-
1
-
-
33645323768
-
Hierarchical multi-label prediction of gene function
-
Z. Barutcuoglu, R. Schapire, and O. Troyanskaya. Hierarchical multi-label prediction of gene function. Bioinformatics, 22(7):830-836, 2006.
-
(2006)
Bioinformatics
, vol.22
, Issue.7
, pp. 830-836
-
-
Barutcuoglu, Z.1
Schapire, R.2
Troyanskaya, O.3
-
2
-
-
33750729556
-
Manifold regularization: A geometric framework for learning from labeled and unlabeled examples
-
M. Belkin, P. Niyogi, and V. Sindhwani. Manifold regularization: a geometric framework for learning from labeled and unlabeled examples. Journal of Machine Learning Research, 7:2399-2434, 2006.
-
(2006)
Journal of Machine Learning Research
, vol.7
, pp. 2399-2434
-
-
Belkin, M.1
Niyogi, P.2
Sindhwani, V.3
-
4
-
-
70450163017
-
Efficient multi-label classification with hypergraph regularization
-
IEEE
-
G. Chen, J. Zhang, F. Wang, C. Zhang, and Y. Gao. Efficient multi-label classification with hypergraph regularization. In Computer Vision and Pattern Recognition (CVPR), 2009 IEEE Conference on, pages 1658-1665. IEEE, 2009.
-
(2009)
Computer Vision and Pattern Recognition (CVPR), 2009 IEEE Conference on
, pp. 1658-1665
-
-
Chen, G.1
Zhang, J.2
Wang, F.3
Zhang, C.4
Gao, Y.5
-
5
-
-
0034069495
-
Gene ontology: Tool for the unification of biology
-
G. O. Consortium et al. Gene ontology: tool for the unification of biology. Nature Genetics, 25(1):25-29, 2000.
-
(2000)
Nature Genetics
, vol.25
, Issue.1
, pp. 25-29
-
-
Consortium, G.O.1
-
9
-
-
80053032591
-
Learning protein functions from bi-relational graph of proteins and function annotations
-
J. Jiang. Learning protein functions from bi-relational graph of proteins and function annotations. Algorithms in Bioinformatics, pages 128-138, 2011.
-
(2011)
Algorithms in Bioinformatics
, pp. 128-138
-
-
Jiang, J.1
-
11
-
-
0037403516
-
Measures of diversity in classifier ensembles and their relationship with the ensemble accuracy
-
L. Kuncheva and C. Whitaker. Measures of diversity in classifier ensembles and their relationship with the ensemble accuracy. Machine Learning, 51(2):181-207, 2003.
-
(2003)
Machine Learning
, vol.51
, Issue.2
, pp. 181-207
-
-
Kuncheva, L.1
Whitaker, C.2
-
12
-
-
8844263749
-
A statistical framework for genomic data fusion
-
G. Lanckriet, T. De Bie, N. Cristianini, M. Jordan, and W. Noble. A statistical framework for genomic data fusion. Bioinformatics, 20(16):2626-2635, 2004.
-
(2004)
Bioinformatics
, vol.20
, Issue.16
, pp. 2626-2635
-
-
Lanckriet, G.1
De Bie, T.2
Cristianini, N.3
Jordan, M.4
Noble, W.5
-
13
-
-
1542714925
-
Mismatch string kernels for discriminative protein classification
-
C. Leslie, E. Eskin, A. Cohen, J. Weston, and W. Noble. Mismatch string kernels for discriminative protein classification. Bioinformatics, 20(4):467, 2004.
-
(2004)
Bioinformatics
, vol.20
, Issue.4
, pp. 467
-
-
Leslie, C.1
Eskin, E.2
Cohen, A.3
Weston, J.4
Noble, W.5
-
15
-
-
77954309042
-
Fast integration of heterogeneous data sources for predicting gene function with limited annotation
-
S. Mostafavi and Q. Morris. Fast integration of heterogeneous data sources for predicting gene function with limited annotation. Bioinformatics, 26(14):1759-1765, 2010.
-
(2010)
Bioinformatics
, vol.26
, Issue.14
, pp. 1759-1765
-
-
Mostafavi, S.1
Morris, Q.2
-
16
-
-
47549107689
-
Genemania: A real-time multiple association network integration algorithm for predicting gene function
-
S. Mostafavi, D. Ray, D. Warde-Farley, C. Grouios, and Q. Morris. Genemania: a real-time multiple association network integration algorithm for predicting gene function. Genome Biology, 9(Suppl 1):S4, 2008.
-
(2008)
Genome Biology
, vol.9
, Issue.SUPPL. 1
-
-
Mostafavi, S.1
Ray, D.2
Warde-Farley, D.3
Grouios, C.4
Morris, Q.5
-
18
-
-
36849044810
-
-
Technical Report TR 06-028, Department of Computer Science and Engineering, University of Minnesota, Twin Cities
-
G. Pandey, V. Kumar, and M. Steinbach. Computational approaches for protein function prediction. Technical Report TR 06-028, Department of Computer Science and Engineering, University of Minnesota, Twin Cities, 2006.
-
(2006)
Computational Approaches for Protein Function Prediction
-
-
Pandey, G.1
Kumar, V.2
Steinbach, M.3
-
19
-
-
67650898284
-
Incorporating functional inter-relationships into protein function prediction algorithms
-
G. Pandey, C. Myers, and V. Kumar. Incorporating functional inter-relationships into protein function prediction algorithms. BMC Bioinformatics, 10(1):142, 2009.
-
(2009)
BMC Bioinformatics
, vol.10
, Issue.1
, pp. 142
-
-
Pandey, G.1
Myers, C.2
Kumar, V.3
-
20
-
-
0036100116
-
Learning gene functional classifications from multiple data types
-
P. Pavlidis, J. Weston, J. Cai, and W. Noble. Learning gene functional classifications from multiple data types. Journal of Computational Biology, 9(2):401-411, 2002.
-
(2002)
Journal of Computational Biology
, vol.9
, Issue.2
, pp. 401-411
-
-
Pavlidis, P.1
Weston, J.2
Cai, J.3
Noble, W.4
-
21
-
-
70349309671
-
Ensemble based data fusion for gene function prediction
-
M. Re and G. Valentini. Ensemble based data fusion for gene function prediction. Multiple Classifier Systems, pages 448-457, 2009.
-
(2009)
Multiple Classifier Systems
, pp. 448-457
-
-
Re, M.1
Valentini, G.2
-
22
-
-
9144257282
-
The funcat, a functional annotation scheme for systematic classification of proteins from whole genomes
-
A. Ruepp, A. Zollner, D. Maier, K. Albermann, J. Hani, M. Mokrejs, I. Tetko, U. Güldener, G. Mannhaupt, M. Münsterkötter, et al. The funcat, a functional annotation scheme for systematic classification of proteins from whole genomes. Nucleic Acids Research, 32(18):5539-5545, 2004.
-
(2004)
Nucleic Acids Research
, vol.32
, Issue.18
, pp. 5539-5545
-
-
Ruepp, A.1
Zollner, A.2
Maier, D.3
Albermann, K.4
Hani, J.5
Mokrejs, M.6
Tetko, I.7
Güldener, U.8
Mannhaupt, G.9
Münsterkötter, M.10
-
24
-
-
56349128167
-
Protein functional class prediction with a combined graph
-
H. Shin, K. Tsuda, and B. Schölkopf. Protein functional class prediction with a combined graph. Expert Systems with Applications, 36(2):3284-3292, 2009.
-
(2009)
Expert Systems with Applications
, vol.36
, Issue.2
, pp. 3284-3292
-
-
Shin, H.1
Tsuda, K.2
Schölkopf, B.3
-
25
-
-
41149096059
-
Random walk with restart: Fast solutions and applications
-
H. Tong, C. Faloutsos, and J. Pan. Random walk with restart: fast solutions and applications. Knowledge and Information Systems, 14(3):327-346, 2008.
-
(2008)
Knowledge and Information Systems
, vol.14
, Issue.3
, pp. 327-346
-
-
Tong, H.1
Faloutsos, C.2
Pan, J.3
-
27
-
-
27544435126
-
Fast protein classification with multiple networks
-
K. Tsuda, H. Shin, and B. Schölkopf. Fast protein classification with multiple networks. Bioinformatics, 21(suppl 2):ii59, 2005.
-
(2005)
Bioinformatics
, vol.21
, Issue.SUPPL. 2
-
-
Tsuda, K.1
Shin, H.2
Schölkopf, B.3
-
28
-
-
80052893001
-
Image annotation using bi-relational graph of images and semantic labels
-
IEEE
-
H. Wang, H. Huang, and C. Ding. Image annotation using bi-relational graph of images and semantic labels. In Computer Vision and Pattern Recognition (CVPR), 2011 IEEE Conference on, pages 793-800. IEEE, 2011.
-
(2011)
Computer Vision and Pattern Recognition (CVPR), 2011 IEEE Conference on
, pp. 793-800
-
-
Wang, H.1
Huang, H.2
Ding, C.3
-
29
-
-
25144481906
-
Semi-supervised protein classification using cluster kernels
-
J. Weston, C. Leslie, E. Ie, D. Zhou, A. Elisseeff, and W. Noble. Semi-supervised protein classification using cluster kernels. Bioinformatics, 21(15):3241-3247, 2005.
-
(2005)
Bioinformatics
, vol.21
, Issue.15
, pp. 3241-3247
-
-
Weston, J.1
Leslie, C.2
Ie, E.3
Zhou, D.4
Elisseeff, A.5
Noble, W.6
-
30
-
-
84866013930
-
A framework for incorporating functional inter-relationships into protein function prediction algorithms
-
X. Zhang and D. Dai. A framework for incorporating functional inter-relationships into protein function prediction algorithms. Computational Biology and Bioinformatics, IEEE/ACM Transactions on, (99):1-1, 2011.
-
(2011)
Computational Biology and Bioinformatics, IEEE/ACM Transactions On
, Issue.99
, pp. 1-1
-
-
Zhang, X.1
Dai, D.2
-
31
-
-
84899006908
-
Learning with local and global consistency
-
D. Zhou, O. Bousquet, T. Lal, J. Weston, and B. Schölkopf. Learning with local and global consistency. Advances in Neural Information Processing Systems, 16:321-328, 2004.
-
(2004)
Advances in Neural Information Processing Systems
, vol.16
, pp. 321-328
-
-
Zhou, D.1
Bousquet, O.2
Lal, T.3
Weston, J.4
Schölkopf, B.5
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