structure. Mach Learn. 2013;90:191.
-
(2013)
Mach Learn
, vol.90
, pp. 191
-
-
Dondelinger, F.1
Lèbre, S.2
Husmeier, D.3
-
107
-
-
33746353952
-
Applying dynamic Bayesian networks to perturbed gene expression data
-
Dojer N, Gambin A, Mizera A, et al. Applying dynamic Bayesian networks to perturbed gene expression data. BMC Bioinform. 2006;7:249.
-
(2006)
BMC Bioinform
, vol.7
, pp. 249
-
-
Dojer, N.1
Gambin, A.2
Mizera, A.3
-
108
-
-
12744261506
-
A new dynamic Bayesian network (DBN) approach for identifying gene regulatory networks from time course microarray data
-
Zou M, Conzen SD. A new dynamic Bayesian network (DBN) approach for identifying gene regulatory networks from time course microarray data. Bioinformatics. 2005;21(1):71-9.
-
(2005)
Bioinformatics
, vol.21
, Issue.1
, pp. 71-79
-
-
Zou, M.1
Conzen, S.D.2
-
109
-
-
38549107133
-
Comparison of probabilistic Boolean network and dynamic Bayesian network approaches for inferring gene regulatory networks
-
Li P, Zhang CY, Perkins EJ, et al. Comparison of probabilistic Boolean network and dynamic Bayesian network approaches for inferring gene regulatory networks. BMC Bioinform. 2007;8 Suppl 7:S13. doi:10.1186/1471-2105-8-S7-S13.
-
(2007)
BMC Bioinform
, vol.8
, pp. S13
-
-
Li, P.1
Zhang, C.Y.2
Perkins, E.J.3
-
110
-
-
79958861169
-
Non-homogeneous dynamic Bayesian networks for continuous data
-
Grzegorczyk M, Husmeier D. Non-homogeneous dynamic Bayesian networks for continuous data. Mach Learn. 2011;83:355.
-
(2011)
Mach Learn
, vol.83
, pp. 355
-
-
Grzegorczyk, M.1
Husmeier, D.2
-
113
-
-
85023753418
-
Inferring cellular networks using probabilistic graphical models Carvalho, C. M., West, M. Dynamic matrix-variate graphical models
-
Friedman N, Inferring cellular networks using probabilistic graphical models Carvalho, C. M., West, M. Dynamic matrix-variate graphical models. Bayesian Anal. 2007;2(1):69-97.
-
(2007)
Bayesian Anal
, vol.2
, Issue.1
, pp. 69-97
-
-
Friedman, N.1
-
114
-
-
67650927399
-
Granger causality vs. Dynamic Bayesian network inference: A comparative study
-
Zou C, Feng H. Granger causality vs. Dynamic Bayesian network inference: a comparative study. BMC Bioinform. 2009;10:122.
-
(2009)
BMC Bioinform
, vol.10
, pp. 122
-
-
Zou, C.1
Feng, H.2
-
115
-
-
4644372593
-
Dynamic Bayesian network and nonparametric regression model for inferring gene networks
-
Kimm SY, Imoto S, Miyano S. Dynamic Bayesian network and nonparametric regression model for inferring gene networks. Genome Inform. 2002;13:371-2.
-
(2002)
Genome Inform
, vol.13
, pp. 371-372
-
-
Kimm, S.Y.1
Imoto, S.2
Miyano, S.3
-
116
-
-
79551497706
-
Learning Non-stationary dynamic Bayesian networks
-
Robinson J, Hartemink A. Learning Non-stationary dynamic Bayesian networks. J Mach Learn Res. 2010;11:3647-80.
-
(2010)
J Mach Learn Res
, vol.11
, pp. 3647-3680
-
-
Robinson, J.1
Hartemink, A.2
-
117
-
-
84888045025
-
Autoregressive models for gene regulatory network inference: Sparsity, stability and causality
-
Michailidis G, d'Alché-Buc F. Autoregressive models for gene regulatory network inference: sparsity, stability and causality. Math Biosci. 2013;246(2):326-34.
-
(2013)
Math Biosci
, vol.246
, Issue.2
, pp. 326-334
-
-
Michailidis, G.1
Alché-Buc, F.2
-
118
-
-
84993927170
-
Elastic-Net copula granger causality for inference of biological networks
-
Furqan MS, Siyal MY. Elastic-Net copula granger causality for inference of biological networks. PLoS One. 2016; 11(10):e0165612.
-
(2016)
PLoS One
, vol.11
, Issue.10
, pp. e0165612
-
-
Furqan, M.S.1
Siyal, M.Y.2
-
119
-
-
84964498737
-
Inference of biological networks using Bi-directional random forest granger causality
-
Furqan MS, Siyal MY. Inference of biological networks using Bi-directional random forest granger causality. Springerplus. 2016;5:514.
-
(2016)
Springerplus
, vol.5
, pp. 514
-
-
Furqan, M.S.1
Siyal, M.Y.2
-
120
-
-
84893164708
-
Gene regulatory network discovery using pairwise granger causality
-
Tam GH, Chang C, Hung YS. Gene regulatory network discovery using pairwise granger causality. ET Syst Biol. 2013;7(5):195-204.
-
(2013)
ET Syst Biol
, vol.7
, Issue.5
, pp. 195-204
-
-
Tam, G.H.1
Chang, C.2
Hung, Y.S.3
-
121
-
-
84940042083
-
Prior knowledge driven granger causality analysis on gene regulatory network discovery
-
Yao S, Yoo S, Yu D. Prior knowledge driven granger causality analysis on gene regulatory network discovery. BMC Bioinform. 2015;16:273.
-
(2015)
BMC Bioinform
, vol.16
, pp. 273
-
-
Yao, S.1
Yoo, S.2
Yu, D.3
-
122
-
-
66349115724
-
Grouped graphical granger modeling for gene expression regulatory networks discovery
-
Lozano AC, Abe N, Liu Y, Rosset S. Grouped graphical granger modeling for gene expression regulatory networks discovery. Bioinformatics. 2009;25(12):i110-8.
-
(2009)
Bioinformatics
, vol.25
, Issue.12
, pp. i110-i118
-
-
Lozano, A.C.1
Abe, N.2
Liu, Y.3
Rosset, S.4
-
123
-
-
84923918105
-
Gene network inference using continuous time Bayesian networks: A comparative study and application to Th17 cell differentiation
-
Acerbi E, Zelante T, Narang V, Stella F. Gene network inference using continuous time Bayesian networks: a comparative study and application to Th17 cell differentiation. BMC Bioinform. 2014;15(387):1471-2105.
-
(2014)
BMC Bioinform
, vol.15
, Issue.387
, pp. 1471-2105
-
-
Acerbi, E.1
Zelante, T.2
Narang, V.3
Stella, F.4
-
124
-
-
77349091815
-
NetPath: A public resource of curated signal transduction pathways
-
Kandasamy K, Mohan SS, Raju R, et al. NetPath: a public resource of curated signal transduction pathways. Genome Biol. 2010;11:R3.
-
(2010)
Genome Biol
, vol.11
, pp. R3
-
-
Kandasamy, K.1
Mohan, S.S.2
Raju, R.3
-
125
-
-
84859498694
-
HiPathDB: A human-integrated pathway database with facile visualization
-
Yu N, Seo J, Rho K, et al. hiPathDB: a human-integrated pathway database with facile visualization. Nucleic Acids Res. 2012;40(Database issue):D797-802.
-
(2012)
Nucleic Acids Res
, vol.40
, pp. D797-D802
-
-
Yu, N.1
Seo, J.2
Rho, K.3
-
126
-
-
33644873677
-
Pathguide: A pathway resource list
-
Bader GD, Cary MP, Sander C. Pathguide: a pathway resource list. Nucleic Acids Res. 2006;34:D504-506. doi:10. 1093/nar/gkj126.
-
(2006)
Nucleic Acids Res
, vol.34
, pp. D504-D506
-
-
Bader, G.D.1
Cary, M.P.2
Sander, C.3
-
127
-
-
61449172037
-
Systematic and integrative analysis of large gene lists using DAVID Bioinformatics Resources
-
Huang DW, Sherman BT, Lempicki RA. Systematic and integrative analysis of large gene lists using DAVID Bioinformatics Resources. Nature Protoc. 2009;4(1):44-57.
-
(2009)
Nature Protoc
, vol.4
, Issue.1
, pp. 44-57
-
-
Huang, D.W.1
Sherman, B.T.2
Lempicki, R.A.3
-
128
-
-
58549112996
-
Bioinformatics enrichment tools: Paths toward the comprehensive functional analysis of large gene lists
-
Huang DW, Sherman BT, Lempicki RA. Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists. Nucleic Acids Res. 2009;37(1):1-13.
-
(2009)
Nucleic Acids Res
, vol.37
, Issue.1
, pp. 1-13
-
-
Huang, D.W.1
Sherman, B.T.2
Lempicki, R.A.3
-
129
-
-
84870453566
-
SemMedDB: A PubMed-scale repository of biomedical semantic predications
-
Kilicoglu H, Shin D, Fiszman M, et al. SemMedDB: a PubMed-scale repository of biomedical semantic predications. Bioinformatics. 2012;28(23):3158-60.
-
(2012)
Bioinformatics
, vol.28
, Issue.23
, pp. 3158-3160
-
-
Kilicoglu, H.1
Shin, D.2
Fiszman, M.3
-
130
-
-
67849122322
-
VisANT 3.5: Multi-scale network visualization, analysis and inference based on the gene ontology
-
Hu Z, Hung JH, Wang Y, Chang YC, Huang CL, Huyck M, DeLisi C. VisANT 3.5: multi-scale network visualization, analysis and inference based on the gene ontology. Nucleic Acids Res. 2009;37:W115-121. doi:10.1093/nar/ gkp406.
-
(2009)
Nucleic Acids Res
, vol.37
, pp. W115-W121
-
-
Hu, Z.1
Hung, J.H.2
Wang, Y.3
Chang, Y.C.4
Huang, C.L.5
Huyck, M.6
Delisi, C.7
-
131
-
-
84978621488
-
Inferring causal molecular networks: Empirical assessment through a community-based effort
-
Hill SM, Heiser LM, Cokelaer T, Unger M, Nesser NK, Carlin DE, Zhang Y, Sokolov A, Paull EO, Wong CK, Graim K, Bivol A, Wang H, Zhu F, Afsari B, Danilova LV, Favorov AV, Lee WS, Taylor D, Hu CW, Long BL, Noren DP, Bisberg AJ, The HPN-DREAM Consortium, Mills GB, Gray JW, Kellen M, Norman T, Friend S, Qutub AA, Fertig EJ, Guan Y, Song M, Stuart JM, Spellman PT, Koeppl H, Stolovitzky G+, Saez-Rodriguez J+ & Mukherjee S+. Inferring causal molecular networks: empirical assessment through a community-based effort. Nat Methods. 2016;13:310-8.
-
(2016)
Nat Methods
, vol.13
, pp. 310-318
-
-
Hill, S.M.1
Heiser, L.M.2
Cokelaer, T.3
Unger, M.4
Nesser, N.K.5
Carlin, D.E.6
Zhang, Y.7
Sokolov, A.8
Paull, E.O.9
Wong, C.K.10
Graim, K.11
Bivol, A.12
Wang, H.13
Zhu, F.14
Afsari, B.15
Danilova, L.V.16
Favorov, A.V.17
Lee, W.S.18
Taylor, D.19
Hu, C.W.20
Long, B.L.21
Noren, D.P.22
Bisberg, A.J.23
Mills, G.B.24
Gray, J.W.25
Kellen, M.26
Norman, T.27
Friend, S.28
Qutub, A.A.29
Fertig, E.J.30
Guan, Y.31
Song, M.32
Stuart, J.M.33
Spellman, P.T.34
Koeppl, H.35
Stolovitzky, G.36
Saez-Rodriguez, J.37
Mukherjee, S.38
more..
-
132
-
-
84906549588
-
A community effort to assess and improve drug sensitivity prediction algorithms
-
Costello J, Heiser L, et al. A community effort to assess and improve drug sensitivity prediction algorithms. Nat Biotechnol. 2014;32:1202-12.
-
(2014)
Nat Biotechnol
, vol.32
, pp. 1202-1212
-
-
Costello, J.1
Heiser, L.2
-
133
-
-
85004101018
-
Analysis of metabolomic data: Tools, current strategies and future challenges for omics data integration
-
Cambiaghi, A., Ferrario, M., Masseroli, M. (2016). Analysis of metabolomic data: tools, current strategies and future challenges for omics data integration. Briefings in Bioinformatics pii: bbw031.
-
(2016)
Briefings in Bioinformatics
-
-
Cambiaghi, A.1
Ferrario, M.2
Masseroli, M.3
-
134
-
-
84863547465
-
The human phosphotyrosine signaling network: Evolution and hotspots of hijacking in cancer
-
Lei L, Tibiche C, Fu C, et al. The human phosphotyrosine signaling network: evolution and hotspots of hijacking in cancer. Genome Res. 2012;22(7):1222-30.
-
(2012)
Genome Res
, vol.22
, Issue.7
, pp. 1222-1230
-
-
Lei, L.1
Tibiche, C.2
Fu, C.3
-
135
-
-
84959123422
-
Trajectories of cell-cycle progression from fixed cell populations
-
Gut G, Tadmor MD, Pe'er D, Pelkmans L, Liberali P. Trajectories of cell-cycle progression from fixed cell populations. Nat Methods. 2015;12(10):951-4.
-
(2015)
Nat Methods
, vol.12
, Issue.10
, pp. 951-954
-
-
Gut, G.1
Tadmor, M.D.2
Pe'Er, D.3
Pelkmans, L.4
Liberali, P.5
-
136
-
-
84884682741
-
Genotype-environment interactions reveal causal pathways that mediate genetic effects on phenotype
-
Gagneur J, Stegle O, Zhu C, Jakob P, Tekkedil MM, Aiyar RS, Schuon AK, Pe'er D, Steinmetz LM. Genotype-environment interactions reveal causal pathways that mediate genetic effects on phenotype. PLoS Genet. 2013;9(9):e1003803.
-
(2013)
PLoS Genet
, vol.9
, Issue.9
, pp. e1003803
-
-
Gagneur, J.1
Stegle, O.2
Zhu, C.3
Jakob, P.4
Tekkedil, M.M.5
Aiyar, R.S.6
Schuon, A.K.7
Pe'Er, D.8
Steinmetz, L.M.9
-
137
-
-
84255175663
-
A transcriptional dynamic network during Arabidopsis thaliana pollen development
-
Wang J, Qiu X, Deng Y, et al. A transcriptional dynamic network during Arabidopsis thaliana pollen development. BMC Syst Biol. 2011;5 Suppl 3:S8.
-
(2011)
BMC Syst Biol
, vol.5
, pp. S8
-
-
Wang, J.1
Qiu, X.2
Deng, Y.3
-
138
-
-
84255175679
-
A comprehensive network and pathway analysis of candidate genes in major depressive disorder
-
Jia P, Kao CF, Kuo PH, et al. A comprehensive network and pathway analysis of candidate genes in major depressive disorder. BMC Syst Biol. 2011;5 Suppl 3:S12. doi:10.1186/1752-0509-5-S3-S12.
-
(2011)
BMC Syst Biol
, vol.5
, pp. S12
-
-
Jia, P.1
Kao, C.F.2
Kuo, P.H.3
-
139
-
-
84255194385
-
An integrative analysis of DNA methylation and RNA-Seq data for human heart kidney and liver
-
Xie L, Weichel B, Ohm JE, et al. An integrative analysis of DNA methylation and RNA-Seq data for human heart, kidney and liver. BMC Syst Biol. 2011;5 Suppl 3:S4.
-
(2011)
BMC Syst Biol
, vol.5
, pp. S4
-
-
Xie, L.1
Weichel, B.2
Ohm, J.E.3
-
140
-
-
84255194431
-
Biological network motif detection and evaluation
-
Kim W, Li M, Wang J, et al. Biological network motif detection and evaluation. BMC Syst Biol. 2011;5 Suppl 3:S5.
-
(2011)
BMC Syst Biol
, vol.5
, pp. S5
-
-
Kim, W.1
Li, M.2
Wang, J.3
-
141
-
-
84255175681
-
Leveraging social networks for understanding the evolution of epidemics
-
Martin G, Marinescu MC, Singh DE, et al. Leveraging social networks for understanding the evolution of epidemics. BMC Syst Biol. 2011;5 Suppl 3:S14.
-
(2011)
BMC Syst Biol
, vol.5
, pp. S14
-
-
Martin, G.1
Marinescu, M.C.2
Singh, D.E.3
-
142
-
-
78650572031
-
Paintomics a web based tool for the joint visualization of transcriptomics and metabolomics data
-
Garcia-Alcalde F, Garcia-Lopez F, Dopazo J, Conesa A. Paintomics a web based tool for the joint visualization of transcriptomics and metabolomics data. Bioinformatics. 2011;27:137-9. doi:10.1093/ bioinformatics/btq594.
-
(2011)
Bioinformatics
, vol.27
, pp. 137-139
-
-
Garcia-Alcalde, F.1
Garcia-Lopez, F.2
Dopazo, J.3
Conesa, A.4
-
143
-
-
84933676032
-
Applications for single cell trajectory analysis in inner ear development and regeneration
-
Durruthy, R & Heller, S (2015). Applications for single cell trajectory analysis in inner ear development and regeneration. Cell and Tissue Research, 361(1), 49-7. http://doi.org/10.1007/s00441-014-2079-2.
-
(2015)
Cell and Tissue Research
, vol.361
, Issue.1
, pp. 49-57
-
-
Durruthy, R.1
Heller, S.2
-
144
-
-
84964318219
-
Modeling dynamic systems with efficient ensembles of process-based models
-
Simidjievski N, Todorovski L, Dzeroski S. Modeling dynamic systems with efficient ensembles of process-based models. PLoS One. 2016;11(4):e0153507.
-
(2016)
PLoS One
, vol.11
, Issue.4
, pp. e0153507
-
-
Simidjievski, N.1
Todorovski, L.2
Dzeroski, S.3
-
145
-
-
78650572031
-
Paintomics: A web based tool for the joint visualization of transcriptomics and metabolomics data
-
Garcia-Alcalde F, Garcia-Lopez F, Dopazo J, Conesa A. Paintomics: a web based tool for the joint visualization of transcriptomics and metabolomics data. Bioinformatics. 2011;27:137-9. doi:10.1093/bioinformatics/btq594.
-
(2011)
Bioinformatics
, vol.27
, pp. 137-139
-
-
Garcia-Alcalde, F.1
Garcia-Lopez, F.2
Dopazo, J.3
Conesa, A.4
-
146
-
-
44449127723
-
Correlating the transcriptome, proteome, and Metabolome in the environmental adaptation of a Hyperthermophile
-
Trauger SA, Kalisak E, Kalisiak J, Morita H, Weinberg MV, Menon AL, Ii Poole FL, Adams MWW, Siuzdak G. Correlating the transcriptome, proteome, and Metabolome in the environmental adaptation of a Hyperthermophile. J Proteome Res. 2008;7:1027-35. doi:10.1021/pr700609j.
-
(2008)
J Proteome Res
, vol.7
, pp. 1027-1035
-
-
Trauger, S.A.1
Kalisak, E.2
Kalisiak, J.3
Morita, H.4
Weinberg, M.V.5
Menon, A.L.6
Ii Poole, F.L.7
Adams, M.W.W.8
Siuzdak, G.9
-
147
-
-
85006358697
-
Longitudinal omics modeling and integration in clinical metabonomics research: Challenges in childhood metabolic health research
-
Sperisen P, Cominetti O, Martin F-PJ. Longitudinal omics modeling and integration in clinical metabonomics research: challenges in childhood metabolic health research. Front Mol Biosci. 2015;2:44. http://doi.org/10.3389/ fmolb.2015.00044.
-
(2015)
Front Mol Biosci
, vol.2
, pp. 44
-
-
Sperisen, P.1
Cominetti, O.2
F-Pj, M.3
-
148
-
-
84856562561
-
Metscape 2 bioinformatics tool for the analysis and visualization of metabolomics and gene expression data
-
Karnovsky A, Weymouth T, Hull T, Tarcea VG, Scardoni G, Laudanna C, Sartor MA, Stringer KA, Jagadish HV, Burant C, et al. Metscape 2 bioinformatics tool for the analysis and visualization of metabolomics and gene expression data. Bioinformatics. 2012;28:373-80. doi:10.1093/bioinformatics/btr661.
-
(2012)
Bioinformatics
, vol.28
, pp. 373-380
-
-
Karnovsky, A.1
Weymouth, T.2
Hull, T.3
Tarcea, V.G.4
Scardoni, G.5
Laudanna, C.6
Sartor, M.A.7
Stringer, K.A.8
Jagadish, H.V.9
Burant, C.10
-
149
-
-
60749103244
-
Arena3D: Visualization of biological networks in 3D
-
Pavlopoulos G, O'Donoghue S, Satagopam V, Soldatos T, Pafilis E, Schneider R. Arena3D: visualization of biological networks in 3D. BMC Syst Biol. 2008;2:104. doi:10.1186/1752-0509-2-104.
-
(2008)
BMC Syst Biol
, vol.2
, pp. 104
-
-
Pavlopoulos, G.1
O'Donoghue, S.2
Satagopam, V.3
Soldatos, T.4
Pafilis, E.5
Schneider, R.6
-
150
-
-
0242490780
-
Cytoscape: A software environment for integrated models of biomolecular interaction networks
-
Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, Amin N, Schwikowski B, Ideker T. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 2003;13:2498-504. doi:10.1101/gr.1239303.
-
(2003)
Genome Res
, vol.13
, pp. 2498-2504
-
-
Shannon, P.1
Markiel, A.2
Ozier, O.3
Baliga, N.S.4
Wang, J.T.5
Ramage, D.6
Amin, N.7
Schwikowski, B.8
Ideker, T.9
-
151
-
-
70350638951
-
Interaction techniques for selecting and manipulating subgraphs in network visualizations
-
McGuffin MJ, Jurisica I. Interaction techniques for selecting and manipulating subgraphs in network visualizations. IEEE Trans Vis Comput Graph. 2009;15:937-44.
-
(2009)
IEEE Trans Vis Comput Graph
, vol.15
, pp. 937-944
-
-
McGuffin, M.J.1
Jurisica, I.2
-
152
-
-
54949110152
-
C. Visualizing multiple experimental conditions on a graph with biological context
-
Barsky A, Munzner T, Gardy J, Kincaid R: C. Visualizing multiple experimental conditions on a graph with biological context. IEEE Trans Vis Comput Graph. 2008;14:1253-60.
-
(2008)
IEEE Trans Vis Comput Graph
, vol.14
, pp. 1253-1260
-
-
Barsky, A.1
Munzner, T.2
Gardy, J.3
Kincaid, R.4
-
153
-
-
84989328936
-
A method to identify and analyze biological programs through automated reasoning
-
Yordanov, B., Dunn, S. J., Kugler, H., et al. (2016). A method to identify and analyze biological programs through automated reasoning. NP J Systems Biology and Applications. Article number: 16010.
-
(2016)
NP J Systems Biology and Applications
-
-
Yordanov, B.1
Dunn, S.J.2
Kugler, H.3
-
154
-
-
84934444680
-
Pattern identification in time-course gene expression data with the CoGAPS matrix factorization
-
Fertig EJ, Stein-O'Brien G, Jaffe A, et al. Pattern identification in time-course gene expression data with the CoGAPS matrix factorization. Methods Mol Biol. 2014;1101:87-112.
-
(2014)
Methods Mol Biol
, vol.1101
, pp. 87-112
-
-
Fertig, E.J.1
Stein-O'Brien, G.2
Jaffe, A.3
-
155
-
-
77958479692
-
CoGAPS: An R/C++ package to identify patterns and biological process activity in transcriptomic data
-
Fertig EJ, Ding J, Favorov AV, et al. CoGAPS: an R/C++ package to identify patterns and biological process activity in transcriptomic data. Bioinformatics. 2010;26(21):2792-3.
-
(2010)
Bioinformatics
, vol.26
, Issue.21
, pp. 2792-2793
-
-
Fertig, E.J.1
Ding, J.2
Favorov, A.V.3
-
156
-
-
84962141535
-
CHRONOS: A time-varying method for microRNA-mediated sub-pathway enrichment analysis
-
Vrahatis AG, Dimitrakopoulou K, Balomenos P, Tsakalidis AK, Bezerianos A. CHRONOS: a time-varying method for microRNA-mediated sub-pathway enrichment analysis. Bioinformatics. 2015;32(6):884-92.
-
(2015)
Bioinformatics
, vol.32
, Issue.6
, pp. 884-892
-
-
Vrahatis, A.G.1
Dimitrakopoulou, K.2
Balomenos, P.3
Tsakalidis, A.K.4
Bezerianos, A.5
-
157
-
-
84858983547
-
KEGG for integration and interpretation of large-scale molecular datasets
-
Kanehisa M, Goto S, Sato Y, et al. KEGG for integration and interpretation of large-scale molecular datasets. Nucleic Acids Res. 2012;40:D109-14. doi:10.1093/nar/gkr988.
-
(2012)
Nucleic Acids Res
, vol.40
, pp. D109-D114
-
-
Kanehisa, M.1
Goto, S.2
Sato, Y.3
-
158
-
-
84940758157
-
INSPEcT: A computational tool to infer mRNA synthesis, processing and degradation dynamics from RNA- and 4sU-seq time course experiments
-
de Pretis S, Kress T, Morelli MJ, Melloni GE, Rival L, Amati B, Pelizzola M. INSPEcT: a computational tool to infer mRNA synthesis, processing and degradation dynamics from RNA- and 4sU-seq time course experiments. Bioinformatics. 2015;31(17):2829-35.
-
(2015)
Bioinformatics
, vol.31
, Issue.17
, pp. 2829-2835
-
-
De Pretis, S.1
Kress, T.2
Morelli, M.J.3
Melloni, G.E.4
Rival, L.5
Amati, B.6
Pelizzola, M.7
-
159
-
-
84855869711
-
Genetic network analyzer: A tool for the qualitative modeling and simulation of bacterial regulatory networks
-
van Helden J, Toussaint A, Thieffry D, editors., New York: Humana Press, Springer
-
Batt G, Besson B, Ciron PE, et al. Genetic network analyzer: a tool for the qualitative modeling and simulation of bacterial regulatory networks. In: van Helden J, Toussaint A, Thieffry D, editors. Bacterial molecular networks : methods and protocols, methods in molecular biology. New York: Humana Press, Springer; 2012. p. 439-62.
-
(2012)
Bacterial Molecular Networks: Methods and Protocols, Methods in Molecular Biology
, pp. 439-462
-
-
Batt, G.1
Besson, B.2
Ciron, P.E.3
-
160
-
-
84880999420
-
BNFinder2: Faster Bayesian network learning and Bayesian classification
-
Dojer N, Bednarz P, Podsiadło A, et al. BNFinder2: faster Bayesian network learning and Bayesian classification. Bioinformatics. 2013;29(16):2068-70.
-
(2013)
Bioinformatics
, vol.29
, Issue.16
, pp. 2068-2070
-
-
Dojer, N.1
Bednarz, P.2
Podsiadło, A.3
-
161
-
-
58349093534
-
BNFinder: Exact and efficient method for learning Bayesian networks
-
Wilczynski B, Dojer N. BNFinder: exact and efficient method for learning Bayesian networks. Bioinformatics. 2009; 25(2):286-7.
-
(2009)
Bioinformatics
, vol.25
, Issue.2
, pp. 286-287
-
-
Wilczynski, B.1
Dojer, N.2
-
162
-
-
84990943503
-
Learning continuous time Bayesian networks in Non-stationary domains
-
Villa S, Stella F. Learning continuous time Bayesian networks in Non-stationary domains. J Artif Intel Res. 2016;57: 1-37.
-
(2016)
J Artif Intel Res
, vol.57
, pp. 1-37
-
-
Villa, S.1
Stella, F.2
|