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S. A. Khan et al.
12. Ammad-ud-din M et al (2017) Systematic identification of feature combinations for predicting drug response with Bayesian multi-view multi-task linear regression. Bioinformatics
33(14):i359–i368
13. Bishop CM (2006) Pattern recognition and machine learning. Springer, New York, USA
14. Gelman A et al (2013) Bayesian data analysis. Chapman and Hall/CRC
15. Bartholomew DJ et al (2011) Latent variable models and factor analysis: a unified approach.
Wiley, 904
16. Salakhutdinov R, Mnih A (2008) Bayesian probabilistic matrix factorization using markov
chain monte carlo. In: Proceedings of the 25th international conference on Machine learning,
pp 880–887
17. Tipping ME, Bishop CM (1999) Probabilistic principal component analysis. J R Stat Soc: Ser
B (Stat Methodol) 61(3):611–622
18. Witten DM et al (2009) A penalized matrix decomposition, with applications to sparse principal
components and canonical correlation analysis, Biostatistics, pp 1–8
19. Kossenkov AV, Ochs MF (2009) Matrix factorization for recovery of biological processes from
microarray data. Methods Enzymol 467:59–77
20. Blei DM et al (2003) Latent dirichlet allocation. J Mach Learn Res 3:993–1022
21. Ghahramani Z (2015) Probabilistic machine learning and artificial intelligence. Nature
28:452–459
22. Guo Y et al (2017) Mining meaning from online ratings and reviews: tourist satisfaction analysis
using latent dirichlet allocation. Tour Manag 59:467–483
23. Moro S et al (2015) Business intelligence in banking: A literature analysis from 2002–2013
using text mining and latent Dirichlet allocation. Expert Syst Appl 42(3):1314–1324
24. Krestel R et al (2009) Latent dirichlet allocation for tag recommendation. In: Proceedings of
the third ACM conference on recommender systems, pp 61–68
25. Caldas J et al (2009) Probabilistic retrieval and visualization of biologically relevant microarray
experiments. Bioinformatics 25(12):145–153
26. Pinoli P et al (2014) Latent Dirichlet allocation based on Gibbs sampling for gene function
prediction. In: Computational intelligence in bioinformatics and computational biology, pp 1–8
27. Backenroth D et al (2018) FUN-LDA: a latent dirichlet allocation model for predicting tissuespecific functional effects of noncoding variation: methods and applications. Am J Hum Genet
102(5):920–942
28. Virtanen S et al (2012) Bayesian group factor analysis. In: Artificial Intelligence and Statistics,
pp 1269–1277
29. Klami A et al (2015) Group factor analysis. IEEE Trans Neural Netw Learn Syst
26(9):2136–2147
30. Leppäaho E et al (2017) GFA: exploratory analysis of multiple data sources with group factor
analysis. J Mach Learn Res 18(1):1294–1298
31. Neal RM (1996) Bayesian learning for neural networks. Springer-Verlag
32. Kolda T, Bader B (2009) Tensor decompositions and applications. SIAM Review
51(3):455–500
33. Carroll JD, Chang JJ (1970) Analysis of individual differences in multidimensional scaling via
an n-way generalization of Eckart-Young decomposition. Psychometrika 35(3):283–319
34. Harshman RA (1970) Foundations of the parafac procedure: models and conditions for an
explanatory multimodal factor analysis, UCLA Working Papers in Phonetics, 16:1–84
35. Tucker LR (1966) Some mathematical notes on three-mode factor analysis. Psychometrika
31(3):279–311
36. Yener B et al (2008) Multiway modeling and analysis in stem cell systems biology. BMC Syst
Biol 2(1):63
37. Omberg L et al (2007) A tensor higher-order singular value decomposition for integrative analysis of dna microarray data from different studies. Proc Natl Acad Sci 104(47):18371–18376
38. Li W et al (2011) Integrative analysis of many weighted co-expression networks using tensor
computation. PLoS Comput Biol 7(6):e1001106
S. A. Khan et al.
12. Ammad-ud-din M et al (2017) Systematic identification of feature combinations for predicting drug response with Bayesian multi-view multi-task linear regression. Bioinformatics
33(14):i359–i368
13. Bishop CM (2006) Pattern recognition and machine learning. Springer, New York, USA
14. Gelman A et al (2013) Bayesian data analysis. Chapman and Hall/CRC
15. Bartholomew DJ et al (2011) Latent variable models and factor analysis: a unified approach.
Wiley, 904
16. Salakhutdinov R, Mnih A (2008) Bayesian probabilistic matrix factorization using markov
chain monte carlo. In: Proceedings of the 25th international conference on Machine learning,
pp 880–887
17. Tipping ME, Bishop CM (1999) Probabilistic principal component analysis. J R Stat Soc: Ser
B (Stat Methodol) 61(3):611–622
18. Witten DM et al (2009) A penalized matrix decomposition, with applications to sparse principal
components and canonical correlation analysis, Biostatistics, pp 1–8
19. Kossenkov AV, Ochs MF (2009) Matrix factorization for recovery of biological processes from
microarray data. Methods Enzymol 467:59–77
20. Blei DM et al (2003) Latent dirichlet allocation. J Mach Learn Res 3:993–1022
21. Ghahramani Z (2015) Probabilistic machine learning and artificial intelligence. Nature
28:452–459
22. Guo Y et al (2017) Mining meaning from online ratings and reviews: tourist satisfaction analysis
using latent dirichlet allocation. Tour Manag 59:467–483
23. Moro S et al (2015) Business intelligence in banking: A literature analysis from 2002–2013
using text mining and latent Dirichlet allocation. Expert Syst Appl 42(3):1314–1324
24. Krestel R et al (2009) Latent dirichlet allocation for tag recommendation. In: Proceedings of
the third ACM conference on recommender systems, pp 61–68
25. Caldas J et al (2009) Probabilistic retrieval and visualization of biologically relevant microarray
experiments. Bioinformatics 25(12):145–153
26. Pinoli P et al (2014) Latent Dirichlet allocation based on Gibbs sampling for gene function
prediction. In: Computational intelligence in bioinformatics and computational biology, pp 1–8
27. Backenroth D et al (2018) FUN-LDA: a latent dirichlet allocation model for predicting tissuespecific functional effects of noncoding variation: methods and applications. Am J Hum Genet
102(5):920–942
28. Virtanen S et al (2012) Bayesian group factor analysis. In: Artificial Intelligence and Statistics,
pp 1269–1277
29. Klami A et al (2015) Group factor analysis. IEEE Trans Neural Netw Learn Syst
26(9):2136–2147
30. Leppäaho E et al (2017) GFA: exploratory analysis of multiple data sources with group factor
analysis. J Mach Learn Res 18(1):1294–1298
31. Neal RM (1996) Bayesian learning for neural networks. Springer-Verlag
32. Kolda T, Bader B (2009) Tensor decompositions and applications. SIAM Review
51(3):455–500
33. Carroll JD, Chang JJ (1970) Analysis of individual differences in multidimensional scaling via
an n-way generalization of Eckart-Young decomposition. Psychometrika 35(3):283–319
34. Harshman RA (1970) Foundations of the parafac procedure: models and conditions for an
explanatory multimodal factor analysis, UCLA Working Papers in Phonetics, 16:1–84
35. Tucker LR (1966) Some mathematical notes on three-mode factor analysis. Psychometrika
31(3):279–311
36. Yener B et al (2008) Multiway modeling and analysis in stem cell systems biology. BMC Syst
Biol 2(1):63
37. Omberg L et al (2007) A tensor higher-order singular value decomposition for integrative analysis of dna microarray data from different studies. Proc Natl Acad Sci 104(47):18371–18376
38. Li W et al (2011) Integrative analysis of many weighted co-expression networks using tensor
computation. PLoS Comput Biol 7(6):e1001106