ResearcherChiba Cancer CenterNo recent work listed
Naoki Kuwayama
Matched on OpenAlex author ID A5109512597. Sources: OpenAlex.
Their work
- Papers
- 3
- Trials
- 0
- Grants
- 0
- Labs led
- 0
- Collaborators
- 9
2019: 1 paper · 2020: none · 2021: none · 2022: 2 papers
Papers 3
- Transcriptomic analysis reveals high ITGB1 expression as a predictor for poor prognosis of pancreatic cancer. Co-author DOI 10.1371/journal.pone.0268630↗
- Machine learning with imaging features to predict the expression of ITGAV, which is a poor prognostic factor derived from transcriptome analysis in pancreatic … Co-author DOI 10.3892/ijo.2022.5350↗
- [A Case of Boderline Resectable Pancreatic Cancer Curatively Resected after Chemotherapy with Gemcitabine and Nab-Paclitaxel]. Co-author PMID 31164515↗
Closest collaborators
The 9 strongest of 9 by collab_strength@1: shared papers, trials and grants, recent and small-team work weighing most.
- Yosuke Iwatate · 2 shared papers, 2022 · strength 0.25
- Paper Transcriptomic analysis reveals high ITGB1 expression as a predictor for poor prognosis of pancreatic cancer. DOI 10.1371/journal.pone.0268630↗
- Paper Machine learning with imaging features to predict the expression of ITGAV, which is a poor prognostic factor derived fr… DOI 10.3892/ijo.2022.5350↗
- Fumitaka Ishige · 2 shared papers, 2022 · strength 0.25
- Paper Transcriptomic analysis reveals high ITGB1 expression as a predictor for poor prognosis of pancreatic cancer. DOI 10.1371/journal.pone.0268630↗
- Paper Machine learning with imaging features to predict the expression of ITGAV, which is a poor prognostic factor derived fr… DOI 10.3892/ijo.2022.5350↗
- Hidehito Arimitsu · 2 shared papers, 2022 · strength 0.25
- Paper Transcriptomic analysis reveals high ITGB1 expression as a predictor for poor prognosis of pancreatic cancer. DOI 10.1371/journal.pone.0268630↗
- Paper Machine learning with imaging features to predict the expression of ITGAV, which is a poor prognostic factor derived fr… DOI 10.3892/ijo.2022.5350↗
- Osamu Shimozato · 2 shared papers, 2022 · strength 0.25
- Paper Transcriptomic analysis reveals high ITGB1 expression as a predictor for poor prognosis of pancreatic cancer. DOI 10.1371/journal.pone.0268630↗
- Paper Machine learning with imaging features to predict the expression of ITGAV, which is a poor prognostic factor derived fr… DOI 10.3892/ijo.2022.5350↗
- Yasukuni Mori · 2 shared papers, 2022 · strength 0.25
- Paper Transcriptomic analysis reveals high ITGB1 expression as a predictor for poor prognosis of pancreatic cancer. DOI 10.1371/journal.pone.0268630↗
- Paper Machine learning with imaging features to predict the expression of ITGAV, which is a poor prognostic factor derived fr… DOI 10.3892/ijo.2022.5350↗
- Takashi Uno · 2 shared papers, 2022 · strength 0.25
- Paper Transcriptomic analysis reveals high ITGB1 expression as a predictor for poor prognosis of pancreatic cancer. DOI 10.1371/journal.pone.0268630↗
- Paper Machine learning with imaging features to predict the expression of ITGAV, which is a poor prognostic factor derived fr… DOI 10.3892/ijo.2022.5350↗
- Hiroki Nagase · 2 shared papers, 2022 · strength 0.25
- Paper Transcriptomic analysis reveals high ITGB1 expression as a predictor for poor prognosis of pancreatic cancer. DOI 10.1371/journal.pone.0268630↗
- Paper Machine learning with imaging features to predict the expression of ITGAV, which is a poor prognostic factor derived fr… DOI 10.3892/ijo.2022.5350↗
- Wataru Takayama · 2 shared papers, 2022 · strength 0.25
- Paper Transcriptomic analysis reveals high ITGB1 expression as a predictor for poor prognosis of pancreatic cancer. DOI 10.1371/journal.pone.0268630↗
- Paper Machine learning with imaging features to predict the expression of ITGAV, which is a poor prognostic factor derived fr… DOI 10.3892/ijo.2022.5350↗
- Shinichiro Shimizu · 1 shared paper, 2019 · strength 0.08
How to reach this person
A chain of introductions through people who have published, run trials or held grants together (warm_path@1: fewest links first, then the strongest). Built from public records only; no contact details are kept.