Welcome
In the era of Big Data, advanced sequencing technologies have revolutionized multi-omics research by generating data across genomics, transcriptomics, proteomics, and metabolomics. However, this deluge of multi-omics data, coupled with its inherent complexity and diversity, present significant challenges for storage, processing, and analysis. Heuristic algorithms (HA) and pattern mining (PM) offer innovative solutions, enabling efficient optimization and the discovery of hidden patterns in biological datasets.
This workshop focuses on innovative computational approaches, based on HA and PM, to address critical challenges in multi-omics research and advance health informatics. The aim is to explore cutting-edge techniques in this rapidly evolving field by providing a networking opportunity to bring together researchers and practitioners. By highlighting the intersection of data science and multi-omics, the workshop will contribute to the broader goals of the BIBM conference, advancing knowledge in computational biology and data-driven multi-omics analytics.
Topics
The workshop will cover a wide range of topics, including but not limited to:
- Applications of HA and PM in genomics, transcriptomics, proteomics, and metabolomics.
- HA for multi-omics data integration and analysis.
- PM for identifying biomarkers, regulatory motifs, and functional elements.
- HA for optimizing sequence alignment and genome assembly in large-scale datasets.
- PM for associating genetic variants with diseases and phenotypic traits.
- HA for drug repurposing and virtual screening in drug discovery.
- PM for identifying therapeutic targets in cancer genomics and rare diseases.
- HA for optimizing clinical decision support systems using multi-omics data.
- Distributed and parallel HA for large-scale multi-omics data processing.
- Scalable PM algorithms for high-throughput sequencing data.
- Memory-efficient HA for handling high-dimensional omics datasets.
- Real-time PM techniques for dynamic and streaming omics data.
- Graph-based PM for analyzing biological networks (e.g., protein-protein interactions, gene regulatory networks).
- Swarm intelligence and evolutionary-based HA for multi-omics data analysis and pipeline optimization.
- Hybrid approaches combining HA and PM with ML for enhanced predictive modeling.
- Integration of HA and PM with deep learning for feature extraction and classification.
- Explainable AI (XAI) using HA for interpretable multi-omics analytics.
- HA and PM for single-cell multi-omics data analysis.
- PM for microbiome data analysis: Identifying microbial patterns linked to health or disease
- HA for long-read sequencing data analysis and error correction.
- HA for workflow optimization in multi-omics data pipelines
- Novel PM algorithms for high-dimensional and heterogeneous omics data.
- Quantum-inspired HA for multi-omics data optimization.
- Case studies of HM and PM in disease diagnosis, drug discovery, and personalized medicine.
- Real-world applications of PM in infectious disease surveillance and pathogen evolution.
- Theoretical foundations of HA and PM in biological data analysis.
- Scalability and performance optimization in PM and ML algorithms.
- Open-source tools and frameworks for HA and PM in bioinformatics.
Publication
Accepted papers will be published in the main IEEE Proceedings of the BIBM 2025 conference (CCF-B rank).
A special issue in a journal is also planned for extensions of accepted papers (under discussion, to be announced later).
A "best paper award" will be awarded for the best paper of the workshop.
Participation
The workshop will be held in hybrid mode, which means that authors of accepted papers can do their presentation online or offline (at BIBM).
Contact
For any questions, please contact the organizing committee.
