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HOME ＞ Kyoto Group ＞ Wataru Fujibuchi, Ph.D.

Wataru Fujibuchi, Ph.D.

fujibuchi

Current Job:
Professor, Theoretical Cell Science Laboratory
Dept. of Life Science Frontiers,
Center for iPS Cell Research and Application (CiRA),
Kyoto University
53 Kawahara-cho, Shogoin, Sakyo-ku, Kyoto 606-8507, JAPAN
Email: fujibuchi-g(a)cira.kyoto-u.ac.jp

Research Interests:
Stem cell informatics, Machine learning, Systems biology, Sequence analysis, Gene expression analysis, Genetic networks, Integrative analysis of cell functions

Education and Job Career
2012.4- Professor, Center for iPS Cell Research and Application, Kyoto University
2012.4-2014.3 Invited Senior Researcher, CBRC
2007.4-2012.3 Team Leader, CBRC
2003.10-2007.3 Research Scientist, CBRC
2003.6-2003.9 AIST Research Staff, CBRC
2002.7-2003.5 Staff Scientist, NCBI, NIH, U.S.A.
1999.4-2002.7 Visiting Fellow, National Center for Biotechnology Information (NCBI), NIH, U.S.A.
1995.7-1999.3 Research Associate, Institute for Chemical Research, Kyoto Univ.
1998.5 Ph.D. Biophysics, Kyoto Univ.
1993.3 M.S. Biophysics, Kyoto Univ.
1991.3 B.S. Zoology, Hiroshima Univ.

Publications
【Reviewed Publications】 *...Corresponding

  1. Methylated Site Display (MSD)-AFLP, a sensitive and affordable method for analysis of CpG methylation profiles. Aiba T, Saito T, Hayashi A, Sato S, Yunokawa H, Maruyama T, Fujibuchi W, Kurita H, Tohyama C, Ohsako S. BMC Molecular Biology 18(7), 2017.
  2. Development of Enhanced Reduced Representation Bisulfite Sequencing Method for Single-cell Methylome Analysis. Yamane J, Mori T, Taniyama N, Kobayashi K, *Fujibuchi W. Genomics and Computational Biology 3(2):e49, 2017.
  3. Developing Global Cellular Information Retrieval System with Minimum Reporting Guidelines on Cellular Data for Regenerative Medicine. Sakurai K, *Fujibuchi W. Genomics and Computational Biology 3(2):e50, 2017.
  4. Development of 3D Tissue Reconstruction Method from Single-cell RNA-seq Data. Mori T, Yamane J, Kobayashi K, Taniyama N, Tano T, *Fujibuchi W. Genomics and Computational Biology 3(1):e53, 2017.
  5. First Proposal of Minimum Information About a Cellular Assay for Regenerative Medicine. Sakurai K, Kurtz A, Stacey G, Sheldon M, and *Fujibuchi W. Stem Cells Transl. Med. 5(10):1345-1361, 2016.
  6. Prediction of developmental chemical toxicity based on gene networks of human embryonic stem cells. Yamane J, Aburatani S, Imanishi S, Akanuma H, Nagano R, Kato T, Sone H, Ohsako S and *Fujibuchi W. Nucleic Acids Res. 44(12):5515-5528, 2016.
  7. SERPINI1 regulates epithelial-mesenchymal transition in an orthotopic implantation model of colorectal cancer. Matsuda Y, Miura K, Yamane J, Shima H, Fujibuchi W, Ishida K, Fujishima F, Ohnuma S, Sasaki H, Nagao M, Tanaka N, Satoh K, Naitoh T, Unno M. Cancer Sci. 107(5):619-628, 2016.
  8. Oil Accumulation by the Oleaginous Diatom Fistulifera solaris as Revealed by the Genome and Transcriptome. Tanaka T, Maeda Y, Veluchamy A, Tanaka M, Abida H, Maréchal E, Bowler C, Muto M, Sunaga Y, Tanaka M, Yoshino T, Taniguchi T, Fukuda Y, Nemoto M, Matsumoto M, Wong PS, Aburatani S, Fujibuchi W. Plant Cell. 27(1):162-176, 2015.
  9. A set of external reference controls/probes that enable quality assurance between different microarray platforms. Akiyama H, Ueda Y, Nobumasa H, Ooshima H, Ishizawa Y, Kitahiro K, Miyagawa I, Watanabe K, Nakamura T, Tanaka R, Yamamoto N, Nakae H, Kawase M, Gemma N, Sekiguchi Y, Fujibuchi W, Matoba R. Anal Biochem. 472:75-83, 2015.
  10. Tracking difference in gene expression in a time-course experiment using gene set enrichment analysis. Wong PS, Tanaka M, Sunaga Y, Tanaka M, Taniguchi T, Yoshino T, Tanaka T, Fujibuchi W, Aburatani S., PLoS One. 9(9):e107629, 2014.
  11. Standardization of iPS cells by single-cell transcriptome analysis. Yamane J, Maruyama T, *Fujibuchi W. SEITAI NO KAGAKU 65(2):154-158 (2014)
  12. Commensal microbiota contributes to chronic endocarditis in TAX1BP1 deficient mice. Nakano S, Ikebe E,Tsukamoto Y, Wong Y, Matsumoto T, Mitsui T, Yahiro T, Inoue K, Itoh K, Tanaka Y, Moriyama M, Yokoyama S, Hasegawa H, Jeang K-T, Hori M, Ono K, Kubota K, Fujibuchi W, Iha H, Nishizono A. PLOS ONE 8(9):e73205 (2013).
  13. The impact of collapsing data on microarray analysis and DILI prediction. J.F. Pessiot, P.S. Wong, Maruyama T, Morioka R, Aburatani S, Tanaka M, *Fujibuchi W. Landis Bioscience. 1(3):1-7 (2013).
  14. Inference of Gene Regulatory Networks to Detect Toxicity-Specific Effects in Human Embryonic Stem Cells. Aburatani S, Fujibuchi W, Yamane J, Imanishi S, Nagano R, Sone H, Ohsako S. International Journal On Advances in Life Sciences. 5(1&2):103-114 (2013).
  15. Infiltration of CD40-Positive Tumor-Associated Macrophages Indicates a Favorable Prognosis in Colorectal Cancer Patients. Kinouchi M, Miura K, Mizoi T, Ishida K, Fujibuchi W, Sasaki H, Ohnuma S, Saito K, Katayose Y, Naitoh T, Motoi F, Shiiba KI, Eigawa S, Shibata C, Unno M. Hepatogastroenterology. 60(121):83-88 (2013).
  16. Pairwise Ranking Component Analysis. J.F. Pessiot, Kim H, Fujibuchi W. Knowledge and Information Systems. 36(2): 459-487 (2013).
  17. Application of Structural Equation Modeling for Inferring Toxicity-Dependent Regulation in Human Embryonic Stem Cells. Aburatani S, Fujibuchi W. GLOBAL HEALTH 2012, The First International Conference on Global Health Challenges. Venice, Italy. (2012). [Best Paper Award]
  18. Inference of Specific Gene Regulation by Environmental Chemicals in Human Embryonic Stem Cells. Aburatani S, Fujibuchi W. Journal of Molecular Biology Research. 2(1): 54-64 (2012).
  19. Review: Splice isoforms as therapeutic targets of colorectal cancer. Miura K, Fujibuchi W, Unno M. Carcinogenesis. 33(12): 2311-2319 (2012).
  20. Review: Splice variants in apoptotic pathway. Miura K, Fujibuchi W, Unno M. Experimental Oncology. 34(3): 212-217 (2012).
  21. Effects of methylmercury exposure on neuronal differentiation of mouse and human embryonic stem cells. He X, Imanishi S, Sone H, Nagano R, Oin XY, Yoshinaga J, Akanuma H, Yamane J, Fujibuchi W, Ohsako S. Toxicol Lett. 212(1): 1-10 (2012).
  22. Review: Differentiating rectal carcinoma by an immunohistological analysis of carcinomas of pelvic organs based on the NCBI literature survey and the Human Protein Atlas database. Miura K, Ishida K, Fujibuchi W, Ito A, Niikura H, Ogawa H, Sasaki I. Surg Today. 42(6): 515-525 (2012).
  23. MiR-126 acts as a tumor suppressor in pancreatic cancer cells via the regulation of ADAM9. Hamada S, Satoh K, Fujibuchi W, Hirota M, Kanno A, Unno J, Masamune A, Kikuta K, Kume K, Shimosegawa T. Mol Cancer Research. 10(1): 3-10 (2012).
  24. CELLPEDIA: a repository for human cell information for cell studies and differentiation analyses. Hatano A, Chiba H, Moesa M.A, Taniguchi T, Nagaie S, Yamanegi K, Takai T, Tanaka H, *Fujibuchi W, Database 2011, bar046 (2011).
  25. Prediction of Chemical Toxicity by Network-based SVM on ES-cell Validation System. *Fujibuchi W, Aburatani S, Yamane J, Imanishi S, Akanuma H, Sone H, Ohsako S. The Proceedings of the 2011 Joint Conference of CBI-Society and JSBi, Kobe (2011).
  26. Infiltration of CD14-positive macrophages at the invasive front indicates a favorable prognosis in colorectal cancer patients with lymph node metastasis. Kinouchi M, Miura K, Mizoi T, Ishida K, Fujibuchi W, Ando T, Yazaki N, Saito K, Shiiba K, Sasaki I. Hepatogastroenterology. 58(106):352-8 (2011).
  27. Structural elements of the signal propagation pathway in squid rhodopsin and bovine rhodopsin. Sugihara M, Fujibuchi W, Suwa M. J Phys Chem B. 115(19):6172-9 (2011).
  28. Inhibitor of apoptosis protein family as diagnostic markers and therapeutic targets of colorectal cancer. Miura K, Fujibuchi W, Ishida K, Naitoh T, Ogawa H, Ando T, Yazaki N, Watanabe K, Haneda S, Shibata C, Sasaki I. Surg Today. 41(2):175-82 (2011).
  29. Review: Alternative pre-mRNA splicing in digestive tract malignancy. Miura K, Fujibuchi W, Sasaki I. Cancer Sci. 102(2):309-16 (2011).
  30. In search of true reads: A classification approach to next generation sequencing data. E. Wijaya, J.F. Pessiot, M.C. Frith, W. Fujibuchi, K. Asai & P. Horton, Intl. Conference on Bioinformatics and Biomedicine Workshops, 37:561-6 (2011).
  31. PeakRegressor identifies composite sequence motifs responsible for STAT1 binding sites and their potential rSNPs. Pessiot J.F, Chiba H, Hyakkoku H, Taniguchi T, *Fujibuchi W. PLoS ONE 5(8):e11881 (2010).
  32. Sensitive and convenient yeast reporter assay for high-throughput analysis by using a secretory luciferase from Cypridina noctiluca. Tochigi Y, Sato N, Sahara T, Wu C, Saito S, Irie T, Fujibuchi W, Goda T, Yamaji R, Ogawa M, Ohmiya Y, Ohgiya S. Analytical Chemistry 82(13):5768-76 (2010).
  33. Profiles of Chemical Effects on Cells (pCEC): a toxicogenomics database with a toxicoinformatics system for risk evaluation and toxicity prediction of environmental chemicals. Sone H, Okura M, Zaha H, Fujibuchi W, Taniguchi T, Akanuma H, Nagano R, Ohsako S, Yonemoto J. J Toxicol Sci. 35(1):115-23 (2010).
  34. PeakRegressor identifies composite sequence motifs responsible for STAT1 binding sites and their potential rSNPs. Pessiot J.F, Chiba H, Hyakkoku H, Taniguchi T, Wijibuchi W. Critical Assessment of Massive Data Analysis 2009, 4-11, Chicago, (2009).
  35. Designing Pyro-Primer Sequences Using a Simulated Annealing Algorithm, to Critically Target mRNAs in Quantitative Cell Analysis. *Fujibuchi W, Chiba H, Akiyama H,　Shiku H. Proceedings of the 6'th International Forum on Post-genome Technologies :253-7, Beijing (2009).
  36. Expression of the calcium-binding protein S100P is regulated by bone morphogenetic protein in pancreatic duct epithelial cell lines. Hamada S, Satoh K, Hirota M, Fujibuchi W, Kanno A, Umino J, Ito H, Satoh A, Kikuta K, Kume K, Masamune A, Shimosegawa T. Cancer Sci. 100(1):103-10 (2009).
  37. Down-regulation of cIAP2 enhances 5-FU sensitivity through the apoptotic pathway in human colon cancer cells. Karasawa H, Miura K, Fujibuchi W, Ishida K, Kaneko N, Kinouchi M, Okabe M, Ando T, Murata Y, Sasaki H, Takami K, Yamamura A, Shibata C, Sasaki I. Cancer Sci. 100(5):903-13 (2009).
  38. Cancer-associated splicing variants of the CDCA1 and MSMB genes expressed in cancer cell lines and surgically resected gastric cancer tissues. Ohnuma S, Miura K, Horii A, Fujibuchi W, Kaneko N, Gotoh O, Nagasaki H, Mizoi T, Tsukamoto N, Kobayashi T, Kinouchi M, Okabe M, Sasaki H, Shiiba K, Miyagawa K, Sasaki I., Surgery 145(1):57-68 (2009).
  39. Up-regulation of MSX2 enhances the malignant phenotype and associates with Twist 1 expression in human pancreatic cancer cells, Sato, K., Hamada, S., Kimura, K., Kanno, A., Hirota, M., Fujibuchi, W., Tanaka, N., Miura, K., Masamune, A., Vonderhaar, B.K. and Shimosegawa, T., American Journal of Pathology 172(4):926-939, 2008.
  40. An orthotopic implantation mice model and cDNA microarray analysis demonstrates several genes potentially involved in the lymph node metastasis of colorectal cancer. Sasaki H, Miura K, Horii A, Kaneko N, Fujibuchi W, Kiseleva L, Gu Z, Murata Y, Karasawa H, Mizoi T, Kobayashi T, Kinouchi M, Ohnuma S, Yazaki N, Unno M, Sasaki I. Cancer Science 99(4):711-9 (2008).
  41. An upper bound on the hardness of exact matrix based motif discovery. Horton P, Fujibuchi W. Journal of Discrete Algorithms 5(4): 706-713 (2007).
  42. Classification of Heterogeneous Microarray Data by Maximum Entropy Kernel. *Fujibuchi W. Kato T. BMC Bioinformatics 8(267) (2007).
  43. CellMontage: Similar Expression Profile Search Server. Fujibuchi W, Kiseleva L, Taniguchi T, Harada H, Horton P. Bioinformatics 23(22):3103-3104 (2007).
  44. A biclustering method for gene expression module discovery using closed itemset enumeration algorithm. Okada Y, Fujibuchi W, Horton P. IPSJ Transactions on Bioinformatics 48(SIG 5(TBIO2)):39-48 (2007).
  45. Exhaustive search of maximal biclusters in gene expression data. Okada Y, Fujibuchi W, Horton P. IAENG,2-1:307-312 (2007).
  46. Exhaustive Search Method of Gene Expression Modules and Its Application to Human Tissue Data. Okada Y, Okubo K, Horton P, *Fujibuchi W. IAENG International Journal of Computer Science 34(1), 119-126 (2007).
  47. Mining a Large-scale Microarray Database for Similar Gene Expression Modules to Find Distant Relationships between Down Syndrome and Huntington's Disease. Okada Y, *Fujibuchi W. Proceedings of Critical Assessment of Microarray Data Analysis 07, Valencia, Spain (2007).
  48. Network-based de-noising improves prediction from microarray data. Kato T, Murata Y, Miura K, Asai K, Horton P, Tsuda K, *Fujibuchi W. BMC Bioinformatics 7(Suppl. 1):S4 (2006).
  49. Comparative genomic analysis of transcription regulation elements involved in the human MAP-kinase G-protein coupling pathway. Polouliakh N, Natsume T, Harada H, *Fujibuchi W, Horton P. Journal of Bioinformatics and Computational Biology 4:469-482 (2006).
  50. Learning Kernels from Distance Constraints. Kato T, Fujibuchi W, Asai K. IPSJ 47(SIG10):1-11 (2006).
  51. RaPiDS: An Algorithm for Rapid Expression Profile Database Search. Horton P, Kiseleva L, Fujibuchi W. Genome Informatics 17(2):67-76 (2006).
  52. Kernels for Noisy Microarray Data. Tsuyoshi Kato, Wataru Fujibuchi, Kiyoshi Asai. CBRC Technical Report AIST-02-J00001-8 (2006).
  53. Drug-response prediction from microarray using network-based de-noising. Tsuyoshi Kato, Yukio Murata, Koh Miura, Kiyoshi Asai, Paul B. Horton, Koji Tsuda, Wataru Fujibuchi. IPSJ SIG technical reports 2006:47-52 BIO-5-(8) (2006).
  54. Kernel construction from distance constraints. Tsuyoshi Kato, Wataru Fujibuchi, Kiyoshi Asai. CBRC Technical Report AIST02-J00001-7 (2005).
  55. NCBI GEO: Mining millions of expression profiles - database and tools. Tanya Barrett, Tugba Suzek, Dennis Troup, Stephen Wilhite, Wing-Chi Ngau, Pierre Ledoux, Dmitry Rudnev, Alex Lash, Fujibuchi, W., Ron Edgar, Nucleic Acids Res. 33:D562-566 (2005).
  56. Automatic gene collection system for genome-scale overview of G-protein coupled receptors in eukaryotes. Ono Y, Fujibuchi W, Suwa, M. GENE 364:53-62 (2005).
  57. An Upper Bound on the Hardness of Exact Matrix Based Motif Discovery. Horton P, Fujibuchi W. Combinatorial Pattern Matching,16th Annual Symposium. 219-228 (2005).
  58. PROSPECT improves cis-acting regulatory element prediction by integrating expression profile data with consensus pattern searches. Fujibuchi W, John S, Anderson and David Landsman. Nucleic Acids Res. 29(19):3988-96 (2001).
  59. Automatic detection of conserved gene clusters in multiple genomes by graph comparison and P-quasi grouping. Fujibuchi W, Ogata H, Matsuda H, Kanehisa M. Nucleic Acids Res. 28(20):4029-36 (2000).
  60. A heuristic graph comparison algorithm and its application to detect functionally related enzyme clusters. Ogata H, Fujibuchi W, Goto S, Kanehisa, M. Nucleic Acids Res. 28(20):4021-28 (2000).
  61. KEGG: Kyoto Encyclopedia of Genes and Genomes. Ogata H, Goto S, Sato K, Fujibuchi W, Bono H, Kanehisa M. Nucleic Acids Res. 27(1):29-34 (1999).
  62. Computation with the KEGG pathway database. Ogata H, Goto S, Fujibuchi W, Kanehisa M. BioSystems 47(1-2):119-128 (1998).
  63. Systematic prediction of orthologous units of genes in the complete genomes. Bono H, Goto S, Fujibuchi W, Ogata H, Kanehisa M. Genome Informatics 9:32-40 (1998).
  64. KEGG and DBGET/LinkDB: Integration of biological relationships in divergenet molecular biology data. Fujibuchi W, Sato K, Ogata H, Goto S, Kanehisa, M. Knowledge Sharing Across Biological and Medical Knowledge Based Systems. Technical Report WS-98-04:35-40 (1998).
  65. Prediction of gene expression specificity by promoter sequence patterns. Fujibuchi W, Kanehisa M. DNA Res., 4(2):81-90 (1997).
  66. DBGET/LinkDB: an Integrated Database Retrieval System. Fujibuchi W, Goto S, Migimatsu H, Uchiyama I, Ogiwara J, Akiyama Y, Kanehisa M. Pacific Symposium on Biocomputing '98, 683-694 (1997).
  67. The size differences among mammalian introns are due to the accumulation of small deletions. Ogata H, Fujibuchi W, Kanehisa M. FEBS Lett. 390(1):99-103 (1996).
  68. Analysis of binary relations and hierarchies of enzymes in the metabolic pathways. Ogata H, Bono H, Fujibuchi W, Goto S, Kanehisa M. Genome Informatics 7 :128-136 (1996).
  69. Organizing and computing metabolic pathway data in terms of binary relations. Goto S, Bono H, Ogata H, Fujibuchi W, Nishioka T, Sato K, Kanehisa M. Pacific Symposium on Biocomputing '97: 175-186 (1996).
  70. A method to extract functional motifs for transcriptional regulation in eukaryotic sequences. Fujibuchi W, Kanehisa M. Bull. Inst. Chem. Res., Kyoto Univ. 71:317-26 (1993).
【Books】
  1. 森智弥、藤渕航「精密細胞分類に基づく幹細胞・分化細胞の評価」実験医学増刊34(17):174-180、羊土社2016.
  2. 桜井都衣、藤渕航「iPS細胞と元素周期表の密接な関係」海洋化学研究29(1):17-23、海洋化学研究所2016.
  3. 加藤有己、桜井都衣、藤渕航「ヒト細胞からのビッグデータの情報管理と情報解析技術」ビッグデータの収集、調査、分析と活用事例, pp.249-254, 技術情報協会2014.
  4. 植田充美／監　藤渕航、他「iPS細胞からのビッグデータの情報セキュリティと創薬、医療への活用」生命のビッグデータ利用の最前線, pp.176-184、シーエムシー出版 2014.
  5. 秋山徹／監　藤渕航、井元清哉、河府和義／編「バイオ実験に絶対使える　統計の基本　Q&A」羊土社2012.
  6. 千葉啓和、藤渕航「細胞情報解析に役立つツール―幹細胞研究の進展とその創薬応用に向けて」実験医学28(19): 3171-3174、羊土社2010.
  7. Kato, T., and Fujibuchi, W., "Kernel classification methods for cancer microarray data", Medical Biostatistics for Complex Diseases, WILEY-VCH, Weinheim, Germany 2010.
  8. 藤渕航「シミュレーテッドアニーリングによる多重プライマー配列デザイン法」、シングルセル解析の最前線、シーエムシー出版 2010.
  9. *Fujiubchi, W., Kim, H., Okada, Y., Taniguchi, T., and Sone, H., "High-performance gene expression module analysis tool and its application to chemical toxicity data", Methods in Molecular Biology, vol. 577: 55-65, Humana Press Inc., U.S.A. 2009.
  10. 「マイクロアレイ データ統合解析プロトコール」藤渕 航／堀本 勝久（編）, 羊土社（2008）.
  11. 「GSEAによるマイクロアレイデータからの遺伝子機能セットの探索法」, 藤渕 航, 岡田 吉史, 実験医学、羊土社, 25-26, 2007.
  12. 「バイオデータベースとウェブツールの手取り足取り利用法」「GEO」,岡田 吉史, 藤渕 航,羊土社, 150-156, 2007.
  13. 「極大２部クリーク列挙法による遺伝子発現モジュールの抽出」, 岡田 吉史, 藤渕 航, Horton Paul, IPSJ SIG Technical Report,2006-BIO-6, 17-23, 2006.
  14. 「細胞の知識ベース開発と遺伝子発現プロファイルによる細胞種と特徴予測」, 藤渕 航, Larisa Kiseleva, 谷口丈晃, Paul Horton, IPSJ SIG Technical Report, 2005-BIO-2, 33-37, 2005.
  15. 「タンパク質分析（研究）超基本Q＆A,Q94 タンパク質の相互作用をインターネットで調べたい」,藤渕 航,羊土社, 2005.
  16. 「ゲノム研究実験ハンドブック,第２章生物学データベース ７．遺伝子発現プロファイルデータベースGEOデータベース」, Polouliakh Natalia, 藤渕 航,羊土社, 2004.
  17. 「遺伝子クラスター解析」藤渕 航, 金久 實, 数理科学別冊, 2001.
  18. 「遺伝子クラスター解析」藤渕 航, 金久 實, 数理科学、432, 12-18, 1999.
  19. 「ゲノムネットのデータベース利用法（第２版）」第二章, 藤渕 航, 共立出版、1998.
  20. 「遺伝子・ゲノム百科辞典」藤渕 航, 金久 實, 遺伝子医学、1(2), 119-124, 1997.
  21. 「ゲノムネットのデータベース利用法」第一章, 第三章, 藤渕 航, 共立出版、1996.
  22. 「インターネットを利用した遺伝情報検索システム最前線」中井謙太, 藤渕 航, 薬学図書館, 41(4), 359-364, 1996.
【Patents】
  1. DNAメチル化解析のための試料調整方法, 藤渕　航(50)、山根　順子(50),特願：2015-242932、京都大学、2015.12.14.
  2. 細胞画像判定装置、方法、並びにプログラム, 藤渕 航(30)、千葉　啓和(40)、富永大介(30), 特願2012-071376, 産業技術総合研究所, 2012.3.27
  3. 核酸標準物質検出用プローブの設計方法、核酸標準物質検出用プローブ及び当該核酸標準物質検出用プローブを有する該酸検出系, 藤渕 航(40)、関口 勇地(20)、秋山英雄(30)（JMAC）、的場亮(10)（JMAC）特願2010-113343、産業技術総合研究所、バイオチップコンソーシアム2010.05.17
  4. 「プライマーセット探索装置、方法およびプログラム」, 藤渕　航(50)、千葉　啓和(50), 特願2009-212703, 2009.9.15
  5. 遺伝子発現モジュール探索装置、遺伝子発現モジュール探索方法及び遺伝子発現モジュール探索プログラム、岡田吉史(50)、藤渕航(50), 特願2007-320636、産業技術総合研究所2007.12.12
  6. 細胞輪郭抽出装置、細胞輪郭抽出方法およびプログラム，藤渕 航(100)，特願2006-331416、産業技術総合研究所、2006.12.08
  7. 遺伝子発現プロファイル比較装置，Horton Paul(75)、藤渕 航(25)，特願2005-236198、産業技術総合研究所、2005.08.17
  8. 生物学的情報処理装置、生物学的情報処理方法および生物学的情報処理プログラム，加藤 毅(70)、藤渕 航(30)，特願2005-235562、産業技術総合研究所、2005.08.15
  9. 遺伝子発現プロファイル検索装置、遺伝子発現プロファイル検索方法およびプログラム，藤渕 航(75)、Horton Paul(25)，特願2004-280257、産業技術総合研究所、2004.09.27
【International Patents】
  1. ＤＮＡメチル化解析のための試料調製方法,藤渕航(50)、山根順子(50),特願：PCT/JP2016/087100、京都大学、2016.12.13.
  2. 核酸標準物質検出用プローブの設計方法、核酸標準物質検出用プローブ及び当該核酸標準物質検出用プローブを有する該酸検出系, 藤渕 航(40)、関口 勇地(20)、秋山英雄(30)（JMAC）、的場亮(10)（JMAC）PCT/JP2011/061310、産業技術総合研究所、バイオチップコンソーシアム2011.05.17
  3. 遺伝子発現プロファイル検索装置、遺伝子発現プロファイル検索方法およびプログラム，藤渕 航(75)、Horton Paul(25)，11/235150（米国）、産業技術総合研究所2005.09.27

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