Stefan Wuchty | Searcharxiv

arXiv2025

Interdisciplinary Papers Supported by Disciplinary Grants Garner Deep and Broad Scientific Impact

Interdisciplinary research has emerged as a hotbed for innovation and a key approach to addressing complex societal challenges. The increasing dominance of grant-supported research in shaping scientific advances, coupled with growing interest in funding interdisciplinary work, raises fundamental questions about the effectiveness of interdisciplinary grants in fostering high-impact interdisciplinary research outcomes. Here, we quantify the interdisciplinarity of both research grants and publications, capturing 350,000 grants from 164 funding agencies across 26 countries and 1.3 million papers that acknowledged their support from 1985 to 2009. Our analysis uncovers two seemingly contradictory patterns: Interdisciplinary grants tend to produce interdisciplinary papers, which are generally associated with high impact. However, compared to disciplinary grants, interdisciplinary grants on average yield fewer papers and interdisciplinary papers they support tend to have substantially reduced impact. We demonstrate that the key to explaining this paradox lies in the power of disciplinary grants in propelling high-impact interdisciplinary research. Specifically, our results show that highly interdisciplinary papers supported by deeply disciplinary grants garner disproportionately more citations, both within their core disciplines and from broader fields. Moreover, disciplinary grants, particularly when combined with other similar grants, are more effective in producing high-impact interdisciplinary research. Amidst the rapid rise of support for interdisciplinary work across the sciences, these results highlight the hitherto unknown role of disciplinary grants in driving crucial interdisciplinary advances, suggesting that interdisciplinary research requires deep disciplinary expertise and investments.

cs.DL↗

arXiv2019

A Stable Combinatorial Particle Swarm Optimization for Scalable Feature Selection in Gene Expression Data

Evolutionary computation (EC) algorithms, such as discrete and multi-objective versions of particle swarm optimization (PSO), have been applied to solve the Feature selection (FS) problem, tackling the combinatorial explosion of search spaces that are peppered with local minima. Furthermore, high-dimensional FS problems such as finding a small set of biomarkers to make a diagnostic call add an additional challenge as such methods ability to pick out the most important features must remain unchanged in decision spaces of increasing dimensions and presence of irrelevant features. We developed a combinatorial PSO algorithm, called COMB-PSO, that scales up to high-dimensional gene expression data while still selecting the smallest subsets of genes that allow reliable classification of samples. In particular, COMB-PSO enhances the encoding, speed of convergence, control of divergence and diversity of the conventional PSO algorithm, balancing exploration and exploitation of the search space. Applying our approach on real gene expression data of different cancers, COMB-PSO finds gene sets of smallest size that allow a reliable classification of the underlying disease classes.

cs.NE↗