AI in Integrative Bioinformatics

In recent years, artificial intelligence (AI) has revolutionized many fields by providing advanced tools for data analysis and interpretation. In the domain of integrative bioinformatics, AI methods are increasingly being utilized to address complex biological questions. Integrative bioinformatics involves the combination of diverse datasets, such as genomic, proteomic, and metabolomic data, to generate a holistic understanding of biological processes. The vast amount of data generated from modern high-throughput technologies poses significant challenges in terms of analysis and interpretation. AI algorithms, particularly machine learning and deep learning techniques, have demonstrated remarkable capabilities in deciphering patterns from large, multidimensional data sets. These methods can not only identify novel biological insights but also facilitate the development of predictive models for disease progression, drug response, and other critical biomedical applications. However, integrating AI into bioinformatics to draw meaningful conclusions remains a complex task that requires interdisciplinary collaboration and innovation. Developing effective AI-driven models and tools to enhance integrative bioinformatics efforts could greatly bolster our understanding of fundamental biological systems and enable personalized medicine approaches.

The aim of this research topic is to explore the potential and challenges of applying AI technologies in integrative bioinformatics. By fostering interdisciplinary collaboration, we seek to advance the development and application of AI-driven methods and tools to enhance biological data analysis, interpretation, and prediction.

We invite submissions that demonstrate the novel application of AI techniques in integrative bioinformatics, review articles that consolidate existing knowledge, and perspective papers proposing innovative approaches or highlighting future directions alongside methodologies and technology and code papers. We welcome contributions from researchers across bioinformatics, computer science, medicine, and other related fields. We welcome contributions that explore but are not limited to:

1. AI in Genomic Data Interpretation and Application: Application of AI algorithms to analyze and interpret genomic data, uncovering genetic variants or expression patterns that influence biological traits or diseases and further application to preclinical models.
2. AI in Protein Data Interpretation and Application: Application of AI to analyze structure, uncover variant, inform in-silico experiments and further applications.
3. Systems-Level Proteomics and Metabolomics Analysis Using AI: Developing AI models to process and integrate proteomic and metabolomic data for insights into cellular processes and pathways at a systems level.
4. AI in Multi-Omics Data Integration: Integrating multiple data types (such as genomics, transcriptomics, proteomics, and epigenomics) using AI approaches to generate comprehensive biological insights.
5. Systematic Biological Network Analysis: Leveraging AI techniques to examine complex biological networks, such as protein-protein interactions and gene regulatory networks, to reveal critical biological functions or disease mechanisms.
6. AI-Assisted Predictive Modeling in Precision Medicine: Utilizing AI to develop predictive models for personalized treatments based on integrative bioinformatics data, advancing precision medicine initiatives.
7. AI-Driven Drug Discovery, Development, and Application: Exploring the role of AI in identifying novel drug candidates and predicting drug responses by utilizing integrative bioinformatics approaches and applying them to preclinical models.
8. AI-Assisted Environmental Bioinformatics: Applying AI to assess the impact of environmental factors on biological systems, integrating genomic and ecological data to understand ecosystem dynamics and organismal responses.

We are particularly interested in submissions that present collaborative efforts that bridge the gap between AI technology and biological sciences, aiming to foster the development of practical and scalable AI applications in bioinformatics.

Article types and fees

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Case Report
• Data Report
• Editorial
• FAIR² Data
• FAIR² DATA Direct Submission
• General Commentary
• Hypothesis and Theory
• Methods
• ... View all formats

Articles that are accepted for publication by our external editors following rigorous peer review incur a publishing fee charged to Authors, institutions, or funders.

Article types

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Case Report
• Data Report
• Editorial
• FAIR² Data
• FAIR² DATA Direct Submission
• General Commentary
• Hypothesis and Theory
• Methods
• Mini Review
• Opinion
• Original Research
• Perspective
• Policy Brief
• Review
• Systematic Review
• Technology and Code

Keywords: Artificial Intelligence, Machine Learning, Multi-Omics Integration, Predictive Modeling, Systems Biology, Precision Medicine, Drug Development, Environmental Factors

Important note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.