Demo Data from VLDB 2026 / reviewers in the wild / expert
Nobuhiko Tanaka
dblp:46/7969
· DBLP profile ↗
2ranked-venue papers
1as first author
0since 2021 · last 2020
0000-0002-9568-9917ORCID · corroborated
Domains — the database's venue-derived domains; a paper can count in several
Applied, interdisciplinary, general and emerging computing · 2 · 1 first-author
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Interdisciplinary, comprehensive, and emerging computing
2 papers |
Bioinformatics and computational biology · 89% Medical and health informatics · 11% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Bioinformatics and computational biology › statistical genetics
genotype-phenotype association |
0.4 | 1 | 2020 | Soft windowing application to improve analysis of high-throughput phenotyping data · Bioinform. 2020 |
Bioinformatics and computational biology
phenomics |
0.4 | 1 | 2020 | Soft windowing application to improve analysis of high-throughput phenotyping data · Bioinform. 2020 |
Medical and health informatics › clinical data analysis
phenotyping |
0.1 | 1 | 2010 | SDOP-DB: a comparative standardized-protocol database for mouse phenotypic analyses · Bioinform. 2010 |
Methods — techniques the papers use, named apart from their topics
soft windowing · 0.4resampling · 0.4
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2020 | Soft windowing application to improve analysis of high-throughput phenotyping dataabstractMOTIVATION: High-throughput phenomic projects generate complex data from small treatment and large control groups that increase the power of the analyses but introduce variation over time. A method is needed to utlize a set of temporally local controls that maximizes analytic power while minimizing noise from unspecified environmental factors. RESULTS: Here we introduce 'soft windowing', a methodological approach that selects a window of time that includes the most appropriate controls for analysis. Using phenotype data from the International Mouse Phenotyping Consortium (IMPC), adaptive windows were applied such that control data collected proximally to mutants were assigned the maximal weight, while data collected earlier or later had less weight. We applied this method to IMPC data and compared the results with those obtained from a standard non-windowed approach. Validation was performed using a resampling approach in which we demonstrate a 10% reduction of false positives from 2.5 million analyses. We applied the method to our production analysis pipeline that establishes genotype-phenotype associations by comparing mutant versus control data. We report an increase of 30% in significant P-values, as well as linkage to 106 versus 99 disease models via phenotype overlap with the soft-windowed and non-windowed approaches, respectively, from a set of 2082 mutant mouse lines. Our method is generalizable and can benefit large-scale human phenomic projects such as the UK Biobank and the All of Us resources. AVAILABILITY AND IMPLEMENTATION: The method is freely available in the R package SmoothWin, available on CRAN http://CRAN.R-project.org/package=SmoothWin. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. Hamed Haselimashhadi, Jeremy C. Mason, Violeta Muñoz-Fuentes, Federico López 0001, Kolawole O. Babalola, Elif F. Acar, Jacqui White, Ann M. Flenniken, Ruairidh King, Ewan Straiton, John Richard Seavitt, Angelina Gaspero, Arturo Garza, Audrey E. Christianson, Corey L. Reynolds, Denise G. Lanza, Isabel Lorenzo, Jennie R. Green, Juan J. Gallegos, Ritu Bohat, Rodney C. Samaco, Surabi Veeraragavan, Jong Kyoung Kim, Gregor Miller, Helmut Fuchs, Lillian Garrett, Lore Becker, Yeon Kyung Kang, David Clary, Soo Young Cho, Masaru Tamura, Nobuhiko Tanaka, Kyung Dong Soo, Alexandr Bezginov, Ghina Bou About, Marie-France Champy, Laurent Vasseur, Sophie Leblanc, Hamid Meziane, Mohammed Selloum, Patrick T. Reilly, Nadine Spielmann, Holger Maier, Valérie Gailus-Durner, Tania Sorg, Hiroshi Masuya, Yuichi Obata, Jason D. Heaney, Mary E. Dickinson, Wolfgang Wurst, Glauco P. Tocchini-Valentini, Kevin C. Kent Lloyd, Colin McKerlie, Je Kyung Seong, Yann Herault, Martin Hrabé de Angelis, Steve D. M. Brown, Damian Smedley, Paul Flicek, Ann-Marie Mallon, Helen E. Parkinson, Terrence F. Meehan |
Bioinform. | 34 |
| 2010 | SDOP-DB: a comparative standardized-protocol database for mouse phenotypic analysesabstractAbstract Summary: This article reports the development of SDOP-DB, which can provide definite, detailed and easy comparison of experimental protocols used in mouse phenotypic analyses among institutes or laboratories. Because SDOP-DB is fully compliant with international standards, it can act as a practical foundation for international sharing and integration of mouse phenotypic information. Availability: SDOP-DB (http://www.brc.riken.jp/lab/bpmp/SDOP/) Contact: [email protected] Supplementary information: Supplementary data are available at Bioinformatics online. Nobuhiko Tanaka, Kazunori Waki, Hideki Kaneda, Ikuko Yamada, Tamio Furuse, Kimio Kobayashi, Hiromi Motegi, Hideaki Toki, Maki Inoue, Osamu Minowa, Tetsuo Noda, Keizo Takao, Tsuyoshi Miyakawa, Aki Takahashi, Tsuyoshi Koide, Shigeharu Wakana, Hiroshi Masuya |
Bioinform. | 1 |