Dr. David Broneske

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

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

Extracting information from diverse websites is increasingly important, especially for analyzing vast data sets to detect trends, gain insights. By studying job ads, researchers can monitor employer demand shifts, assisting policymakers in aiding affected workers and industries. However, extraction faces challenges like varied website formats, dynamic content, and duplicate data. This study introduces a method for extracting data from diverse private university websites involving keyword identification, website categorization, and extraction pipelines.