Large-Scale Optimization Based Bioinformatics and Genetic Diseases

Dear Colleagues,

In practically all disciplines where mathematical models are used, bioinformatics and genetic diseases (BGDs) can be formulated as large-sale optimization problems (LSOPs). With the explosion of heterogeneous omics datasets combined with other high-quality clinical and imaging datasets in BGD areas, the corresponding LSOP tasks turn out to be challenging due to the curse of dimensionality in terms of both efficiency and effectiveness. Thus, more practical and versatile algorithms for solving large-sale optimization problems in BGD areas are expected, which can assist the development of computational intelligence and contribute to other emerging areas. This Special Issue is devoted to a broad range of large-scale practical problems that occur in BGD areas, including graph neural network optimization, intelligent optimization, artificial intelligence, and pattern recognition, to name but a few. In this Special Issue, we welcome the submission of research articles and review articles on topics related to large-scale optimization-based bioinformatic approaches in human diseases, describing novel models, algorithms, tools, software, and pipelines in all fields of human diseases. Research topics among the following themes are especially welcomed:

1. Multi-omics large-scale molecular optimization and analysis of human diseases, using bulk or single-cell data of genomic, transcriptomic, epigenomic, proteomic, metabonomic, or metagenomic samples.
2. Integrative analysis of human diseases, using molecular, text, image, behaviour, and phenotyping feature data from sick or healthy individuals.
3. Temporal–spatial analysis of omics data from disease cohorts or animal models horizontally and longitudinally.
4. The large-scale optimization and identification of disease drivers, disease dynamic network biomarkers, and disease treatment targets for human diseases.
5. Novel large-scale optimization algorithms (e.g., mathematical programming methods, reinforcement learning methods, evolutionary algorithms, and other optimizers) for disease prediction, disease stratification, etc., in precision medicine.
6. Special techniques for handling large-scale search spaces, e.g., dimensionality reduction, gradient guidance, generative models, parallelization, etc., supporting biomedical big data analysis of human diseases.
7. Performance assessment, database and website construction, and benchmarking algorithms on BGDs.

Prof. Dr. Peiluan Li
Dr. Weifeng Guo
Guest Editors

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• large-scale optimization
• bioinformatics
• single cell
• precision medicine
• multi-omics analysis
• integrative analysis
• temporal-spatial analysis
• machine learning
• computational intelligence