Articles

Computational biology research faces a formidable challenge in the detection of complexes within protein-protein interaction (PPI) networks, critical for unraveling cellular processes, predicting functions of uncharacterized proteins, and diagnosing diseases. While evolutionary algorithms (EAs), particularly state-of-the-art methods, often partition PPI networks based on graph properties or biological semantics, their resilience to noisy or missing interactions remains an underexplored territory. In this paper, we propose a groundbreaking heuristic operator, termed "strong neighbor-node migration", specifically designed to elevate solution quality [...] Full Abstract: https://ieeexplore.ieee.org/document/10440281

It continues to be a puzzle that women are disproportionally often dropping out of academic careers. Researchers and policymakers have suggested that same-gender supervisors are important for tightening this ‘leaky pipeline’. Especially in subjects with a strong overrepresentation of men, it seems likely that female supervisors work as positive role models and help preventing discrimination. Anticipating this effect, female doctoral students might also prefer supervisors of the same gender. Therefore, we ask how widespread a gender match is between doctoral student and supervisor in Germany and whether a gender match between supervisors and doctoral students is beneficial for the doctorate and for a possible scientific career thereafter.

This paper investigates the relationship between age and attaining a tenured position in academia. Following considerations about ageism towards doctoral graduates who were 40 years and older upon attaining a PhD and Robert K. Merton’s idea of cumulative advantages in academic careers, we differentiate between biological and academic age. We test the relationships and the resources accumulated behind the latter using data from the DZHW PhD Panel 2014. Applying piecewise constant exponential estimations and an entropy balancing, we find that PhDs aged 40+ experience a significantly positive effect on attaining a professorship at a university of applied science or receiving tenure as a postdoctoral researcher.

Graph data models enable efficient storage, visualization, and analysis of highly interlinked data, by providing the benefits of horizontal scalability and high query performance. Clustering techniques, such as K-means, hierarchical clustering, are highly beneficial tools in data mining and machine learning to find meaningful similarities and differences between data points. Recent developments in graph data models, as well as clustering algorithms for graph data, have shown promising results in image segmentation, gene data analysis, etc. This has been primarily achieved through research and development of algorithms in the field of spectral theory, [...] Full abstract: https://doi.org/10.1007/s44163-024-00102-x