Methods, Tools and Algorithms in Integrative Bioinformatics

The Integrative Bioinformatics section is dedicated to publishing research focused on advancing the understanding and application of integrative bioinformatics in life sciences. This Topic brings together methodological innovations, open-source tools, and rigorous algorithmic frameworks that enable robust integration, preprocessing, and interpretation of heterogeneous biological data across scales.

We welcome contributions that:
Develop, benchmark, or validate computational methods for multi-omics data mining (e.g., genomics, transcriptomics, epigenomics, proteomics, metabolomics, single-cell and spatial omics).

Advance computational systems and network biology, including network inference, dynamic modeling, and causal discovery across biological layers.

Propose data imputation, normalization, harmonization, and preprocessing strategies tailored to multi-omics and multi-cohort settings.

Integrate multi-modal datasets to decipher disease mechanisms, patient stratification, and therapeutic targets, including translational and clinical applications.

Address heterogeneous data analysis and multi-modal data integration, encompassing structured and unstructured data (e.g., imaging, EHR, text).

Design or curate integrative omics databases, knowledge graphs, and interoperable, open-source tools that promote reproducibility, FAIR data principles, and community standards.

Introduce or apply machine learning methods in data integration, including representation learning, graph learning, generative modeling, transfer and federated learning, and uncertainty quantification.

Present novel multi-omics data integration methods, pipelines, and end-to-end frameworks with clear evaluation metrics, interpretability, and generalizability across datasets.

Article types may include methods papers, reviews and mini-reviews. Submissions should emphasize methodological transparency, reproducibility and rigorous validation on diverse datasets. Work that bridges basic biology and clinical translation, or that sets best practices and standards for the field, is especially encouraged.