ArticlePloS one2026
Identifying researcher characteristics driving growth in Japanese University-originated deep-tech startups: A machine learning approach.
Article in PloS one, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Authors and funding
2 authors.
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Abstract
Active involvement of researchers with strong outputs is essential for growth of deep-tech university-originated startups. We used the University-Originated Venture Database to link total funding amount with researchers' publication metrics and KAKENHI grant records. Classification models were constructed to distinguish growing and non-growing startups, followed by feature-importance and breakdown-tree analyses to interpret which researcher attributes drive growth. We then applied these defined characteristics to evaluate researcher profiles at top Japanese universities to assess their entrepreneurial engagement. This process revealed a disconnect between high-quality research achievements and actual startup activity. These insights underpin an identification framework that can guide investment and policy decisions in Japan's deep-tech ecosystem.
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