4 Matrix and Tensor Factorization Methods …
73
39. Brink-Jensen K et al (2013) Integrative analysis of metabolomics and transcriptomics data: a
unified model framework to identify underlying system pathways. PLoS ONE 8(9):e72116
40. Khan SA, Kaski S (2014) Bayesian multi-view tensor factorization. In: Joint european conference on machine learning and knowledge discovery in databases. Springer, Berlin, Heidelberg,
pp 656–671
41. Khan SA et al (2016) Bayesian multi-tensor factorization. Mach Learn 105(2):233–253
42. Andersson CA, Bro R (2000) The N-way toolbox for MATLAB. Chemometr Intell Lab Syst
52(1):1–4
43. Mørup M, Hansen LK (2009) Automatic relevance determination for multiway models. J
Chemom 23(7):352–363
44. Xiong L (2010) Temporal collaborative filtering with bayesian probabilistic tensor factorization, vol. 10. In: Proceedings of SIAM data mining, pp 211–222
45. Khan SA, Ammad-ud-din M (2017) TensorBF: an R package for Bayesian tensor factorization,
bioRxiv, 6097048 1–6
46. Lamb J et al (2006) The connectivity map: using gene-expression signatures to connect small
molecules, genes and disease. Science 313(5795):1929–1935
47. Khan SA et al (2012) Comprehensive data-driven analysis of the impact of chemoinformatic
structure on the genome-wide biological response profiles of cancer cells to 1159 drugs. BMC
Bioinform 13(1):112–127
48. Khan SA (2014) Identification of structural features in chemicals associated with cancer drug
response: a systematic data-driven analysis. Bioinformatics 30(17):i497–i504
49. Shoemaker RH (2006) The nci60 human tumour cell line anticancer drug screen. Nat Rev
Cancer 6(10):813–823
50. Isaacs JS et al (2003) Heat shock protein 90 as a molecular target for cancer therapeutics.
Cancer Cell 3(3):213–217
51. Neckers L, Workman P (2012) Hsp90 molecular chaperone inhibitors: are we there yet? Clin
Cancer Res 18(1):64–76
52. Igarashi Y et al (2015) Open TG-GATEs: a large-scale toxicogenomics database. Nucleic Acids
Res 43:D921–D927
53. Hore V et al (2016) Tensor decomposition for multiple-tissue gene expression experiments.
Nat Genet 48(9):1094
54. Tang J et al (2018) Drug target commons: a community effort to build a consensus knowledge
base for drug-target interactions. Cell Chem Biol 25(2):224–229
Suleiman Ali Khan received his Ph.D. in Machine Learning from Aalto University, Finland in
2015. He has specialized in Bayesian machine learning and its applications in bioinformatics.
He subsequently joined FIMM as a postdoctoral researcher and has developed novel statistical
machine learning methods for various drug discovery and drug repurposing approaches. He is
currently an Academy of Finland postdoctoral researcher working on identification of predictive genomic markers through advanced and integrative machine learning methods. His current
research interests include statistical machine learning, explainable artificial intelligence, computational biology, and medicine.
Tero Aittokallio received his Ph.D. in Applied Mathematics from the University of Turku in 2001.
He did his postdoctoral training in the Systems Biology Lab at the Institut Pasteur (2006–2007),
where he focused on network biology applications using high-throughput experimental assays. Dr.
Aittokallio then launched his independent career as a principal investigator in the Turku Biomathematics Research Group in 2007 and received a five-year appointment as an Academy of Finland
Research Fellow (2007–2012). In 2011, he started as EMBL Group Leader at FIMM, where his
research group is developing computational biology approaches to personalized medicine.
73
39. Brink-Jensen K et al (2013) Integrative analysis of metabolomics and transcriptomics data: a
unified model framework to identify underlying system pathways. PLoS ONE 8(9):e72116
40. Khan SA, Kaski S (2014) Bayesian multi-view tensor factorization. In: Joint european conference on machine learning and knowledge discovery in databases. Springer, Berlin, Heidelberg,
pp 656–671
41. Khan SA et al (2016) Bayesian multi-tensor factorization. Mach Learn 105(2):233–253
42. Andersson CA, Bro R (2000) The N-way toolbox for MATLAB. Chemometr Intell Lab Syst
52(1):1–4
43. Mørup M, Hansen LK (2009) Automatic relevance determination for multiway models. J
Chemom 23(7):352–363
44. Xiong L (2010) Temporal collaborative filtering with bayesian probabilistic tensor factorization, vol. 10. In: Proceedings of SIAM data mining, pp 211–222
45. Khan SA, Ammad-ud-din M (2017) TensorBF: an R package for Bayesian tensor factorization,
bioRxiv, 6097048 1–6
46. Lamb J et al (2006) The connectivity map: using gene-expression signatures to connect small
molecules, genes and disease. Science 313(5795):1929–1935
47. Khan SA et al (2012) Comprehensive data-driven analysis of the impact of chemoinformatic
structure on the genome-wide biological response profiles of cancer cells to 1159 drugs. BMC
Bioinform 13(1):112–127
48. Khan SA (2014) Identification of structural features in chemicals associated with cancer drug
response: a systematic data-driven analysis. Bioinformatics 30(17):i497–i504
49. Shoemaker RH (2006) The nci60 human tumour cell line anticancer drug screen. Nat Rev
Cancer 6(10):813–823
50. Isaacs JS et al (2003) Heat shock protein 90 as a molecular target for cancer therapeutics.
Cancer Cell 3(3):213–217
51. Neckers L, Workman P (2012) Hsp90 molecular chaperone inhibitors: are we there yet? Clin
Cancer Res 18(1):64–76
52. Igarashi Y et al (2015) Open TG-GATEs: a large-scale toxicogenomics database. Nucleic Acids
Res 43:D921–D927
53. Hore V et al (2016) Tensor decomposition for multiple-tissue gene expression experiments.
Nat Genet 48(9):1094
54. Tang J et al (2018) Drug target commons: a community effort to build a consensus knowledge
base for drug-target interactions. Cell Chem Biol 25(2):224–229
Suleiman Ali Khan received his Ph.D. in Machine Learning from Aalto University, Finland in
2015. He has specialized in Bayesian machine learning and its applications in bioinformatics.
He subsequently joined FIMM as a postdoctoral researcher and has developed novel statistical
machine learning methods for various drug discovery and drug repurposing approaches. He is
currently an Academy of Finland postdoctoral researcher working on identification of predictive genomic markers through advanced and integrative machine learning methods. His current
research interests include statistical machine learning, explainable artificial intelligence, computational biology, and medicine.
Tero Aittokallio received his Ph.D. in Applied Mathematics from the University of Turku in 2001.
He did his postdoctoral training in the Systems Biology Lab at the Institut Pasteur (2006–2007),
where he focused on network biology applications using high-throughput experimental assays. Dr.
Aittokallio then launched his independent career as a principal investigator in the Turku Biomathematics Research Group in 2007 and received a five-year appointment as an Academy of Finland
Research Fellow (2007–2012). In 2011, he started as EMBL Group Leader at FIMM, where his
research group is developing computational biology approaches to personalized medicine.