Reiji Teramoto

Summary

Affiliation: NEC Corporation
Country: Japan

Publications

  1. doi Prediction of Alzheimer's diagnosis using semi-supervised distance metric learning with label propagation
    Reiji Teramoto
    Bio IT Center, NEC Corporation, Tsukuba, Ibaraki, Japan
    Comput Biol Chem 32:438-41. 2008
  2. doi Protein expression profile characteristic to hepatocellular carcinoma revealed by 2D-DIGE with supervised learning
    Reiji Teramoto
    Bio IT Center, NEC Corporation, 34, Miyukigaoka, Tsukuba, Ibaraki 305 8501, Japan
    Biochim Biophys Acta 1784:764-72. 2008
  3. doi Prediction of protein-ligand binding affinities using multiple instance learning
    Reiji Teramoto
    Advanced Technology Solutions Division, NEC Informatec Systems, Ltd, 2 6 1, Kitamigata, Takatsu ku, Kawasaki, Kanagawa 213 8511, Japan
    J Mol Graph Model 29:492-7. 2010
  4. ncbi Supervised consensus scoring for docking and virtual screening
    Reiji Teramoto
    Fundamental and Environmental Research Laboratories, NEC Corporation, 34 Miyukigaoka, Tsukuba, Ibaraki 305 8501, Japan
    J Chem Inf Model 47:526-34. 2007
  5. ncbi Supervised scoring models with docked ligand conformations for structure-based virtual screening
    Reiji Teramoto
    Fundamental and Environmental Research Laboratories, NEC Corporation, 34, Miyukigaoka, Tsukuba, Ibaraki 305 8501, Japan
    J Chem Inf Model 47:1858-67. 2007
  6. doi Structure-based virtual screening with supervised consensus scoring: evaluation of pose prediction and enrichment factors
    Reiji Teramoto
    Bio IT Center and Nano Electronics Research Laboratories, NEC Corporation, 34, Miyukigaoka, Tsukuba, Ibaraki 305 8501, Japan
    J Chem Inf Model 48:747-54. 2008
  7. doi Consensus scoring with feature selection for structure-based virtual screening
    Reiji Teramoto
    Bio IT Center, NEC Corporation, 34, Miyukigaoka, Tsukuba, Ibaraki 305 8501, Japan
    J Chem Inf Model 48:288-95. 2008
  8. ncbi Comparative proteomic and transcriptomic profiling of the human hepatocellular carcinoma
    Hirotaka Minagawa
    Nano Electronics Research Laboratories, NEC Corporation, 34, Miyukigaoka, Tsukuba, Ibaraki 305 8501, Japan
    Biochem Biophys Res Commun 366:186-92. 2008
  9. doi Bootstrap-based consensus scoring method for protein-ligand docking
    Hiroaki Fukunishi
    Nano Electronics Research Laboratories, NEC Corporation, 34, Miyukigaoka, Tsukuba, Ibaraki 305 8501, Japan
    J Chem Inf Model 48:988-96. 2008
  10. ncbi Transfer learning for cytochrome P450 isozyme selectivity prediction
    Reiji Teramoto
    Forerunner Pharma Research Co, Ltd, 1 6, Suehiro cho, Turumi ku, Yokohama, Kanagawa 230 0045, Japan
    J Bioinform Comput Biol 9:521-40. 2011

Collaborators

Detail Information

Publications15

  1. doi Prediction of Alzheimer's diagnosis using semi-supervised distance metric learning with label propagation
    Reiji Teramoto
    Bio IT Center, NEC Corporation, Tsukuba, Ibaraki, Japan
    Comput Biol Chem 32:438-41. 2008
    ..Especially, SRF-LP largely outperformed when the number of training samples is very small. Our results also suggested that SRF-LP exhibits a synergistic effect of semi-supervised distance metric learning and label propagation...
  2. doi Protein expression profile characteristic to hepatocellular carcinoma revealed by 2D-DIGE with supervised learning
    Reiji Teramoto
    Bio IT Center, NEC Corporation, 34, Miyukigaoka, Tsukuba, Ibaraki 305 8501, Japan
    Biochim Biophys Acta 1784:764-72. 2008
    ..Our pilot study provides new insights on understanding the pathogenesis of HCC, histological grade and AFP level...
  3. doi Prediction of protein-ligand binding affinities using multiple instance learning
    Reiji Teramoto
    Advanced Technology Solutions Division, NEC Informatec Systems, Ltd, 2 6 1, Kitamigata, Takatsu ku, Kawasaki, Kanagawa 213 8511, Japan
    J Mol Graph Model 29:492-7. 2010
    ..The proposed method will accelerate efficient lead optimization on structure-based drug design and provide a new direction to designing of new scoring score functions...
  4. ncbi Supervised consensus scoring for docking and virtual screening
    Reiji Teramoto
    Fundamental and Environmental Research Laboratories, NEC Corporation, 34 Miyukigaoka, Tsukuba, Ibaraki 305 8501, Japan
    J Chem Inf Model 47:526-34. 2007
    ..SCS performs quite well in docking accuracy and is presumably useful for screening large-scale compound databases before predicting binding affinity...
  5. ncbi Supervised scoring models with docked ligand conformations for structure-based virtual screening
    Reiji Teramoto
    Fundamental and Environmental Research Laboratories, NEC Corporation, 34, Miyukigaoka, Tsukuba, Ibaraki 305 8501, Japan
    J Chem Inf Model 47:1858-67. 2007
    ..We also demonstrated that SSM is especially good at enhancing enrichments of the top ranks of screened compounds, which is useful in practical drug screening...
  6. doi Structure-based virtual screening with supervised consensus scoring: evaluation of pose prediction and enrichment factors
    Reiji Teramoto
    Bio IT Center and Nano Electronics Research Laboratories, NEC Corporation, 34, Miyukigaoka, Tsukuba, Ibaraki 305 8501, Japan
    J Chem Inf Model 48:747-54. 2008
    ..Since SCS requires only one 3D structure of protein-ligand complex, SCS will be useful for identifying new ligands...
  7. doi Consensus scoring with feature selection for structure-based virtual screening
    Reiji Teramoto
    Bio IT Center, NEC Corporation, 34, Miyukigaoka, Tsukuba, Ibaraki 305 8501, Japan
    J Chem Inf Model 48:288-95. 2008
    ..Moreover, we found that one can infer which scoring functions significantly enrich active compounds by using feature selection before actual docking and that the selected scoring functions are complementary...
  8. ncbi Comparative proteomic and transcriptomic profiling of the human hepatocellular carcinoma
    Hirotaka Minagawa
    Nano Electronics Research Laboratories, NEC Corporation, 34, Miyukigaoka, Tsukuba, Ibaraki 305 8501, Japan
    Biochem Biophys Res Commun 366:186-92. 2008
    ..Such multi-spotted proteins might arise as a consequence of post-translational modifications...
  9. doi Bootstrap-based consensus scoring method for protein-ligand docking
    Hiroaki Fukunishi
    Nano Electronics Research Laboratories, NEC Corporation, 34, Miyukigaoka, Tsukuba, Ibaraki 305 8501, Japan
    J Chem Inf Model 48:988-96. 2008
    ..3% to 52.1% and that (2) the rank of the crystal structure improved for 54.2% of the complexes and worsened for none. We also found that BBCS performed better than conventional consensus scoring (CS)...
  10. ncbi Transfer learning for cytochrome P450 isozyme selectivity prediction
    Reiji Teramoto
    Forerunner Pharma Research Co, Ltd, 1 6, Suehiro cho, Turumi ku, Yokohama, Kanagawa 230 0045, Japan
    J Bioinform Comput Biol 9:521-40. 2011
    ..Our algorithm can be an effective tool for P450 selectivity prediction for new chemical entities using multiple P450 isozyme activity data...
  11. doi Hidden active information in a random compound library: extraction using a pseudo-structure-activity relationship model
    Hiroaki Fukunishi
    Nano Electronics Research Laboratories, Central Research Laboratories, NEC Corporation, 34, Miyukigaoka, Tsukuba, Ibaraki 305 8501, Japan
    J Chem Inf Model 48:575-82. 2008
    ....
  12. doi Balanced gradient boosting from imbalanced data for clinical outcome prediction
    Reiji Teramoto
    Bio IT Center, NEC Corporation
    Stat Appl Genet Mol Biol 8:Article20. 2009
    ..e., gradient boosting, Random Forests and Support Vector Machine. Our results led us to the conclusion that BalaBoost is promising for clinical outcome prediction from imbalanced data...
  13. pmc A unique gene expression signature discriminates familial Alzheimer's disease mutation carriers from their wild-type siblings
    Yosuke Nagasaka
    Research Division, Sumitomo Pharmaceuticals Co, Ltd, 3 1 98 Kasugade naka, Konohana, Osaka 554 0022, Japan
    Proc Natl Acad Sci U S A 102:14854-9. 2005
    ..The results indicate that the disease process starts several decades before the onset of cognitive decline, suggesting that presymptomatic diagnosis of AD and other progressive cognitive disorders may be feasible in the near future...
  14. ncbi Prediction of siRNA functionality using generalized string kernel and support vector machine
    Reiji Teramoto
    Genomic Science Laboratories, Sumitomo Pharmaceuticals Co, Ltd, Osaka, Japan
    FEBS Lett 579:2878-82. 2005
    ..We applied this algorithm to published siRNAs, and could classify effective and ineffective siRNAs with 90.6%, 86.2% accuracy, respectively...
  15. ncbi A method for clustering gene expression data based on graph structure
    Shigeto Seno
    Department of Bioinformatic Engineering, Graduate School of Information Science and Technology, Osaka University, 1 3 Machikaneyama, Toyonaka, Osaka 560 8531, Japan
    Genome Inform 15:151-60. 2004
    ..We apply this method to the gene expression data of yeast cell-cycles and human lung cancer. The effectiveness of our method is demonstrated by comparing clustering results with other methods...