Abstract
This special issue includes a selection of papers presented at the 19th International Symposium on Bioinformatics Research and Applications (ISBRA 2023), which was held at the Łukasiewicz Research Network—PORT Polish Center for Technology Development, Wrocław, Poland on October 9–12, 2023. ISBRA provides a forum for the exchange of ideas and results among researchers, developers, and practitioners working on all aspects of bioinformatics and computational biology and their applications.
In 2023, 89 abstracts were submitted in response to the call for papers, out of which 28 full papers and 16 short papers appeared in the ISBRA proceedings published as volume 14,248 of Springer Verlag’s Lecture Notes in Bioinformatics series. The authors of 19 papers were invited to submit an extended version of their abstracts to this two-part special issue. This first part contains 10 papers, while the second part contains the remaining 9 papers.
In “A Branch-and-Bound Algorithm for the Molecular Ordered Covering Problem”, the authors present a branch-and-bound algorithm for optimizing the ordering of distance constraints to improve the computational efficiency of constructing 3D molecular structures, and test it on protein data bank data. The article “Visual Recalibration and Gating Enhancement Network for Radiology Report Generation” presents an approach, which enhances both visual and textual aspects of automated radiology medical report generation, compared with existing models. In “Towards the Reconciliation of Inconsistent Molecular Structures from Biochemical Databases,” the authors present a novel tool for resolving unique molecular structures from the database identifiers of many popular databases, and can be easily extended to more databases in the future. The authors of “Boolean Network Models of Human Preimplantation Development” present a framework for generating Boolean networks for differentiating between stages in cell development from single-cell transcriptomics data. In “Enhanced Compression of k-mer Sets with Counters via de Bruijn Graphs,” the authors improve the compression of k-mer sets with counts by leveraging the connectivity and density of the constructed de Bruijn graph. The article “A Comparative Study of Gene Co-Expression Thresholding Algorithms” compares the performance of a number of thresholding methods over a large collection of graphs derived from real high throughput sequencing data. In “Phylogenetic and Chemical Probing Information as Soft Constraints in RNA Secondary Structure Prediction,” the authors demonstrate that phylogenetic and chemical probing information can be incorporated into thermodynamics-based RNA folding algorithms in the form of pseudo-energies, which can substantially improve prediction accuracy for single sequences. The authors of “Improve the Cryo-EM Micrographs Denoising with Simulation-Aware Pre-Training” introduce a pre-trained deep learning model based on an accurately simulated dataset for denoising cryo-electron microscopy (cryo-EM) images. In “A Fusion Learning Model Based on Deep Learning for scRNA-seq Clustering,” the authors present a learning model that takes into account both deep and surface information from single-cell transcriptomics data for improved clustering accuracy. The article “Transcriptional Hubs Within Cliques in Ensemble Hi-C Chromatin Interaction Networks” presents a clique-based approach for analyzing high throughput chromatin conformation capture (Hi-C) data that helps identify chromosomal transcriptional hotspots.