-
1
-
-
84908019121
-
Big data opportunities and challenges: Discussions from data analytics perspectives
-
Z. Zhou, N. Chawla, Y. Jin, and G. Williams, "Big data opportunities and challenges: Discussions from data analytics perspectives, " IEEE Computational Intelligence Magazine, vol. 9, no. 4, pp. 62-74, 2014.
-
(2014)
IEEE Computational Intelligence Magazine
, vol.9
, Issue.4
, pp. 62-74
-
-
Zhou, Z.1
Chawla, N.2
Jin, Y.3
Williams, G.4
-
2
-
-
1542559402
-
Support vector machine applications in computational biology
-
B. Scholkopf, K. Tsuda, and J.-P. Vert, Eds. The MIT Press, ch. 3
-
W. Nobel, "Support vector machine applications in computational biology, " in Kernel Methods in Computational Biology, B. Scholkopf, K. Tsuda, and J.-P. Vert, Eds. The MIT Press, 2004, ch. 3, pp. 71-92.
-
(2004)
Kernel Methods in Computational Biology
, pp. 71-92
-
-
Nobel, W.1
-
4
-
-
84925031191
-
Methods of integrating data to uncover genotype-phenotype interactions
-
M. Ritchie, E. Holzinger, R. Li, S. Pendergrass, and D. Kim, "Methods of integrating data to uncover genotype-phenotype interactions, " Nature Review Genetics, vol. 15, pp. 85-97, 2015.
-
(2015)
Nature Review Genetics
, vol.15
, pp. 85-97
-
-
Ritchie, M.1
Holzinger, E.2
Li, R.3
Pendergrass, S.4
Kim, D.5
-
5
-
-
34249753618
-
Support vector networks
-
c. Cortes and V. Vapnik, "Support vector networks, " Machine Learning, vol. 20, pp. 273-297, 1995.
-
(1995)
Machine Learning
, vol.20
, pp. 273-297
-
-
Cortes, C.1
Vapnik, V.2
-
6
-
-
85194972808
-
Regression shrinkage and selection via the Lasso
-
R. Tibshirani, "Regression shrinkage and selection via the Lasso, " Journal of the Royal Statistical Society. Series B (Methodological), vol. 58, no. 1, pp. 267-288, 1996.
-
(1996)
Journal of the Royal Statistical Society. Series B (Methodological)
, vol.58
, Issue.1
, pp. 267-288
-
-
Tibshirani, R.1
-
7
-
-
79952266465
-
Accurate inference of transcription factor binding from DNA sequence and chromatin accessibility data
-
R. Pique-Regi, J. Degner, A. Pai, D. Gaffney, Y. Gilad, and J. Pritchard, "Accurate inference of transcription factor binding from DNA sequence and chromatin accessibility data, " Genome Research, vol. 21, pp. 447-455, 2011.
-
(2011)
Genome Research
, vol.21
, pp. 447-455
-
-
Pique-Regi, R.1
Degner, J.2
Pai, A.3
Gaffney, D.4
Gilad, Y.5
Pritchard, J.6
-
9
-
-
0001019707
-
Learning Bayesian networks is NPcomplete
-
D. Frisher and H.-J. Lenz, Eds. Springer, ch. 12
-
D. Chickering, "Learning Bayesian networks is NPcomplete, " in Learning from Data: Al and Statistics V, ser. Lecture Notes in Statistics, D. Frisher and H.-J. Lenz, Eds. Springer, 1996, ch. 12, pp. 121-130.
-
(1996)
Learning from Data: Al and Statistics V, Ser. Lecture Notes in Statistics
, pp. 121-130
-
-
Chickering, D.1
-
10
-
-
34548183010
-
"Ideal parent" structure learning for continuous variable Bayesian networks
-
G. Elidan, I. Nachman, and N. Friedman, ""Ideal Parent " structure learning for continuous variable Bayesian networks, " Journal of Machine Learning Research, vol. 8, pp. 1799-1833, 2007.
-
(2007)
Journal of Machine Learning Research
, vol.8
, pp. 1799-1833
-
-
Elidan, G.1
Nachman, I.2
Friedman, N.3
-
11
-
-
33847259436
-
Mix-nets: Factored mixtures of Gaussians in Bayesian networks with mixed continuous and discrete variables
-
San Francisco, CA: Morgan Kaufmann Publishers Inc
-
S. Davies and A. Moore, "Mix-nets: Factored mixtures of Gaussians in Bayesian networks with mixed continuous and discrete variables, " in Proceedings of The Sixteenth Conference on Uncertainty in Artificial Intelligence. San Francisco, CA: Morgan Kaufmann Publishers Inc, 2000, pp. 168-175.
-
(2000)
Proceedings of the Sixteenth Conference on Uncertainty in Artificial Intelligence
, pp. 168-175
-
-
Davies, S.1
Moore, A.2
-
13
-
-
0026992322
-
An analysis of Bayesian classifiers
-
P. Langley, W. Iba, and K. Thompson, "An analysis of Bayesian classifiers, " in Proceedings of AAAI, 1992, pp. 223-228.
-
(1992)
Proceedings of AAAI
, pp. 223-228
-
-
Langley, P.1
Iba, W.2
Thompson, K.3
-
14
-
-
0031276011
-
Bayesian network classifiers
-
N. Friedman, D. Geiger, and M. Goldszmith, "Bayesian network classifiers, " Machine Learning, vol. 29, pp. 103-130, 1997.
-
(1997)
Machine Learning
, vol.29
, pp. 103-130
-
-
Friedman, N.1
Geiger, D.2
Goldszmith, M.3
-
16
-
-
84863165439
-
-
2nd ed. Springer
-
J. Friedman, R. Tibshirani, and T. Hastie, The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd ed. Springer, 2009.
-
(2009)
The Elements of Statistical Learning: Data Mining, Inference, and Prediction
-
-
Friedman, J.1
Tibshirani, R.2
Hastie, T.3
-
19
-
-
0030211964
-
Bagging predictors
-
L. Breiman, "Bagging predictors, " Machine Learning, vol. 24, no. 3, pp. 123-140, 1996.
-
(1996)
Machine Learning
, vol.24
, Issue.3
, pp. 123-140
-
-
Breiman, L.1
-
21
-
-
0346786584
-
Arcing classifiers
-
L. Breiman, "Arcing classifiers, " The Annals of Statistics, vol. 26, no. 3, pp. 801-849, 1998.
-
(1998)
The Annals of Statistics
, vol.26
, Issue.3
, pp. 801-849
-
-
Breiman, L.1
-
22
-
-
33748611921
-
Ensemble based systems in decision making
-
R. Polikar, "Ensemble based systems in decision making, " IEEE Circuits and Systems Magazine, vol. 6, no. 3, pp. 21-45, 2006.
-
(2006)
IEEE Circuits and Systems Magazine
, vol.6
, Issue.3
, pp. 21-45
-
-
Polikar, R.1
-
23
-
-
85032751446
-
Bootstrap inspired techniques in computational intelligence: Ensemble of classifiers, incremental learning, data fusion and missing features
-
-, "Bootstrap inspired techniques in computational intelligence: Ensemble of classifiers, incremental learning, data fusion and missing features, " IEEE Signal Processing Magazine, vol. 24, no. 4, pp. 59-72, 2007.
-
(2007)
IEEE Signal Processing Magazine
, vol.24
, Issue.4
, pp. 59-72
-
-
Polikar, R.1
-
24
-
-
84887090067
-
A survey of multiple classifier systems as hybrid systems
-
M. Wozniak, M. Grana, and E. Corchado, "A survey of multiple classifier systems as hybrid systems, " Information Fusion, vol. 16, pp. 3-17, 2014.
-
(2014)
Information Fusion
, vol.16
, pp. 3-17
-
-
Wozniak, M.1
Grana, M.2
Corchado, E.3
-
25
-
-
20444392475
-
Using random forest to learn imbalanced data
-
University of California, Berkeley, Tech. Rep.
-
C. Chen, A. Liaw, and L. Breiman, "Using Random Forest to Learn Imbalanced Data, " Department of Statistics, University of California, Berkeley, Tech. Rep., 2004.
-
(2004)
Department of Statistics
-
-
Chen, C.1
Liaw, A.2
Breiman, L.3
-
26
-
-
84977474367
-
Problems with the nested granularity of feature domains in bioinformatics: The eXtasy case
-
D. Popovic, A. Sifrim, J. Davis, Y. Moreau, and B. D. Moor, "Problems with the nested granularity of feature domains in bioinformatics: The eXtasy case, " BMC Bioinformatics, vol. 16, no. 4, p. S2, 2015.
-
(2015)
BMC Bioinformatics
, vol.16
, Issue.4
, pp. S2
-
-
Popovic, D.1
Sifrim, A.2
Davis, J.3
Moreau, Y.4
Moor, B.D.5
-
27
-
-
2942534096
-
Integrated modeling of clinical and gene expression information for personalized prediction of disease outcomes
-
J. Pittman, E. Huang, H. Dressman, c.-F. Horng, S. Cheng, M.-H. Tsou, C.-M. Chen, A. Bild, E. Iversen, A. Huang, J. Nevins, and M. West, "Integrated modeling of clinical and gene expression information for personalized prediction of disease outcomes, " PNAS, vol. 101, no. 22, pp. 8431-8436, 2004.
-
(2004)
PNAS
, vol.101
, Issue.22
, pp. 8431-8436
-
-
Pittman, J.1
Huang, E.2
Dressman, H.3
Horng, C.-F.4
Cheng, S.5
Tsou, M.-H.6
Chen, C.-M.7
Bild, A.8
Iversen, E.9
Huang, A.10
Nevins, J.11
West, M.12
-
29
-
-
85162550129
-
Metric learning with multiple kernels
-
J. Shawe-Taylor, R. Zemel, P. Bartlett, F. Pereira, and K. Weinberger, Eds.
-
J. Wang, H. T. Do, A. Woznica, and A. Kalousis, "Metric learning with multiple kernels, " in Advances in Neural Information Processing Systems 24, J. Shawe-Taylor, R. Zemel, P. Bartlett, F. Pereira, and K. Weinberger, Eds., 2011, pp. 1170-1178.
-
(2011)
Advances in Neural Information Processing Systems
, vol.24
, pp. 1170-1178
-
-
Wang, J.1
Do, H.T.2
Woznica, A.3
Kalousis, A.4
-
30
-
-
84879571292
-
Distance metric learning with application to clustering with sideinformation
-
S. Becker, S. Thrun, and K. Obermayer, Eds. MIT Press
-
E. P. Xing, M. I. Jordan, S. J. Russell, and A. Y. Ng, "Distance metric learning with application to clustering with sideinformation, " in Advances in Neural Information Processing Systems 15, S. Becker, S. Thrun, and K. Obermayer, Eds. MIT Press, 2003, pp. 521-528.
-
(2003)
Advances in Neural Information Processing Systems
, vol.15
, pp. 521-528
-
-
Xing, E.P.1
Jordan, M.I.2
Russell, S.J.3
Ng, A.Y.4
-
31
-
-
84905378574
-
A survey on metric learning for feature vectors and structured data
-
vol. abs/1306.6709
-
A. Bellet, A. Habrard, and M. Sebban, "A survey on metric learning for feature vectors and structured data, " CoRR, vol. abs/1306.6709, 2013.
-
(2013)
CoRR
-
-
Bellet, A.1
Habrard, A.2
Sebban, M.3
-
32
-
-
84887890691
-
Metric learning: A survey
-
B. Kulis, "Metric learning: A survey, " Foundations and Trends in Machine Learning, vol. 5, no. 4, pp. 287-364, 2012.
-
(2012)
Foundations and Trends in Machine Learning
, vol.5
, Issue.4
, pp. 287-364
-
-
Kulis, B.1
-
33
-
-
84908474112
-
A decomposition method for large-scale sparse coding in representation learning
-
IEEE, July
-
Y. Li, R. Caron, and A. Ngom, "A decomposition method for large-scale sparse coding in representation learning, " in World Congress on Computational Intelligence (lJCNNIWCCl). IEEE, July 2014, pp. 3732-2738.
-
(2014)
World Congress on Computational Intelligence (LJCNNIWCCl)
, pp. 3732-3738
-
-
Li, Y.1
Caron, R.2
Ngom, A.3
-
34
-
-
33749246584
-
Optimal kernel selection in kernel fisher discriminant analysis
-
S.-J. Kim, A. Magnani, and S. Boyd, "Optimal kernel selection in kernel fisher discriminant analysis, " in International Conference on Machine Learning, 2006, pp. 465-472.
-
(2006)
International Conference on Machine Learning
, pp. 465-472
-
-
Kim, S.-J.1
Magnani, A.2
Boyd, S.3
-
36
-
-
2542430932
-
Singular value decomposition and principal component analysis
-
D. Berrar, W. Dubitzky, and M. Granzow, Eds. Norwell, MA: Kluwer
-
M. Wall, A. Rechtsteiner, and L. Rocha, " Singular value decomposition and principal component analysis, " in A Practical Approach to Microarray Data Analysis, D. Berrar, W. Dubitzky, and M. Granzow, Eds. Norwell, MA: Kluwer, 2003, pp. 91-109.
-
(2003)
A Practical Approach to Microarray Data Analysis
, pp. 91-109
-
-
Wall, M.1
Rechtsteiner, A.2
Rocha, L.3
-
37
-
-
0003130097
-
The estimation of factor loadings by the method of maximum likelihood
-
D. Lawley, 'The estimation of factor loadings by the method of maximum likelihood, " Proceedings of the Royal Society of Edinburgh, vol. 60, pp. 64-82, 1940.
-
(1940)
Proceedings of the Royal Society of Edinburgh
, vol.60
, pp. 64-82
-
-
Lawley, D.1
-
38
-
-
0242295767
-
Bayesian factor regression models in the "large p, small n " paradigm
-
M. West, "Bayesian factor regression models in the "large p, small n " paradigm, " Bayesian Statistics, vol. 7, pp. 723-732, 2003.
-
(2003)
Bayesian Statistics
, vol.7
, pp. 723-732
-
-
West, M.1
-
39
-
-
0033592606
-
Learning the parts of objects by non-negative matrix factorization
-
D. D. Lee and S. Seung, "Learning the parts of objects by non-negative matrix factorization, " Nature, vol. 401, pp. 788-791, 1999.
-
(1999)
Nature
, vol.401
, pp. 788-791
-
-
Lee, D.D.1
Seung, S.2
-
40
-
-
77958479692
-
CoGAPS: An RJC++ package to identify patterns and biological process activity in transcriptomic data
-
E. Fertig, J. Ding, A. Favorov, G. Parmigiani, and M. Ochs, "CoGAPS: an RJC++ package to identify patterns and biological process activity in transcriptomic data, " Bioinformatics, vol. 26, no. 21, pp. 2792-2793, 2010.
-
(2010)
Bioinformatics
, vol.26
, Issue.21
, pp. 2792-2793
-
-
Fertig, E.1
Ding, J.2
Favorov, A.3
Parmigiani, G.4
Ochs, M.5
-
41
-
-
84876097941
-
The non-negative matrix factorization toolbox for biological data mining
-
Y. Li and A. Ngom, "The non-negative matrix factorization toolbox for biological data mining, " BMC Source Code for Biology and Medicine, vol. 8, no. 1, p. 10, 2013, https://sites.google.com/site/nmftool.
-
(2013)
BMC Source Code for Biology and Medicine
, vol.8
, Issue.1
, pp. 10
-
-
Li, Y.1
Ngom, A.2
-
42
-
-
84959350566
-
The identification of cis-regulatory elements: A review from a machine learning perspective
-
under revision
-
Y. Li, C. Chen, A. Kaye, and W. Wasserman, "The identification of cis-regulatory elements: A review from a machine learning perspective, " BioSystems, 2015, under revision.
-
(2015)
BioSystems
-
-
Li, Y.1
Chen, C.2
Kaye, A.3
Wasserman, W.4
-
43
-
-
68649096448
-
Tensor decompositions and applications
-
T. Kolda and B. Bader, "Tensor decompositions and applications, " SlAM Review, vol. 51, no. 3, pp. 455-500, 2009.
-
(2009)
SlAM Review
, vol.51
, Issue.3
, pp. 455-500
-
-
Kolda, T.1
Bader, B.2
-
44
-
-
79952383578
-
Non-negative matrix and tensor factorization based classification of clinical microarray gene expression data
-
Piscataway, NJ: IEEE Press, Dec
-
Y. Li and A. Ngom, "Non-negative matrix and tensor factorization based classification of clinical microarray gene expression data, " in IEEE lnternational Conference on Bioinformatics & Biomedicine, IEEE. Piscataway, NJ: IEEE Press, Dec. 2010, pp. 438-443.
-
(2010)
IEEE Lnternational Conference on Bioinformatics & Biomedicine, IEEE
, pp. 438-443
-
-
Li, Y.1
Ngom, A.2
-
45
-
-
84906938126
-
Versatile sparse matrix factorization: Theory and applications
-
-, "Versatile sparse matrix factorization: Theory and applications, " Neurocomputing, vol. 145, pp. 23-29, 2014.
-
(2014)
Neurocomputing
, vol.145
, pp. 23-29
-
-
Li, Y.1
Ngom, A.2
-
46
-
-
84877727222
-
Bayesian group factor analysis
-
S. Virtanen, A. Klami, S. Khan, and S. Kaski, "Bayesian group factor analysis, " in Proceedings of the 15th lnternational Conference on Artificial Intelligence and Statistics, 2012, pp. 1269-1277.
-
(2012)
Proceedings of the 15th Lnternational Conference on Artificial Intelligence and Statistics
, pp. 1269-1277
-
-
Virtanen, S.1
Klami, A.2
Khan, S.3
Kaski, S.4
-
47
-
-
84877621868
-
Bayesian cononical correlation analysis
-
A. Klami, S. Virtanen, and S. Kaski, "Bayesian cononical correlation analysis, " Journal of Machine Learning Research, vol. 14, pp. 965-1003, 2013.
-
(2013)
Journal of Machine Learning Research
, vol.14
, pp. 965-1003
-
-
Klami, A.1
Virtanen, S.2
Kaski, S.3
-
48
-
-
84930630277
-
Deep learning
-
Y. LeCun, Y. Bengio, and G. Hinton, "Deep learning, " Nature, vol. 521, pp. 436-444, 2015.
-
(2015)
Nature
, vol.521
, pp. 436-444
-
-
LeCun, Y.1
Bengio, Y.2
Hinton, G.3
-
49
-
-
33745805403
-
A fast learning algorithm for deep belief nets
-
G. Hinton, S. Osindero, and Y. Teh, "A fast learning algorithm for deep belief nets, " Neural Computation, vol. 18, pp. 1527-1554, 2006.
-
(2006)
Neural Computation
, vol.18
, pp. 1527-1554
-
-
Hinton, G.1
Osindero, S.2
Teh, Y.3
-
50
-
-
0002263996
-
Convolutional networks for images, speech, and time series
-
Cambridge, MA: MIT Press
-
Y. LeCun and Y. Bengio, "Convolutional networks for images, speech, and time series, " in The Handbook of Brain Theory and Neural Networks. Cambridge, MA: MIT Press, 1995, pp. 255-258.
-
(1995)
The Handbook of Brain Theory and Neural Networks
, pp. 255-258
-
-
LeCun, Y.1
Bengio, Y.2
-
52
-
-
84959400381
-
Deep feature selection: Theory and application to identify enhancers and promoters
-
accepted (RECOMB 2015 Special Issue)
-
Y. Li, C. Chen, and W. Wasserman, "Deep feature selection: Theory and application to identify enhancers and promoters, " Journal of Computational Biology, 2015, accepted (RECOMB 2015 Special Issue).
-
(2015)
Journal of Computational Biology
-
-
Li, Y.1
Chen, C.2
Wasserman, W.3
|