From Graphs to Genes: Harnessing the Power of Machine Learning and Bioengineering

Machine Learning and Bioengineering have become pivotal technologies in modern science, technology, engineering, and social sciences. These fields have significantly optimized existing processes and introduced new, more efficient, and accurate methodologies. The advent of deep learning has propelled machine learning into the spotlight, especially as datasets grow in size and complexity. Companies are increasingly leveraging machine learning to enhance their business models, continuously improving performance with new data. Recent advancements in large-scale computing power and storage have accelerated the development of machine learning and bioengineering, making them crucial in areas such as medical applications, automotive technology, robotics, and social media. Despite these advancements, there remains a critical need for more research to develop better algorithms and bioengineering techniques that can handle complex tasks with greater efficiency and fewer errors.

This research topic aims to explore the recent advances in applying machine learning and bioengineering techniques to understand biological networks and their role in genetic regulation. The primary objectives include investigating how machine learning models and bioengineering tools can uncover hidden patterns in complex biological systems, provide insights into the underlying mechanisms of genetic regulation, and enable the development of novel therapeutics. Specific questions to be addressed include: How can machine learning approaches enhance our understanding of biological network structures and dynamics? What are the most effective computational methods for studying genetic regulation? How can graph theory and data mining be integrated with bioengineering to study biological systems?

To gather further insights into the intersection of machine learning and bioengineering, we welcome articles addressing, but not limited to, the following themes:

• Machine learning approaches to understanding the structure and dynamics of biological networks
• Graph theory and visualization of biological networks
• Computational methods for studying genetic regulation in model organisms
• Machine learning models for the analysis of biological networks
• Integration of graph theory, data mining, and machine learning methods with bioengineering approaches to study biological systems
• Models of gene regulatory networks based on time series data
• Computational models of gene expression from single-cell RNA-Seq data
• Network inference methods for understanding transcriptional regulation
• Machine learning methods for analyzing expression data to better understand gene regulation and disease mechanisms
• Data integration techniques for integrating multiple sources of genomic data
• Graph-based machine learning algorithms for predicting gene functions
• Network inference methods for identifying genetic interactions
• Applications of machine learning and bioengineering in personalized medicine

Keywords: machine learning, bioengineering, computational genomics, biological network analysis, gene regulatory networks

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.