-
1
-
-
0034069495
-
Gene ontology: Tool for the unification of biology. The Gene Ontology Consortium
-
Ashburner, M., Ball, C.A., Blake, J.A., et al. 2000. Gene ontology: Tool for the unification of biology. The Gene Ontology Consortium. Nat. Genet. 25, 25-29.
-
(2000)
Nat. Genet.
, vol.25
, pp. 25-29
-
-
Ashburner, M.1
Ball, C.A.2
Blake, J.A.3
-
2
-
-
33645323768
-
Hierarchical multi-label prediction of gene function
-
Barutcuoglu, Z., Schapire, R.E., and Troyanskaya, O.G. 2006. Hierarchical multi-label prediction of gene function. Bioinformatics. 22, 830-836.
-
(2006)
Bioinformatics
, vol.22
, pp. 830-836
-
-
Barutcuoglu, Z.1
Schapire, R.E.2
Troyanskaya, O.G.3
-
3
-
-
34547969350
-
Label propagation and quadratic criterion, 193-216
-
In Chapelle, O., Scholkopf, B., and Zien, A., eds. MIT Press, New York
-
Bengio, Y., Delalleau, O., and Roux, N.L. 2006. Label propagation and quadratic criterion, 193-216. In Chapelle, O., Scholkopf, B., and Zien, A., eds. Semi-Supervised Learning. MIT Press, New York.
-
(2006)
Semi-Supervised Learning
-
-
Bengio, Y.1
Delalleau, O.2
Roux, N.L.3
-
4
-
-
80052409679
-
COSNet: A cost sensitive neural network for semi-supervised learning in graphs, 219-234
-
In Gunopulos D., Hofmann, T., Malerba, D., and Vazirgiannis M., eds European Conference, ECML PKDD 2011, Athens, Greece, September 5-9, 2011. Proceedings, Part I. Springer, Berlin
-
Bertoni, A., Frasca, M., and Valentini, G. 2011. COSNet: A cost sensitive neural network for semi-supervised learning in graphs, 219-234. In Gunopulos, D., Hofmann, T., Malerba, D., and Vazirgiannis, M., eds. Machine Learning and Knowledge Discovery in Databases. European Conference, ECML PKDD 2011, Athens, Greece, September 5-9, 2011. Proceedings, Part I. Springer, Berlin.
-
(2011)
Machine Learning and Knowledge Discovery in Databases
-
-
Bertoni, A.1
Frasca, M.2
Valentini, G.3
-
5
-
-
79952857163
-
Hierarchical cost-sensitive algorithms for genome-wide gene function prediction
-
Cesa-Bianchi, N., and Valentini, G. 2010. Hierarchical cost-sensitive algorithms for genome-wide gene function prediction. J. Mach. Learn. Res. 8, 14-29.
-
(2010)
J. Mach. Learn. Res
, vol.8
, pp. 14-29
-
-
Cesa-Bianchi, N.1
Valentini, G.2
-
6
-
-
84865223440
-
Synergy of multi-label hierarchical ensembles, data fusion, and cost-sensitive methods for gene functional inference
-
Cesa-Bianchi, N., Re, M., and Valentini, G. 2012. Synergy of multi-label hierarchical ensembles, data fusion, and cost-sensitive methods for gene functional inference. Mach. Learn. 88, 209-241.
-
(2012)
Mach. Learn
, vol.88
, pp. 209-241
-
-
Cesa-Bianchi, N.1
Re, M.2
Valentini, G.3
-
7
-
-
13444283846
-
Global protein function annotation through mining genomescale data in yeast saccharo-myces cerevisiae
-
Chen, Y., and Xu, D. 2004. Global protein function annotation through mining genomescale data in yeast saccharo-myces cerevisiae. Nucleic Acids Res. 32, 6414-6424.
-
(2004)
Nucleic Acids Res
, vol.32
, pp. 6414-6424
-
-
Chen, Y.1
Xu, D.2
-
8
-
-
36949038721
-
An efficient strategy for extensive integration of diverse biological data for protein function prediction
-
Chua, H.N., Sung, W.-K., and Wong, L. 2007. An efficient strategy for extensive integration of diverse biological data for protein function prediction. Bioinformatics. 23, 3364-3373.
-
(2007)
Bioinformatics
, vol.23
, pp. 3364-3373
-
-
Chua, H.N.1
Sung, W.-K.2
Wong, L.3
-
9
-
-
84878083062
-
Protein function prediction by massive integration of evolutionary analyses and multiple data sources
-
Cozzetto, D., Buchan, D.W.A., Bryson, K., et al. 2013. Protein function prediction by massive integration of evolutionary analyses and multiple data sources. BMC Bioinform. 14, S1.
-
(2013)
BMC Bioinform
, vol.14
, pp. S1
-
-
Cozzetto, D.1
Buchan, D.W.A.2
Bryson, K.3
-
10
-
-
84929277565
-
Automated gene function prediction through gene multifunctionality in biological networks
-
Frasca, M. 2015. Automated gene function prediction through gene multifunctionality in biological networks. Neurocomputing. 162, 48-56.
-
(2015)
Neurocomputing
, vol.162
, pp. 48-56
-
-
Frasca, M.1
-
11
-
-
84893617064
-
A neural network based algorithm for gene expression prediction from chromatin structure, 1-8
-
IEEE, New York
-
Frasca, M., and Pavesi, G. 2013. A neural network based algorithm for gene expression prediction from chromatin structure, 1-8. In IJCNN. IEEE, New York.
-
(2013)
IJCNN
-
-
Frasca, M.1
Pavesi, G.2
-
12
-
-
84932097860
-
Learning node labels with multi-category hopfield networks
-
Frasca, M., Bassis, S., and Valentini, G. 2015. Learning node labels with multi-category hopfield networks. Neural Comput. Appl. DOI: 10.1007/s00521-015-1965-1
-
(2015)
Neural Comput. Appl.
-
-
Frasca, M.1
Bassis, S.2
Valentini, G.3
-
13
-
-
84875251066
-
A neural network algorithm for semisupervised node label learning from unbalanced data
-
Frasca, M., Bertoni, A., Re, M., et al. 2013a. A neural network algorithm for semisupervised node label learning from unbalanced data. Neural Netw. 43, 84-98.
-
(2013)
Neural Netw
, vol.43
, pp. 84-98
-
-
Frasca, M.1
Bertoni, A.2
Re, M.3
-
14
-
-
84879318540
-
A neural procedure for gene function prediction, 179-188
-
of Smart Innovation, Systems and Technologies. Springer, Berlin
-
Frasca, M., Bertoni, A., and Sion, A. 2013b. A neural procedure for gene function prediction, 179-188. In Neural Nets and Surroundings, Volume 19 of Smart Innovation, Systems and Technologies. Springer, Berlin.
-
(2013)
Neural Nets and Surroundings
, vol.19
-
-
Frasca, M.1
Bertoni, A.2
Sion, A.3
-
15
-
-
84876478272
-
Characterizing the state of the art in the computational assignment of gene function: Lessons from the first critical assessment of functional annotation (CAFA)
-
Gillis, J., and Pavlidis, P. 2013. Characterizing the state of the art in the computational assignment of gene function: Lessons from the first critical assessment of functional annotation (CAFA). BMC Bioinform. 14, S15.
-
(2013)
BMC Bioinform
, vol.14
, pp. S15
-
-
Gillis, J.1
Pavlidis, P.2
-
16
-
-
47549108100
-
Predicting gene function in a hierarchical context with an ensemble of classifiers
-
Guan, Y., Myers, C.L., and Hess, D.C. 2008. Predicting gene function in a hierarchical context with an ensemble of classifiers. Genome Biol. 9, 1.
-
(2008)
Genome Biol
, vol.9
, pp. 1
-
-
Guan, Y.1
Myers, C.L.2
Hess, D.C.3
-
17
-
-
59849089151
-
Pfp: Automated prediction of gene ontology functional annotations with confidence scores using protein sequence data
-
Hawkins, T., Chitale, M., Luban, S., et al. 2009. Pfp: Automated prediction of gene ontology functional annotations with confidence scores using protein sequence data. Proteins. 74, 566-582.
-
(2009)
Proteins
, vol.74
, pp. 566-582
-
-
Hawkins, T.1
Chitale, M.2
Luban, S.3
-
19
-
-
31844446804
-
A support vector method for multivariate performance measures
-
New York, NY. ACM, New York
-
Joachims, T. 2005. A support vector method for multivariate performance measures, 377-384. In Proceedings of the 22nd International Conference on Machine Learning, ICML '05, New York, NY. ACM, New York.
-
(2005)
Proceedings of the 22nd International Conference on Machine Learning, ICML '05
, pp. 377-384
-
-
Joachims, T.1
-
20
-
-
60749104295
-
Sequence-based feature prediction and annotation of proteins
-
Juncker, A.S., Jensen, L.J., Pierleoni, A., et al. 2009. Sequence-based feature prediction and annotation of proteins. Genome Biol. 10, 206.
-
(2009)
Genome Biol
, vol.10
, pp. 206
-
-
Juncker, A.S.1
Jensen, L.J.2
Pierleoni, A.3
-
21
-
-
47549098627
-
Inferring mouse gene functions from genomic-scale data using a combined functional network/classification strategy
-
Kim, W., Krumpelman, C., and Marcotte, E. 2008. Inferring mouse gene functions from genomic-scale data using a combined functional network/classification strategy. Genome Biol. 9, S5.
-
(2008)
Genome Biol
, vol.9
, pp. S5
-
-
Kim, W.1
Krumpelman, C.2
Marcotte, E.3
-
22
-
-
77949748403
-
Bayesian Markov random field analysis for protein function prediction based on network data
-
Kourmpetis, Y.A.I., van Dijk, A.D.J., Bink, M.C.A.M., et al. 2010. Bayesian Markov random field analysis for protein function prediction based on network data. PLoS ONE. 5, e9293.
-
(2010)
PLoS ONE
, vol.5
, pp. e9293
-
-
Kourmpetis, Y.A.I.1
Van Dijk, A.D.J.2
Bink, M.C.A.M.3
-
23
-
-
84879477284
-
MS-kNN: Protein function prediction by integrating multiple data sources
-
Lan, L., Djuric, N., Guo, Y., et al. 2013. MS-kNN: Protein function prediction by integrating multiple data sources. BMC Bioinform. 14, S8.
-
(2013)
BMC Bioinform
, vol.14
, pp. S8
-
-
Lan, L.1
Djuric, N.2
Guo, Y.3
-
24
-
-
8844263749
-
A statistical framework for genomic data fusion
-
Lanckriet, G.R.G., De Bie, T., Cristianini, N., et al. 2004. A statistical framework for genomic data fusion. Bioin-formatics. 20, 2626-2635.
-
(2004)
Bioin-formatics
, vol.20
, pp. 2626-2635
-
-
Lanckriet, G.R.G.1
De Bie, T.2
Cristianini, N.3
-
25
-
-
33645901213
-
Diffusion kernel-based logistic regression models for protein function prediction
-
Lee, H., Tu, Z., Deng, M., et al. 2006. Diffusion kernel-based logistic regression models for protein function prediction. OMICS. 10, 40-55.
-
(2006)
OMICS
, vol.10
, pp. 40-55
-
-
Lee, H.1
Tu, Z.2
Deng, M.3
-
26
-
-
77949543086
-
Cost-sensitive learning and the class imbalanced problem
-
In Sammut C., ed Springer, New York
-
Ling, C.X., and Sheng, V.S. 2007. Cost-sensitive learning and the class imbalanced problem. In Sammut, C., ed. Encyclopedia of Machine Learning. Springer, New York.
-
(2007)
Encyclopedia of Machine Learning
-
-
Ling, C.X.1
Sheng, V.S.2
-
27
-
-
42149090997
-
High-precision high-coverage functional inference from integrated data sources
-
Linghu, B., Snitkin, E.S., Holloway, D.T., et al. 2008. High-precision high-coverage functional inference from integrated data sources. BMC Bioinform. 9, 119.
-
(2008)
BMC Bioinform
, vol.9
, pp. 119
-
-
Linghu, B.1
Snitkin, E.S.2
Holloway, D.T.3
-
28
-
-
77951948203
-
Gene function prediction from synthetic leathality networks via ranking on demand
-
Lippert, G., et al. 2010. Gene function prediction from synthetic leathality networks via ranking on demand. Bioin-formatics. 26, 912-918.
-
(2010)
Bioin-formatics
, vol.26
, pp. 912-918
-
-
Lippert, G.1
-
29
-
-
0033523854
-
A combined algorithm for genome-wide prediction of protein function
-
Marcotte, E.M., Pellegrini, M., Thompson, M.J., et al. 1999. A combined algorithm for genome-wide prediction of protein function. Nature. 402, 83-86.
-
(1999)
Nature
, vol.402
, pp. 83-86
-
-
Marcotte, E.M.1
Pellegrini, M.2
Thompson, M.J.3
-
30
-
-
13244268370
-
Gotcha: A new method for prediction of protein function assessed by the annotation of seven genomes
-
Martin, D., Berriman, M., and Barton, G. 2004. Gotcha: A new method for prediction of protein function assessed by the annotation of seven genomes. BMC Bioinform. 5, 178.
-
(2004)
BMC Bioinform
, vol.5
, pp. 178
-
-
Martin, D.1
Berriman, M.2
Barton, G.3
-
31
-
-
0033664385
-
Protein networks built by association
-
Mayer, M.L., and Hieter, P. 2000. Protein networks built by association. Nat. Biotechnol. 18, 1242-1243.
-
(2000)
Nat. Biotechnol
, vol.18
, pp. 1242-1243
-
-
Mayer, M.L.1
Hieter, P.2
-
32
-
-
84928393479
-
Think globally and solve locally: Secondary memory-based network learning for automated multi-species function prediction
-
Mesiti, M., Re, M., and Valentini, G. 2014. Think globally and solve locally: Secondary memory-based network learning for automated multi-species function prediction. GigaScience. 3, 5.
-
(2014)
GigaScience
, vol.3
, pp. 5
-
-
Mesiti, M.1
Re, M.2
Valentini, G.3
-
33
-
-
80053169447
-
Using the gene ontology hierarchy when predicting gene function
-
Corvallis, Oregon. AUAI Press, Corvallis, OR
-
Mostafavi, S., and Morris, Q. 2009. Using the gene ontology hierarchy when predicting gene function, 419-427. In Proceedings of the Twenty-Fifth Annual Conference on Uncertainty in Artificial Intelligence (UAI-09), Corvallis, Oregon. AUAI Press, Corvallis, OR.
-
(2009)
Proceedings of the Twenty-Fifth Annual Conference on Uncertainty in Artificial Intelligence (UAI-09)
, pp. 419-427
-
-
Mostafavi, S.1
Morris, Q.2
-
34
-
-
77954309042
-
Fast integration of heterogeneous data sources for predicting gene function with limited annotation
-
Mostafavi, S., and Morris, Q. 2010. Fast integration of heterogeneous data sources for predicting gene function with limited annotation. Bioinformatics. 26, 1759-1765.
-
(2010)
Bioinformatics
, vol.26
, pp. 1759-1765
-
-
Mostafavi, S.1
Morris, Q.2
-
35
-
-
47549107689
-
GeneMANIA: A real-time multiple association network integration algorithm for predicting gene function
-
Mostafavi, S., Ray, D., Farley, D.W., et al. 2008. GeneMANIA: A real-time multiple association network integration algorithm for predicting gene function. Genome Biol. 9, S4.
-
(2008)
Genome Biol
, vol.9
, pp. S4
-
-
Mostafavi, S.1
Ray, D.2
Farley, D.W.3
-
36
-
-
40949099766
-
Optimizing f-measure with support vector machines
-
AAAI Press, New York
-
Musicant, D.R., Kumar, V., and Ozgur, A. 2003. Optimizing f-measure with support vector machines, 356-360. In Proceedings of the International Florida AI Research Society Conference. AAAI Press, New York.
-
(2003)
Proceedings of the International Florida AI Research Society Conference
, pp. 356-360
-
-
Musicant, D.R.1
Kumar, V.2
Ozgur, A.3
-
37
-
-
34548749776
-
Context-sensitive data integration and prediction of biological networks
-
Myers, C.L., and Troyanskaya, O.G. 2007. Context-sensitive data integration and prediction of biological networks. Bioinformatics. 23, 2322-2330.
-
(2007)
Bioinformatics
, vol.23
, pp. 2322-2330
-
-
Myers, C.L.1
Troyanskaya, O.G.2
-
38
-
-
47549088657
-
Consistent probabilistic outputs for protein function prediction
-
Obozinski, G., Lanckriet, G., Grant, C., et al. 2008. Consistent probabilistic outputs for protein function prediction. Genome Biol. 9, S6.
-
(2008)
Genome Biol
, vol.9
, pp. S6
-
-
Obozinski, G.1
Lanckriet, G.2
Grant, C.3
-
39
-
-
67650898284
-
Incorporating functional inter-relationships into protein function prediction algorithms
-
Pandey, G., Myers, C., and Kumar, V. 2009. Incorporating functional inter-relationships into protein function prediction algorithms. BMC Bioinform. 10, 1-142.
-
(2009)
BMC Bioinform.
, Issue.10
, pp. 1-142
-
-
Pandey, G.1
Myers, C.2
Kumar, V.3
-
40
-
-
0036100116
-
Learning gene functional classifications from multiple data types
-
Pavlidis, P., Cai, J., Weston, J., et al. 2002. Learning gene functional classifications from multiple data types. J. Comput. Biol. 9, 401-411.
-
(2002)
J. Comput. Biol
, vol.9
, pp. 401-411
-
-
Pavlidis, P.1
Cai, J.2
Weston, J.3
-
41
-
-
47549116997
-
A critical assessment of Mus musculus gene function prediction using integrated genomic evidence
-
Pena-Castillo, L., Tasan, M., Myers, C., et al. 2008. A critical assessment of Mus musculus gene function prediction using integrated genomic evidence. Genome Biol. 9, S1.
-
(2008)
Genome Biol
, vol.9
, pp. S1
-
-
Pena-Castillo, L.1
Tasan, M.2
Myers, C.3
-
42
-
-
42149163727
-
A mixture of feature experts approach for protein-protein interaction prediction
-
Qi, Y., Seetharaman, J.K., and Joseph, Z.B. 2007. A mixture of feature experts approach for protein-protein interaction prediction. BMC Bioinform. 8, S6.
-
(2007)
BMC Bioinform
, vol.8
, pp. S6
-
-
Qi, Y.1
Seetharaman, J.K.2
Joseph, Z.B.3
-
43
-
-
84874663959
-
A large-scale evaluation of computational protein function prediction
-
Radivojac, P., Clark, W.T., Oron, T.R., et al. 2013. A large-scale evaluation of computational protein function prediction. Nat. Methods. 10, 221-227.
-
(2013)
Nat. Methods
, vol.10
, pp. 221-227
-
-
Radivojac, P.1
Clark, W.T.2
Oron, T.R.3
-
44
-
-
84875276463
-
A fast ranking algorithm for predicting gene functions in biomolecular networks
-
Re, M., Mesiti, M., and Valentini, G. 2012. A fast ranking algorithm for predicting gene functions in biomolecular networks. IEEE ACM Trans. Comput. Biol. Bioinform. 9, 1812-1818.
-
(2012)
IEEE ACM Trans. Comput. Biol. Bioinform
, vol.9
, pp. 1812-1818
-
-
Re, M.1
Mesiti, M.2
Valentini, G.3
-
45
-
-
84937418622
-
A hierarchical ensemble method for DAG-structured taxonomies
-
of Lecture Notes in Computer Science. Springer, New York
-
Robinson, P., Frasca, M., Kohler, S., et al. 2015. A hierarchical ensemble method for DAG-structured taxonomies, 15-36. In Multiple Classifier Systems-MCS 2015, Volume 9132 of Lecture Notes in Computer Science. Springer, New York.
-
(2015)
Multiple Classifier Systems-MCS 2015
, vol.9132
, pp. 15-36
-
-
Robinson, P.1
Frasca, M.2
Kohler, S.3
-
46
-
-
85044707001
-
Network-based prediction of protein function
-
Sharan, R., Ulitsky, I., and Shamir, R. 2007. Network-based prediction of protein function. Mol. Sys. Biol. 8, 88.
-
(2007)
Mol. Sys. Biol
, vol.8
, pp. 88
-
-
Sharan, R.1
Ulitsky, I.2
Shamir, R.3
-
47
-
-
77951518446
-
Hierarchical classification of Gene Ontology terms using the GOstruct method
-
Sokolov, A., and Ben-Hur, A. 2010. Hierarchical classification of Gene Ontology terms using the GOstruct method. J. Bioinform. Comput. Biol. 8, 357-376.
-
(2010)
J. Bioinform. Comput. Biol
, vol.8
, pp. 357-376
-
-
Sokolov, A.1
Ben-Hur, A.2
-
48
-
-
84879298648
-
Combining heterogeneous data sources for accurate functional annotation of proteins
-
Sokolov, A., Funk, C., Graim, K., et al. 2013. Combining heterogeneous data sources for accurate functional annotation of proteins. BMC Bioinform. 14, S10.
-
(2013)
BMC Bioinform
, vol.14
, pp. S10
-
-
Sokolov, A.1
Funk, C.2
Graim, K.3
-
49
-
-
47549104748
-
Combining guilt-by-association and guiltby- profiling to predict saccha-romyces cerevisiae gene function
-
Tian, W., Zhang, L., Tasan, M., et al. 2008. Combining guilt-by-association and guiltby- profiling to predict saccha-romyces cerevisiae gene function. Genome Biol. 9, S7.
-
(2008)
Genome Biol
, vol.9
, pp. S7
-
-
Tian, W.1
Zhang, L.2
Tasan, M.3
-
50
-
-
27544435126
-
Fast protein classification with multiple networks
-
Tsuda, K., Shin, H., and Scholkopf, B. 2005. Fast protein classification with multiple networks. Bioinformatics. 21, 59-65.
-
(2005)
Bioinformatics
, vol.21
, pp. 59-65
-
-
Tsuda, K.1
Shin, H.2
Scholkopf, B.3
-
51
-
-
84926348418
-
Hierarchical ensemble methods for protein function prediction
-
Valentini, G. 2014. Hierarchical ensemble methods for protein function prediction. ISRN Bioinform. 2014, 1-34.
-
(2014)
ISRN Bioinform
, vol.2014
, pp. 1-34
-
-
Valentini, G.1
-
52
-
-
84902547630
-
An extensive analysis of disease-gene associations using network integration and fast kernel-based gene prioritization methods
-
Valentini, G., Paccanaro, A., Caniza, H., et al. 2014. An extensive analysis of disease-gene associations using network integration and fast kernel-based gene prioritization methods. Artif. Intell. Med. 61, 63-78.
-
(2014)
Artif. Intell. Med
, vol.61
, pp. 63-78
-
-
Valentini, G.1
Paccanaro, A.2
Caniza, H.3
-
53
-
-
84885209138
-
Three-level prediction of protien function by combining profile-sequence search, profile-profile search, and domain co-occurence networks
-
Wang, Z., Cao, R., and Cheng, J. 2013. Three-level prediction of protien function by combining profile-sequence search, profile-profile search, and domain co-occurence networks. BMC Bioinform. 14, S3.
-
(2013)
BMC Bioinform
, vol.14
, pp. S3
-
-
Wang, Z.1
Cao, R.2
Cheng, J.3
-
54
-
-
84864455716
-
Imp: A multi-species functional genomics portal for integration, visualization and prediction of protein functions and networks
-
Wong, A.K., Park, C.Y., Greene, C.S., et al. 2012. Imp: A multi-species functional genomics portal for integration, visualization and prediction of protein functions and networks. Nucleic Acids Res. 40, W484-W490.
-
(2012)
Nucleic Acids Res
, vol.40
, pp. W484-W490
-
-
Wong, A.K.1
Park, C.Y.2
Greene, C.S.3
-
55
-
-
33947313355
-
A regression-based k nearest neighbor algorithm for gene function prediction from heterogeneous data
-
Yao, Z., and Ruzzo, W.L. 2006. A regression-based k nearest neighbor algorithm for gene function prediction from heterogeneous data. BMC Bioinform. 7, S1.
-
(2006)
BMC Bioinform
, vol.7
, pp. S1
-
-
Yao, Z.1
Ruzzo, W.L.2
-
56
-
-
84903398946
-
Negative example selection for protein function prediction: The NoGO database
-
Youngs, N., Penfold-Brown, D., Bonneau, R., and Shasha, D. 2014. Negative example selection for protein function prediction: The NoGO database. PLoS Comput. Biol. 10, e1003644.
-
(2014)
PLoS Comput. Biol
, vol.10
, pp. e1003644
-
-
Youngs, N.1
Penfold-Brown, D.2
Bonneau, R.3
Shasha, D.4
-
57
-
-
84859205429
-
A framework for incorporating functional interrelationships into protein function prediction algorithms
-
Zhang, X., and Dai, D. 2012. A framework for incorporating functional interrelationships into protein function prediction algorithms. IEEE ACM Trans. Comput. Biol. Bioinform. 9, 740-753.
-
(2012)
IEEE ACM Trans. Comput. Biol. Bioinform
, vol.9
, pp. 740-753
-
-
Zhang, X.1
Dai, D.2
|