About this Research Topic
In the rapidly evolving field of genomics, single-cell transcriptomics has emerged as a pivotal technique, offering unprecedented insights into the cellular heterogeneity and dynamics within complex tissues and organisms. This groundbreaking approach allows researchers to dissect the transcriptome at the individual cell level, thereby revealing the subtleties of gene expression patterns that are often masked in bulk analyses. The advent of high-throughput sequencing technologies has further propelled this field, enabling the simultaneous analysis of thousands of single cells and providing a comprehensive view of cellular states and transitions.
The integration of machine learning (ML) techniques with single-cell transcriptomics has been a game-changer, enhancing the ability to decipher the vast and complex datasets generated by these studies. ML algorithms can identify patterns, predict cellular behaviors, and uncover novel biological insights that are not apparent through traditional analysis methods. This synergy between ML and single-cell transcriptomics is not only transforming our understanding of cellular processes but also opening new avenues for therapeutic interventions and disease modeling.
This Special Issue, titled "Machine Learning in Single-Cell Transcriptomics," aims to bring together researchers and practitioners from diverse backgrounds to explore the latest advancements and challenges in the intersection of these two dynamic fields. We invite submissions that showcase innovative applications of ML in the analysis of single-cell transcriptomic data, highlight methodological breakthroughs, and discuss the implications of these findings for biology and medicine.
• Development and application of novel ML algorithms for single-cell data analysis
• Integration of multi-omics data to enhance single-cell transcriptomic studies
• Exploration of cellular heterogeneity and state transitions using ML approaches
• Machine learning-assisted drug discovery and personalized medicine strategies
• Ethical considerations and challenges in the use of ML in genomics research
The integration of machine learning (ML) techniques with single-cell transcriptomics has been a game-changer, enhancing the ability to decipher the vast and complex datasets generated by these studies. ML algorithms can identify patterns, predict cellular behaviors, and uncover novel biological insights that are not apparent through traditional analysis methods. This synergy between ML and single-cell transcriptomics is not only transforming our understanding of cellular processes but also opening new avenues for therapeutic interventions and disease modeling.
This Special Issue, titled "Machine Learning in Single-Cell Transcriptomics," aims to bring together researchers and practitioners from diverse backgrounds to explore the latest advancements and challenges in the intersection of these two dynamic fields. We invite submissions that showcase innovative applications of ML in the analysis of single-cell transcriptomic data, highlight methodological breakthroughs, and discuss the implications of these findings for biology and medicine.
• Development and application of novel ML algorithms for single-cell data analysis
• Integration of multi-omics data to enhance single-cell transcriptomic studies
• Exploration of cellular heterogeneity and state transitions using ML approaches
• Machine learning-assisted drug discovery and personalized medicine strategies
• Ethical considerations and challenges in the use of ML in genomics research
Keywords: Single-cell transcriptomics, Machine learning (ML)
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