研究業績

PUBLICATIONS

査読付き欧文国際雑誌に掲載された原著論文

1.        Zhang Y-Z, Yamaguchi R, Imoto, S, Miyano S, Sequence-specific bias correction for RNA-seq data using recurrent neural networks, BMC Genomics, in press.

2.        Hasegawa T, Hayashi S, Shimizu E, Mizuno S, Yamaguchi R, Miyano S, Nakagawa H, Imoto S. An in silico automated pipeline to identify tumor specic neoantigens from whole genome and exome sequencing data. Proc. 12th International Symposium on Bioinformatics Research and Applications, 2016.

3.        Yoshino T, Katayama K, Horiba Y, Munakata K, Yamaguchi R, Imoto S, Miyano S, Mima H, Watanabe K. Predicting Japanese Kampo formulas by analyzing database of medical records: a preliminary observational study, BMC Medical Informatics and Decision Making, in press.

4.        Park H, Niida A, Imoto S, Miyano S, Interaction based feature selection for uncovering cancer driver genes via copy number driven expression level, Journal of Computational Biology, in press.

5.        Mao Y, Tamura T, Yuki Y, Abe D, Tamada Y, Imoto S, Tanaka H, Homma H, Miyano S, Okazawa H. The hnRNP-Htt axis regulates necrotic cell death induced by transcriptional repression through impaired RNA splicing, Cell Death & Disease, in press.

6.        Muramatsu T, Kozaki K-i, Imoto S, Yamaguchi R, Tsuda H, Kawano T, Fujiwara N, Morishita M, Miyano S, Inazawa J. The hypusine cascade promotes cancer progression and metastasis through the regulation of RhoA in squamous cell carcinoma, Oncogene, in press.

7.        Park H, Shiraishi Y, Imoto S, Miyano S. Adaptive penalized logistic regression for uncovering biomarker associated with anti-cancer drug sensitivity, IEEE IEEE/ACM Transactions on Computational Biology and Bioinformatics, in press.

8.        Moriyama T, Shiraishi Y, Chiba K, Yamaguchi R, Imoto S, Miyano S. OVarCall: Bayesian mutation calling method utilizing overlapping paired-end reads, Lecture Notes in Computer Science, 9683, 40-51 (2016).

9.        Kato T, Inoue H, Imoto S, Tamada Y, Miyamoto T, Matsuo Y, Nakamura Y, Park JH. Oncogenic roles of TOPK and MELK, and effective growth suppression by small molecular inhibitors in kidney cancer cells, Oncotarget, 7, 17652-17664 (2016).

10.      Park H, Imoto S, Miyano S. Recursive random lasso (RRLasso) for identifying anti-cancer drug targets, PLoS ONE, 10, e0141869 (2015).

11.      Kayano M, Matsui H, Yamaguchi R, Imoto S, Miyano S. Gene set differential analysis of time course expression profiles via sparse estimation in functional logistic model with application to time-dependent biomarker detection, Biostatistics, in press.

12.      Hasegawa T, Niida A, Moria T, Shimamura T, Yamaguchi R, Miyano S, Akutsu T, Imoto S. A likelihood-free filtering method via approximate Bayesian computation in evaluating biological simulation models, Computational Statistics and Data Analysis, 94, 63-74 (2016).

13.      Yoshino T, Katayama K, Horiba Y, Munakata K, Yamaguchi R, Imoto S, Miyano S, Mima H, Watanabe K, Mimura M. The Difference between the Two Representative Kampo Formulas for Treating Dysmenorrhea: An Observational Study, Evidence-Based Complementary and Alternative Medicine, Article ID 3159617 (2016)

14.      Nakata A, Yoshida R, Yamaguchi R, Yamauchi M, Tamada Y, Fujita A, Shimamura T, Imoto S, Higuchi T, Nomura M, Kimura T, Nokihara H, Higashiyama M, Kondoh K, Nishihara H, Tojo A, Yano S, Miyano S, Gotoh N. Elevated beta-catenin pathway as a novel target for patients with resistance to EGF receptor targeting drugs, Scientific Reports, 5, 13076 (2015).

15.      Sainia H, Raicara G, Sharma A, Lala S, Dehzangib A, Lyonsb J, Paliwalb KK, Imoto S, Miyano S. Probabilistic expression of spatially varied amino acid dimers into general form of Chou׳s pseudo amino acid composition for protein fold recognition, J Theoretical Biology, 380, 291–298 (2015).

16.      Yew PY, Alachkar H, Yamaguchi R, Kiyotani K, Fang H, Yap KL, Liu HT, Wickrema A, Artz A, Besien KV, Imoto S, Miyano S, Bishop M, Stock W, Nakamura Y. Quantitative characterization of T cell repertoire in allogeneic hematopoietic stem cell transplant recipients, Bone Marrow Transplantation, 50, 1227-1234 (2015).

17.      Hasegawa T, Mori T, Yamaguchi R, Shimamura T, Miyano S, Imoto S, Akutsu T. Genomic data assimilation using a higher moment filtering technique for restoration of gene regulatory networks, BMC Systems Biology, 9:14 (2015).

18.      Iwakawa R, Kohno T, Totoki Y, Shibata T, Tsuchihara K, Mimaki S, Tsuta K, Narita Y, Nishikawa R, Noguchi M, Harris CC, Robles AI, Yamaguchi R, Imoto S, Miyano S, Totsuka H, Yoshida T, Yokota J. Expression and clinical significance of genes frequently mutated in small cell lung cancers defined by whole exome/RNA sequencing, Carcinogenesis, 36, 616-621 (2015).

19.      Ayada E, Niida A, Hasegawa T, Miyano S, Imoto S. Binary contingency table method for analyzing gene mutation in cancer genome, Proc. 11th International Symposium on Bioinformatics Research and Applications, Lecture Note in Computer Science, Springer-Verlag, 9096, 12-23 (2015).

20.      Kobayashi K, Sakurai K, Hiramatsu H, Inada K, Shiogama K, Nakamura S, Suemasa F, Kobayashi K, Imoto S, Haraguchi T, Ito H,Ishizaka A, Tsutsumi Y, Iba H. The miR-199a/Brm/EGR1 axis is a determinant of anchorage-independent growth in epithelial tumor cell lines, Scientific Reports, 5, 8428 (2015).

21.      Chiba K, Shiraishi Y, Nagata Y, Yoshida K, Imoto S, Ogawa S, Miyano S. Genomon ITDetector: A tool for somatic internal tandem duplication detection from cancer genome sequencing data. Bioinformatics, 31(1), 116-118 (2015).

22.      Chikahara Y, Niida A, Yamaguchi R, Imoto S, Miyano S. Integrative clustering of cancer genome data using infinite relational models. Proc the 7th International Conference on Bioinformatics and Computational Biology (BICoB-2015), 11-18 (2015).

23.      Ikenoue T, Yamaguchi K, Komura M, Imoto S, Yamaguchi R, Shimizu E, Kasuya S, Shibuya T, Hatakeyama S, Miyano S, Furukawa Y. Attenuated familial adenomatous polyposis with desmoids by an APC mutation. Human Genome Variation, 2, 15011 (2015).

24.      Ito H, Shiwaku H, Yoshida C, Homma H, Luo H, Chen X, Fujita K, Musante L, Fischer U, Frints SGM, Romano C, Ikeuchi Y, Shimamura T, Imoto S, Miyano S, Muramatsu S, Kawauchi T, Hoshino M, Sudol M, Arumughan A, Wanker EE, Rich T, Schwartz C, Matsuzaki F, Bonni A, Kalscheuer VM, Okazawa H. In utero gene therapy rescues microcephaly caused by Pqbp1-hypofunction in neural stem progenitor cells. Molecular Psychiatry, 20, 459-471 (2015).

25.      Park H, Niida A, Miyano S, Imoto S. Sparse overlapping group lasso for integrative multi-omics analysis. J Computational Biology. 22(2), 73-84 (2015).

26.      Saini H, Raicar G, Lal S, Dehzangi A, Lyons J, Paliwal KK, Imoto S, Miyano S, Sharma A. Genetic algorithm for an optimized weighted voting scheme incorporating k-separated bigram transition probabilities to improve protein fold recognition. Asia-Pacific World Congress on Computer Science and Engineering, 1-7. (DOI: 10.1109/APWCCSE.2014.7053846)

27.      Tagawa K, Homma H, Saito A, Fujita K, Chen X, Imoto S, Oka T, Ito H, Motoki K, Yoshida C, Hatsuta H, Murayama S, Iwatsubo T, Miyano S, Okazawa H. Comprehensive phosphoproteome analysis unravels the core signaling network that initiates the earliest synapse pathology in preclinical Alzheimer’s disease brain. Human Molecular Genetics. 24(2), 540-558 (2015).

28.      Tamura K, Hazama S, Yamaguchi R, Imoto S, Takenouchi H, Inoue Y, Kanekiyo S, Shindo Y, Miyano S, Nakamura Y, Kiyotani K. Characterization of T cell repertoire in tumor tissues and blood in advanced colorectal cancers through deep T cell receptor sequencing. Oncology Letters, in press.

29.      Yamaguchi K, Komura M, Yamaguchi R, Imoto S, Shimizu E, Kasuya S, Shibuya T, Hatakeyama S, Takahashi N, Ikenoue T, Hata K, Tsurita G, Shinozaki M, Suzuki Y, Sugano S, Miyano S, Furukawa Y. Detection of APC germline mosaicism by next-generation sequencing in an FAP patient. Journal of Human Genetics, 60, 227-231 (2015).

30.      Arima C, Kajino T, Tamada Y, Imoto S, Shimada Y, Nakatochi M, Suzuki M, Isomura H, Yatabe Y, Yamaguchi T, Yanagisawa K, Miyano S, Takahashi T. Lung adenocarcinoma subtypes definable by lung development-related miRNA expression profiles in association with clinicopathologic features. Carcinogenesis. 35(10), 2224-2231 (2014).

31.      Fang H, Yamaguchi R, Liu X, Daigo Y, Yew PY, Tanikawa C, Matsuda K, Imoto S, Miyano S, Nakamura Y. Quantitative T cell repertoire analysis by deep cDNA sequencing of T cell receptor α and β chains using next-generation sequencing (NGS). OncoImmunology, 3(12), e968467 (2014).

32.      Barclay SS, Tamura T, Ito H, Fujita K, Tagawa K, Shimamura T, Katsuta A, Shiwaku H, Sone M, Imoto S, Miyano S, Okazawa, H. Systems biology analysis of Drosophila in vivo screen data elucidates core networks for DNA damage repair in SCA1. Human Molecular Genetics. 23(5), 1345-1364 (2014).

33.      Hasegawa T, Mori T, Yamaguchi R, Imoto S, Miyano S, Akutsu T. An efficient data assimilation schema for restoration and extension of gene regulatory networks using time-course observation data. J Computational Biology. 21(11), 785-798 (2014).

34.      Hasegawa T, Nagasaki M, Yamaguchi R, Imoto S, Miyano S. An efficient method of exploring simulation models by assimilating literature and biological observational data. BioSystems. 121, 54-66 (2014).

35.      Hasegawa T, Yamaguchi R, Nagasaki M, Miyano S, Imoto S. Inference of gene regulatory networks incorporating multi-source biological knowledge via a state space model with L1 regularization. PLoS One. 9(8), e105942 (2014).

36.      Nishiura H, Ejima K, Mizumoto K, Nakaoka S, Inaba H, Imoto S, Yamaguchi R, Saito MM. Cost-effective length and timing of school closure during an influenza pandemic depend on the severity. Theoretical Biology and Medical Modelling. 11, 5 (2014).

37.      Sugimachi K, Niida A, Yamamoto K, Shimamura T, Imoto S, Iinuma H, Shinden Y, Eguchi H, Sudo T, Watanabe M, Tanaka J, Kudo S, Hase K, Kusunoki M, Yamada K, Shimada Y, Sugihara K, Maehara Y, Miyano S, Mori M, Mimori K. Allelic imbalance at an 8q24 oncogenic SNP is involved in activating MYC in human colorectal cancer, Annals of Surgical Oncology. 21, Suppl 4, S515-21 (2014).

38.      Park H, Shimamura T, Miyano S, Imoto S. Robust prediction of anti-cancer drug sensitivity and sensitivity-specific biomarker. PLoS One. 9(10), e108990 (2014).

39.      Sharma A, Dehzangi A, Lyons J, Imoto S, Miyano S, Nakai K, Patil A. Evaluation of sequence features from intrinsically disordered regions for the estimation of protein function, PLoS One. 9(2), e89890 (2014).

40.      Takahashi R, Nagayama S, Furu M, Kajita Y, Jin Y-H, Kato T, Imoto S, Sakai Y, Toguchida J. AFAP1L1, a novel associating partner with vinculin, modulates cellular morphology and motility, and promotes the progression of colorectal cancers. Cancer Medicine. 3(4), 759–774 (2014).

41.      Tokunaga H, Munakata K, Katayama K, Yamaguchi R, Imoto S, Miyano S, Watanabe K. Clinical data mining related to the Japanese Kampo concept "Hie" (oversensivity to coldness) in men and pre- and postmenopausal women. Evidence-Based Complementary and Alternative Medicine. Article ID: 832824 (2014).

42.      Usuyama N, Shiraishi Y, Sato Y, Kume H, Homma Y, Ogawa S, Miyano S, Imoto S. HapMuC: somatic mutation calling using heterozygous germline variants near candidate mutations. Bioinformatics. 30(23), 3302-3309 (2014).

43.      Affara M, Sanders D, Araki H, Tamada Y, Dunmore BJ, Humphreys S, Imoto S, Savoie C, Miyano S, Kuhara S, Jeffries D, Print C, Charnock-Jones DS. Vasohibin-1 is identified as a master-regulator of endothelial cell apoptosis using gene network analysis. BMC Genomics. 14(1), 23 (2013).

44.      Katayama K, Yamaguchi R, Imoto S, Watanabe K, Miyano S. Analysis of questionnaire for traditional medicine and development of decision support system. Evidence-Based Complementary and Alternative Medicine. Article ID: 974139 (2013).

45.      Katayama K, Yoshino T, Munakata K, Yamaguchi R, Imoto S, Miyano S, Watanabe K. Prescription of Kampo drugs in the Japanese Health Care Insurance Program. Evidence-Based Complementary and Alternative Medicine. Article ID: 576973 (2013).

46.      Kayano M, Imoto S, Yamaguchi R, Miyano S. Multi-omics approach for estimating metabolic networks using low-order partial correlations. J Computational Biology20(8), 571-582 (2013).

47.      Komatsu M, Yoshimaru T, Matsuo T, Kiyotani K, Miyoshi Y, Tanahashi T, Rokutan K, Yamaguchi R, Saito A, Imoto S, Miyano S, Nakamura Y, Sasa M, Shimada M, Katagiri T. Molecular features of triple negative breast cancer cells by genome-wide gene expression profiling analysis. International J Oncology42(2), 478-506 (2013).

48.      Niida A, Tremmel G, Imoto S, Miyano S. Multilayer cluster heat map visualizing biological tensor data. Lecture Notes in Bioinformatics. 8213, 116-125 (2013).

49.      Saito MM, Imoto S, Yamaguchi R, Sato H, Nakada H, Kami M, Miyano S, Higuchi T. Extension and verification of the SEIR model on the 2009 influenza A (H1N1) pandemic in Japan.  Mathematical Biosciences. 246(1), 47-54 (2013).

50.      Saito MM, Imoto S, Yamaguchi R, Tsubokura M, Kami M, Nakada H, Sato H, Miyano S, Higuchi T. Enhancement of collective immunity by selective vaccination against emerging influenza pandemic. PLoS One. 8(9), e72866 (2013).

51.      Sharma A, Paliwal KK, Dehzangi A, Lyons J, Imoto S, Miyano S. A Strategy to select suitable physicochemical attributes of amino acids for protein fold recognition. BMC Bioinformatics14, 233 (2013).

52.      Sharma A, Paliwal KK, Imoto S, Miyano S. A feature selection method using improved regularized linear discriminant analysis. Machine Vision and Applications. 25(3), 775-786 (2013).

53.      Sharma A, Paliwal KK, Imoto S, Miyano S, Sharma V, Ananthanarayanan R. A feature selection method using fixed-point algorithm for DNA microarray gene expression data. International J Knowledge-Based and Intelligent Engineering Systems18, 55-59 (2013).

54.      Takatsuno Y, Mimori K, Yamamoto K, Sato T, Niida A, Inoue H, Imoto S, Kawano S, Yamaguchi R, Toh H, Iinuma H, Ishimaru S, Ishii H, Suzuki S, Tokudome S, Watanabe M, Tanaka J, Kudo S, Mochizuki H, Kusunoki M, Yamada K, Shimada Y, Moriya Y, Miyano S, Sugihara K, Mori M. The rs6983267 SNP Is associated with MYC transcription efficiency, which promotes progression and worsens prognosis of colorectal cancer. Annals of Surgical Oncology20(4), 1395-1402 (2013).

55.      Tamura T, Sone M, Nakamura Y, Shimamura T, Imoto S, Miyano S, Okazawa H. A restricted level of PQBP1 is needed for the best longevity of Drosophila. Neurobiology of Aging34(1): 356.e11-20 (2013).

56.      Yamaguchi K, Yamaguchi R, Takahashi N, Ikenoue T, Fujii T, Shinozaki M, Tsurita G, Hata K, Niida A, Imoto S, Miyano S, Nakamura Y, Furukawa Y. Overexpression of cohesion establishment factor DSCC1 through E2F in colorectal cancer. PLoS One. 9(1), e85750 (2013).

57.      Yamaguchi R, Imoto S, Kami M, Watanabe K, Miyano S, Yuji K. Does Twitter trigger bursts in signature collections? PLoS One. 8(3), e58252 (2013).

58.      Yokobori T, Iinuma H, Shimamura T, Imoto S, Ishii H, Sugimachi K, Iwatsuki M, Ota D, Ohkuma M, Iwaya T, Nishida N, Kogo R, Sudo T, Tanaka F, Shibata K, Toh H, Sato T, Barnard GF, Fukagawa T, Yamamoto S, Nakanishi H, Sasaki S, Miyano S, Watanabe T, Kuwano H, Mimori K, Pantel K, Mori M. Plastin3 is a novel marker for circulating tumor cells undergoing the epitheial-mesenchymal transition and is associated with colorectal cancer prognosis. Cancer Research73(7), 2059-2069 (2013).

59.      Yoshimaru T, Komatsu M, Matsuo T, Chen Y-A, Murakami Y, Mizuguchi K, Mizohata E, Inoue T, Akiyama M, Yamaguchi R, Imoto S, Miyano S, Miyoshi Y, Sasah M, Nakamura Y, Katagiri T. Targeting the BIG3-PHB2 interaction to overcome tamoxifen resistance in breast cancer cells. Nature Communications4, 2443 (2013).

60.      Yoshino T, Katayama K, Munakata K, Horiba Y, Yamaguchi R, Imoto S, Miyano S, Watanabe K. Statistical Analysis of Hie (cold sensation) and Hiesho (cold disorder) in Kampo Clinic. Evidence-Based Complementary and Alternative Medicine. Article ID: 398458 (2013).

61.      Hurley D, Araki H, Tamada T, Dunmore B, Sanders D, Humphreys S, Affara M, Imoto S, Yasuda K, Tomiyasu Y, Tashiro K, Savoie C, Cho V, Smith S, Kuhara S, Miyano S, Charnock-Jones DS, Crampin EJ, Print CG. Gene network inference and visualization tools for biologists: application to new human transcriptome datasets. Nucleic Acids Research. 40(6), 2377-2398 (2012).

62.      Ishimaru S, Mimori K, Yamamoto K, Inoue H, Imoto S, Kawano, S, Yamaguchi R, Sato T, Toh H, Iinuma,H, Maeda T, Ishii H, Suzuki S, Tokudome S, Watanabe M, Tanaka J, Kudo S, Sugihara K, Hase K, Mochizuki H, Kusunoki M, Yamada K, Shimada Y, Moriya Y, Barnard GF, Miyano S, Mori M. Increased risk for CRC in diabetic patients with the nonrisk allele of SNPs at 8q24. Annals of Surgical Oncology. 19(9), 2853-2858 (2012).

63.      Kojima K, Imoto S, Yamaguchi R, Fujita A, Yamauchi M, Gotoh N, Miyano S. Identifying regulational alterations in gene regulatory networks by state space representation of vector autoregressive models and variational annealing. BMC Genomics. 13(Suppl 1), S6 (2012).

64.      Katayama K, Yamaguchi R, Imoto S, Matsuura K, Watanabe K, Miyano S. Analysis of questionnaire for Traditional Medical and develop decision support system. Proc. International Workshop on Biomedical and Health Informatics. 762-763 (2012).

65.      Kawano S, Shimamura T, Niida A, Imoto S, Yamaguchi R, Nagasaki M, Yoshida R, Print C, Miyano S. Identifying gene pathways associated with cancer characteristics via sparse statistical methods. IEEE/ACM Transactions on Computational Biology and Bioinformatics. 9(4), 966-972 (2012).

66.      Niida A, Imoto S, Shimamura T, Miyano S. Statistical model-based testing to evaluate the recurrence of genomic aberrations. Bioinformatics. 28, i115-i120 (2012).

67.      Ogami K, Yamaguchi R, Imoto S, Tamada Y, Araki H, Print C, Miyano S. Computational gene network analysis reveals TNF-induced angiogenesis. BMC Systems Biology6(Suppl 2), S12 (2012).

68.      Saito MM, Imoto S, Yamaguchi R, Miyano S, Higuchi T. Identifiability of local transmissibility parameters in agent-based pandemic simulation. Proc. 14th International Conference on Information FUSION. 2466-2471 (2012).

69.      Saito MM, Imoto S, Yamaguchi R, Miyano S, Higuchi T. Parallel agent-based simulator for influenza pandemic. Lecture Notes in Computer Science. 7068, 361-370 (2012).

70.      Sharma A, Imoto S, Miyano S. A between-class overlapping filter-based method for transcriptome data analysis. J Bioinformatics and Computational Biology10(5), 1250010 (2012).

71.      Sharma A, Imoto S, Miyano S. A top-r feature selection algorithm for microarray gene expression data. IEEE/ACM Transactions on Computational Biology and Bioinformatics. 9(3), 754-64 (2012).

72.      Sharma A, Imoto S, Miyano S. A filter based feature selection algorithm using null space of covariance matrix for DNA microarray gene expression data. Current Bioinformatics7(3), 289-294 (2012).

73.      Sharma A, Imoto S, Miyano S, Sharma V. Null space based feature selection method for gene expression data. International J Machine Learning and Cybernetics3(4), 269-276 (2012).

74.      Sharma A, Paliwal KK, Imoto S, Miyano S Principal component analysis using QR decomposition. International J Machine Learning and Cybernetics. (10.1007/s13042-012-0131-7) (2012). (Online)

75.      Wang L, Hurley D, Watkins W, Araki H, Tamada Y, Muthukaruppan, A, Ranjard L, Derkac E, Imoto S, Miyano S, Crampin E, Print C. Cell cycle gene networks are associated with melanoma prognosis. PLoS One7(4), e34247 (2012).

76.      Yamauchi M, Yamaguchi R, Nakata A, Kohno T, Nagasaki M, Shimamura T, Imoto S, Saito A, Ueno K, Hatanaka Y, Yoshida R, Higuchi T, Nomura M, Beer DG, Yokota J, Miyano S, Gotoh N. Epidermal growth factor receptor tyrosine kinase defines critical prognostic genes of stage I lung adenocarcinoma. PLoS One7(9), e43923 (2012).

77.      Yamamoto M, Yamaguchi R, Muanakata K, Takashima K, Nishiyama M, Hioki K, Ohnishi Y, Nagasaki M, Imoto S, Miyano S, Ishige A, Watanabe K. A microarray analysis of gnotobiotic mice indicating that microbial exposure during the neonatal period plays an essential role in immune system development. BMC Genomics13, 335 (2012).

78.      Yuji K,* Imoto S,* Yamaguchi R, Matsumura T, Murashige N, Kodama Y, Miyano S, Imai K, Kami M. Forecasting Japan's physician shortage in 2035 as the first full-fledged aged society.  PLoS One7(11), e50410 (2012). (*Equal contribution)

79.      Furu M, Kajita Y, Nagayama S, Ishibe T, Shima Y, Uejima D, Aoyama T, Nakayama T, Nakamura T, Nakashima Y, Ikegawa M, Imoto S, Katagiri T, Nakamura Y, Toguchida J. Identification of AFAP1L1 as a prognostic marker for spindle cell sarcomas. Oncogene30, 4015-4025 (2011).

80.      Hasegawa T, Yamaguchi R, Nagasaki M, Imoto S, Miyano S. Comprehensive pharmacogenomic pathway screening by data assimilation. Lecture Notes in Computer Science. 6674, 160-171 (2011).

81.      Imoto S, Kojima K, Perrier E, Tamada Y, Miyano S. Searching optimal Bayesian network structure on constraint search space: super-structure approach. Lecture Notes in Computer Science. 6797, 210-218 (2011).

82.      Katayama K, Yamaguchi R, Imoto S, Matsuura K, Watanabe K, Miyano S. Clustering for visual analogue scale data in symbolic data analysis. Procedia Computer Science6, 370-374 (2011).

83.      Katayama K, Yamaguchi R, Imoto S, Matsuura K, Watanabe K, Miyano S. Transform of visual analogue scale data and their clustering. International J Knowledge Engineering and Soft Data Paradigms3(2), 143-151 (2011).

84.      Katayama K, Yamaguchi R, Imoto S, Tokunaga H, Imazu Y, Matuura K, Watanabe K, Miyano S. Symbolic hierarchical clustering for visual analogue scale data. KES-Springer Smart Innovations, Systems and Technologies Series. 10, 799-805 (2011).

85.      Kogo R, Shimamura T, Mimori K, Kawahara K, Imoto S, Sudo T, Tanaka F, Shibata K, Suzuki A, Komune S, Miyano S, Mori M. Long non-coding RNA HOTAIR regulates Polycomb-dependent chromatin modification and is associated with poor prognosis in colorectal cancers. Cancer Research. 71(20), 6320-6326 (2011).

86.      Saito M, Imoto S, Yamaguchi R, Miyano S, Higuchi T. Estimation of macroscopic parameter in agent-based pandemic simulation. Proc. 13th International Conference on Information Fusion. 1-6 (2011).

87.      Sharma A, Hock Koh, C, Imoto S, Miyano S. A strategy of finding the optimal number of features on gene expression data. Electronics Letters. 47(8), 480-482 (2011).

88.      Shimamura T, Imoto S, Shimada Y, Hosono Y, Niida A, Nagasaki M, Yamaguchi R, Takahashi T, Miyano S. A novel network profiling analysis reveals system changes in epithelial-mesenchymal transition. PLoS One. 6(6), e20804 (2011).

89.      Sogawa Y, Shimizu S, Shimamura T, Hyvarinen A, Washio T, Imoto S. Estimating exogenous variables in data with more variables than observations. Neural Networks24(8), 875-880 (2011).

90.      Tamada Y, Imoto S, Araki H, Nagasaki M, Print C, Charnock-Jones S, Miyano S. Estimating genome-wide gene networks using nonparametric Bayesian network models on massively parallel computers. IEEE/ACM Transactions on Computational Biology and Bioinformatics. 8(3), 683-697 (2011).

91.      Tamada Y, Imoto S, Miyano S. Parallel algorithm for learning optimal Bayesian network structure. J Machine Learning Research12, 2437-2459 (2011).

92.      Tamada Y, Shimamura T, Yamaguchi R, Imoto S, Nagasaki M, Miyano S. SiGN: large-scale gene network estimation environment for high performance computing. Genome Informatics. 25, 40-52 (2011).

93.      Tamada Y, Yamaguchi R, Imoto S, Hirose O, Yoshida R, Nagasaki M, Miyano S. SiGN-SSM: open source parallel software for estimating gene networks with state space models. Bioinformatics27(8), 1172-1173 (2011).

94.      Yamauchi M, Yoshino I, Yamaguchi R, Shimamura T, Nagasaki M, Imoto S, Niida A, Koizumi F, Kohno T, Yokota J, Miyano S, Gotoh N. N-cadherin expression is a potential survival mechanism of gefitinib-resistant lung cancer cells. American J Cancer Research. 1, 823-833 (2011).

95.      Fujita A, Nagasaki M, Imoto S, Saito A, Ikeda E, Shimamura T, Yamaguchi R, Suzuki H, Hayashizaki Y, Miyano S. Comparison of gene expression profiles produced by CAGE, illumina microarray and Real Time RT-PCR. Genome Informatics24, 56-68 (2010).

96.      Higashigaki T, Kojima K, Yamaguchi R, Inoue, M, Imoto S, Miyano S. Identifying hidden confounders in gene networks by Bayesian networks. Proc. 10th IEEE Bioinformatics and Bioengineering. 168-173 (2010).

97.      Kawano S, Shimamura T, Niida A, Imoto S, Yamaguchi R, Nagasaki M, Yoshida R, Print C, Miyano S. Discovering functional gene pathways associated with cancer heterogeneity via sparse supervised learning. Proc. IEEE Bioinformatics and Biomedicine. 253-258 (2010).

98.      Kojima K, Imoto S, Nagasaki M, Miyano S. Gene regulatory network clustering for graph layout based on microarray gene expression data. Genome Informatics24, 84-95 (2010).

99.      Kojima K, Perrier E, Imoto S, Miyano S. Optimal search on clustered structural constraint for learning Bayesian network structure. J Machine Learning Research. 11, 285-310 (2010).

100.   Niida A, Imoto S, Yamaguchi R, Nagasaki M, Fujita A, Shimamura T, Miyano S. Model-free unsupervised gene set screening based on information enrichment in expression profiles. Bioinformatics26, 3090-3097 (2010).

101.   Niida A, Imoto S, Yamaguchi R, Nagasaki M, Miyano S. Gene set-based module discovery decodes cis-regulatory codes governing diverse gene expression across human multiple tissues. PLoS One5(6), e10910 (2010).

102.   Sato H,* Nakada H,* Yamaguchi R,* Imoto S,* Miyano S, Kami M. When should we intervene to control the 2009 influenza A (H1N1) pandemic? Euro Surveillance15(1), pii=19455 (2010). (*Equal contribution)

103.   Shimamura T, Imoto S, Nagasaki M, Yamauchi M, Yamaguchi R, Fujita A, Tamada Y, Gotoh N, Miyano S. Collocation-based sparse estimation for inferring continuous-time dynamic gene networks. Genome Informatics. 24, 164-178 (2010).

104.   Shimamura T, Imoto S, Yamaguchi R, Nagasaki M, Miyano S. Inferring dynamic gene networks under varying conditions for transcriptomic network comparison. Bioinformatics26(8), 1064-1072 (2010).

105.   Sogawa Y, Shimizu S, Hyvarinen A, Washio T, Shimamura T, Imoto S. Discovery of exogenous variables in data with more variables than observations. Proc. 20th International Conference on Artificial Neural Networks. 67-76 (2010).

106.   Yamaguchi R, Imoto S, Miyano S. Network-based predictions and simulations by biological state space models: search for drug mode of action. J Computer Science and Technology25(1), 131-153 (2010).

107.   Araki H,* Tamada Y,* Imoto S,* Dunmore, B, Sanders D, Humphrey S, Nagasaki M, Doi A, Nakanishi Y, Yasuda K, Tomiyasu Y, Tashiro K, Print C, Charnock-Jones DS, Kuhara S, Miyano S. Analysis of PPAR alpha-dependent and PPAR alpha-independent transcript regulation following fenofibrate treatment of human endothelial cells. Angiogenesis. 12(3), 221-229 (2009). (*Equal contribution)

108.   Kojima K, Yamaguchi R, Imoto S, Yamauchi M, Nagasaki M, Yoshida R, Shimamura T, Ueno K, Higuchi T, Gotoh N, Miyano S. A state space representation of VAR models with sparse learning for dynamic gene networks. Genome Informatics22, 56-68 (2009).

109.   Niida A, Imoto S, Nagasaki M, Yamaguchi R, Miyano S. A novel meta-analysis approach of cancer transcriptomes reveals prevailing transcriptional networks in cancer cells. Genome Informatics22, 121-131 (2009).

110.   Niida A, Smith AD, Imoto S, Tsutsumi S, Aburatani H, Zhang MQ, Akiyama T. Gene set-based module discovery in the breast cancer transcriptome. BMC Bioinformatics10, 71 (2009).

111.   Shimamura T, Imoto S, Yamaguchi R, Fujita A, Nagasaki M, Miyano S. Recursive regularization for inferring gene networks from time-course gene expression profiles. BMC Systems Biology3, 41 (2009).

112.   Tamada Y,* Araki H,* Imoto S,* Nagasaki M, Doi A, Nakinishi Y, Tomiyasu Y, Yasuda K, Dunmore B, Sanders D, Humphries S, Print C, Charnock-Jones DS, Tashiro K, Kuhara S, Miyano S. Unraveling dynamic activities of autoacine pathways that control drug-response transcriptome networks. Pacific Symposium on Biocomputing14, 251-263 (2009). (*Equal contribution)

113.   Yoshikawa N, Nagasaki M, Sano M, Tokudome S, Ueno K, Shimizu N, Imoto S, Miyano S, Suematsu M, Fukuda K, Morimoto C, Tanaka H. Ligand-based gene expression profiling reveals novel roles of glucocorticoid receptor in cardiac metabolism. American J Physiology, Endocrinology and Metabolism296, E1363-E1373 (2009).

114.   Ando T, Konishi S, Imoto S. Nonlinear regression modeling via regularized radial basis function networks. J Statistical Planning and Inference. 138(11), 3616-3633 (2008).

115.   Hirose O, Yoshida R, Yamaguchi R, Imoto S, Higuchi T, Miyano S. Analyzing time course gene expression data with biological and technical replicates to estimate gene networks by state space models. Proc. 2nd Asia International Conference on Modeling & Simulation. 940-946 (2008).

116.   Hirose O, Yoshida R, Imoto S, Yamaguchi R, Higuchi T, Charnock-Jones SD, Print C, Miyano S. Statistical inference of transcriptional module-based gene networks from time course gene expression profiles by using state space models. Bioinformatics24(7), 932-942 (2008).

117.   Hatanaka Y, Nagasaki M, Yamaguchi R, Obayashi T, Numata K, Fujita A, Shimamura T, Tamada T, Imoto S, Kinoshita K, Nakai K, Miyano S. A novel strategy to search conserved transcription factor binding sites among coexpressing genes. Genome Informatics20, 212-221 (2008).

118.   Kojima K, Fujita A, Shimamura T, Imoto S, Miyano S. Estimation of nonlinear gene regulatory networks via L1 regularized NVAR from time series gene expression data. Genome Informatics. 20, 37-51 (2008).

119.   Niida A, Smith AD, Imoto S, Tsutsumi S, Aburatani H, Zhang MQ, Akiyama T. Integrative bioinformatics analysis of transcriptional regulatory programs in breast cancer cells. BMC Bioinformatics9, 404 (2008).

120.   Numata K, Imoto S, Miyano S. Partial order-based Bayesian network learning algorithm for estimating gene networks. Proc. IEEE Bioinformatics and Biomedicine. 357-360 (2008).

121.   Numata K, Yoshida R, Nagasaki M, Saito A, Imoto S, Miyano S. ExonMiner: web service for analysis of GeneChip Exon Array data. BMC Bioinformatics9, 494 (2008).

122.   Perrier E, Imoto S, Miyano S. Finding optimal Bayesian network given a super-structure. J Machine Learning Research9, 2251-2286 (2008).

123.   Watanabe Y, Yamamoto M, Miura N, Fukutake M, Ishige A, Yamaguchi R, Nagasaki M, Imoto S, Miyano S, Takeda J, Watanabe K. Orengedokuto and berberine improve indomethacin- induced small intestinal injury via adenosine. J Gastroenterology44, 380-389 (2008).

124.   Yamaguchi R, Imoto S, Yamauchi M, Nagasaki M, Yoshida R, Shimamura T, Hatanaka Y, Ueno K, Higuchi T, Gotoh N, Miyano S. Predicting differences in gene regulatory systems by state space models. Genome Informatics21, 101-113 (2008).

125.   Yoshida R, Nagasaki M, Yamaguchi R, Imoto S, Miyano S, Higuchi T. Bayesian learning of biological pathways on genomic data assimilation. Bioinformatics. 24(22), 2592-2601 (2008).

126.   Affara M, Dunmore B, Savoie CJ, Imoto S, Tamada Y, Araki H, Charnock-Jones DS, Miyano S, Print C. Understanding endothelial cell apoptosis: what can the transcriptome glycome and proteome reveal? Philosophical Transactions of Royal Society62(1484), 1469-1487 (2007).

127.   Gupta PK, Yoshida R, Imoto S, Yamaguchi R, Miyano S. Statistical absolute evaluation of gene ontology terms with gene expression data. Lecture Notes in Bioinformatics. 4463, 146-157 (2007).

128.   Hirose O, Yoshida R, Yamaguchi R, Imoto S, Higuchi T, Miyano S. Clustering with time course gene expression profiles and the mixture of state space models. Genome Informatics18, 258-266 (2007).

129.   Numata K, Imoto S, Miyano S. A structure learning algorithm for inference of gene networks from microarray gene expression data using Bayesian networks. Proc. IEEE 7th International Symposium on Bioinformatics & Bioengineering. 1280-1284 (2007).

130.   Shimamura T, Yamaguchi R, Imoto S, Miyano S. Weighted lasso in graphical Gaussian modeling for large gene network estimation based on microarray data. Genome Informatics19, 142-153 (2007).

131.   Termier A, Tamada Y, Numata K, Imoto S, Washio T, Higuchi T. DIGDAG, a first algorithm to mine closed frequent embedded sub-DAGs. Proc. 5th International Workshop on Mining and Learning with Graphs. CR-ROM (2007).

132.   Yamaguchi R, Yamamoto M, Imoto S, Nagasaki M, Yoshida R, Tsuiji K, Ishige A, Asou H, Watanabe K, Miyano S. Identification of activated transcription factors from microarray gene expression data of Kampo-medicine treated mice. Genome Informatics18, 119-129 (2007).

133.   Yamaguchi R, Yoshida R, Imoto S, Higuchi T, Miyano S. Finding module-based gene networks with state-space models - Mining high-dimensional and short time-course gene expression data. IEEE Signal Processing Magazine24(1), 37-46 (2007).

134.   Yoshida R, Numata K, Imoto S, Nagasaki M, Doi A, Ueno K, Miyano S. Computational discovery of aberrant splice variations with genome-wide exon expression profiles. Proc. IEEE 7th International Symposium on Bioinformatics & Bioengineering. 715-722 (2007).

135.   Imoto S, Higuchi T, Goto T, Miyano S. Error tolerant model for incorporating biological knowledge with expression data in estimating gene networks. Statistical Methodology. 3(1), 1-16 (2006).

136.   Imoto S,* Tamada Y,* Araki H,* Yasuda K, Print CG, Charnock-Jones SD, Sanders, D, Savoie CJ, Tashiro K, Kuhara S, Miyano S. Computational strategy for discovering druggable gene networks from genome-wide RNA expression profiles. Pacific Symposium on Biocomputing. 11, 559-571 (2006). (*Equal contribution)

137.   Imoto S, Tamada Y, Savoie CJ, Miyano S. Analysis of gene networks for drug target discovery and validation. Methods in Molecular Biology. 360, 33-56 (2006).

138.   Nagasaki M, Yamaguchi R, Yoshida R, Imoto S, Doi A, Tamada Y, Matsuno H, Miyano S, Higuchi T. Genomic data assimilation for estimating hybrid functional petri net from time-course gene expression data. Genome Informatics17(1), 46-61 (2006).

139.   Nakamichi R, Imoto S, Miyano S. Statistical model selection method to analyze combinatorial effects of SNPs and environmental factors for binary disease. International J Artificial Intelligence Tools15(5), 711-724 (2006).

140.   Termier A, Tamada Y, Imoto S, Washio T, Higuchi T. From closed tree mining towards closed DAG mining. Proc. International Workshop on Data Mining and Statistical Science. 1-7 (2006).

141.   Yoshida R, Numata K, Imoto S, Nagasaki M, Doi A, Ueno K, Miyano S. A statistical framework for genome-wide discovery of biomarker splice variations with GeneChip Human Exon 1.0 ST Arrays. Genome Informatics. 17(1), 88-99 (2006).

142.   Yoshida R, Higuchi T, Imoto S, Miyano S. ArrayCluster: an analytic tool for clustering, data visualization and module finder on gene expression profiles. Bioinformatics. 22, 1538-1539 (2006).

143.   Hirose O, Nariai N, Tamada Y, Bannai H, Imoto S, Miyano S. Estimating gene networks from expression data and binding location data via Boolean networks. Lecture Notes in Computer Science. 3482, 349-356 (2005).

144.   Nariai N, Tamada Y, Imoto S, Miyano S. Estimating gene regulatory networks and protein-protein interactions of Saccharomyces cerevisiae from multiple genome-wide data. Bioinformatics21, Suppl.2, ii206-ii212 (2005).

145.   Tamada Y, Bannai H, Imoto S, Katayama T, Kanehisa M, Miyano S. Utilizing evolutionary information and gene expression data for estimating gene regulations with Bayesian network models. J Bioinformatics and Computational Biology3(6), 1295-1313 (2005).

146.   Tamada Y, Imoto S, Tashiro K, Kuhara S, Miyano S. Identifying drug active pathways from gene networks estimated by gene expression data. Genome Informatics. 16(1), 182-191 (2005).

147.   Yoshida R, Imoto S, Higuchi T. Estimating time-dependent gene networks from time series microarray data by dynamic linear models with Markov switching. Proc. 4th Computational Systems Bioinformatics. 289-298 (2005).

148.   Yoshida R, Imoto S, Higuchi T. A penalized likelihood estimation on transcriptional module-based clustering. Lecture Notes in Computer Science3482, 389-401 (2005).

149.   Ando T, Imoto S, Konishi S. Adaptive learning machines for nonlinear classification and Bayesian information criterion. Bulletin of Informatics and Cybernetics. 36, 147-162 (2004).

150.   Ando T, Imoto S, Miyano S. Kernel mixture survival models for identifying cancer subtypes, predicting patient's cancer types and survival probabilities. Genome Informatics15(2), 201-210 (2004).

151.   Ando T, Imoto S, Miyano S. Functional data analysis of the dynamics of gene regulatory networks. Lecture Notes in Computer Science3303, 69-83 (2004).

152.   Araki Y, Konishi S, Imoto S. Functional discriminant analysis for time-seriese gene expression data via radial basis function expansion. COMPSTAT, 613-620 (2004).

153.   De Hoon MJL, Imoto S, Kobayashi K, Ogasawara N, Miyano S. Predicting the operon structure of Bacillus subtilis using operon length, intergene distance, and gene expression information. Pacific Symposium on Biocomputing9, 276-287 (2004).

154.   De Hoon MJL, Makita Y, Imoto S, Kobayashi K, Ogasawara N, Nakai K, Miyano S. Predicting gene regulation by sigma factors in Bacillus subtilis from genome-wide data. Bioinformatics20 Suppl.1, i101-i108 (2004).

155.   De Hoon MJL, Imoto S, Nolan J, Miyano S. Open source clustering software. Bioinformatics. 20(9), 1453-1454 (2004).

156.   Imoto S, Higuchi T, Goto T, Tashiro K, Kuhara S, Miyano S. Combining microarrays and biological knowledge for estimating gene networks via Bayesian networks. J Bioinformatics and Computational Biology2(1), 77-98 (2004).

157.   Imoto S, Higuchi T, Kim S, Jeong E, Miyano S. Residual bootstrapping and median filtering for robust estimation of gene networks from microarray data.  Lecture Notes in Bioinformatics. 3082, 149-160 (2004).

158.   Kim S, Imoto S, Miyano S. Dynamic Bayesian network and nonparametric regression for nonlinear modeling of gene networks from time series gene expression data. BioSystems75(1-3), 57-65 (2004).

159.   Konishi S, Ando T, Imoto S. Bayesian information criteria and smoothing parameter selection in radial basis function networks. Biometrika. 91(1), 27-43 (2004).

160.   Nakamichi R, Imoto S, Miyano S. Case-control study of binary trait considering interactions between SNPs and environmental effects using logistic regression. Proc. 4th IEEE Bioinformatics and Bioengineering. 73-78 (2004).

161.   Nariai N, Kim S, Imoto S, Miyano S. Using protein-protein interactions for refining gene networks estimated from microarray data by Bayesian networks. Pacific Symposium on Biocomputing. 9, 336-347 (2004).

162.   Ott S, Imoto S, Miyano S. Finding optimal models for small gene networks. Pacific Symposium on Biocomputing9, 557-567 (2004).

163.   Yoshida R, Higuchi T, Imoto S. A mixed factors model for dimension reduction and extraction of a group structure in gene expression data. Proc. 3rd Computational Systems Bioinformatics. 161-172 (2004).

164.   De Hoon MJL, Imoto S, Kobayashi K, Ogasawara N, Miyano S. Inferring gene regulatory networks from time-ordered gene expression data of Bacillus subtilis using differential equations. Pacific Symposium on Biocomputing8, 17-28 (2003).

165.   De Hoon MJL, Ott S, Imoto S, Miyano S. Validation of noisy dynamical system models of gene regulation inferred from time-course gene expression data at arbitrary time intervals. Proc. 2nd European Conference on Computational Biology. 26-28 (2003).

166.   Imoto S, Higuchi T, Goto T, Tashiro K, Kuhara S, Miyano S. Combining microarrays and biological knowledge for estimating gene networks via Bayesian networks. Proc. 2nd Computational Systems Bioinformatics. 104-113 (2003).

167.   Imoto S, Kim S, Goto T, Aburatani S, Tashiro K, Kuhara S, Miyano S. Bayesian network and nonparametric heteroscedastic regression for nonlinear modeling of genetic network. J Bioinformatics and Computational Biology1(2), 231-252 (2003).

168.   Imoto S, Konishi S. Selection of smoothing parameters in B-spline nonparametric regression models using information criteria. Annals of the Institute of Statistical Mathematics55(4), 671-687 (2003).

169.   Imoto S, Savoie CJ, Aburatani S, Kim S, Tashiro K, Kuhara S, Miyano S. Use of gene networks for identifying and validating drug targets. J Bioinformatics and Computational Biology1(3), 459-474 (2003).

170.   Kim S, Imoto S, Miyano S. Dynamic Bayesian network and nonparametric regression for nonlinear modeling of gene networks from time series gene expression data. Lecture Notes in Computer Science2602, 104-113 (2003).

171.   Savoie CJ, Aburatani S, Watanabe S, Eguchi Y, Muta S, Imoto S, Miyano S, Kuhara S, Tashiro K. Use of gene networks from full genome microarray libraries to identify functionally relevant drug-affected genes and gene regulation cascades. DNA Research10, 19-25 (2003).

172.   Tamada Y, Kim S, Bannai H, Imoto S, Tashiro K, Kuhara S, Miyano S. Estimating gene networks from gene expression data by combining Bayesian network model with promoter element detection. Bioinformatics. 19 Suppl.2, ii227-ii236 (2003).

173.   De Hoon MJL, Imoto S, Miyano S. Inferring gene regulatory networks from time-ordered gene expression data using differential equations. Lecture Notes in Artificial Intelligence2534, 267-274 (2002).

174.   De Hoon MJL, Imoto S, Miyano S. Statistical analysis of a small set of time-ordered gene expression data using linear splines. Bioinformatics18, 1477-1485 (2002).

175.   Imoto S, Kim S, Goto T, Aburatani S, Tashiro K, Kuhara S, Miyano S. Bayesian network and nonparametric heteroscedastic regression for nonlinear modeling of genetic network. Proc. 1st IEEE Computer Society Bioinformatics Conference. 219-227 (2002).

176.   Imoto S, Goto T, Miyano S. Estimation of genetic networks and functional structures between genes by using Bayesian network and nonparametric regression. Pacific Symposium on Biocomputing. 7, 175-186 (2002).

欧文国際雑誌に掲載した総説

    1.        Miyano S, Yamaguchi R, Tamada Y, Nagasaki M, Imoto S. Gene networks viewed through two models. Lecture Notes in Bioinformatics4652, 54-66 (2009).

    2.        Kim S, Imoto S, Miyano S. Inferring gene networks from time series microarray data using dynamic Bayesian networks. Briefings in Bioinformatics. 4(3), 228-235 (2003).

欧文著書

    1.        Yamaguchi R, Imoto S, Miyano S. A TCR Sequence Data Analysis Pipeline: Tcrip, Y. Nakamura (Ed.), Immunopharmacogenomics, Springer Japan, in press.

    2.        Imoto S, Matsuno H, Miyano S. Gene Networks: Estimation, Modeling and Simulation. In R. Eils and A. Kriete (Eds.), Computational Systems Biology, 2nd Edition, Academic Press, pp. 89-112 (2014).

    3.        Imoto S, Tamada Y, Araki H, Miyano S. Computational Drug Target Pathway Discovery: A Bayesian Network Approach. In H. Lu, B. Schokop, H. Zhao (Eds.), Handbook of Computational Statistics: Statistical Bioinformatics, Springer-Verlag. pp. 501-532 (2010).

    4.        Imoto S, Miyano S. Bayesian Network Approach to Estimate Gene Networks. In A. Mittal, A. Kassim, T. Tan (Eds.), Bayesian Network Technologies: Applications and Graphical Models, Idea Group Publishers, USA. pp. 269-299 (2007).

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