In recent years, a large amount of omics datasets have been generated by high-throughput sequencing in the cancer genomics field. In this Research Topic, we will mainly focus on the research based on these big data sets, paying close attention to machine learning methods as well as their applications on conducting big data analysis that facilitates the use of genomic information in cancer, including treatment, diagnostics, and prevention.
Most cancer genomics research papers published today include bioinformatics analysis. However, in this special issue, we focus on studies that are big data-driven and led by bioinformatics analysis. This includes research related to state-of-the-art sequencing technologies like single-cell sequencing, 3D genome sequencing, and long-read sequencing. We emphasize novel findings and their translational value or potency through data analysis, which includes work using new strategies or tools or databases or ideas to analyze data in the public domain or studies that integrate multiple sources of published data.
This Research Topic welcomes, but is not limited to, the following subtopics:
• Big data in cancer genomics/epigenomics, including next-generation sequencing data (WGS/RNA-seq/ATAC-seq/WGBS/ChIP-seq/Hi-C) or long-read sequencing data (PacBio/Nanopore sequencing), and its roles and applications in experimental and translational medicine. High-throughput sequencing-based cancer related long noncoding RNA gene identification and functional analysis are also included.
• Development and application of machine learning methods in cancer genomics, especially but not limit to using big data and machine learning to develop new strategies in cancer risk evaluation, early intervention and treatment stratification, diagnosis/prognosis biomarker identification, and medical image processing. Cancer immunology and immunotherapy, such as neoantigen/CAR-T target prediction and cancer immunotherapy biomarker discovery, are included.
• Bioinformatics software and tools in cancer-related experimental and translational medicine, including novel bioinformatics software and pipelines in high-throughput sequencing data analysis, big data visualization, and biological network analysis. Novel cancer-related databases for clinical and basic medicine are also included.
We welcome all article types. Methods papers should report novel methods or significant advances in existing methods. Technology and Code or Data Report articles detailing new software, pipelines, or databases are also welcome. All data must be available (e.g., deposited in public domain or accessed by application). Code is encouraged to be free to the public.
Image courtesy of: Kras-Driven Lung Cancer. Photo by Eric Snyder on Unsplash
In recent years, a large amount of omics datasets have been generated by high-throughput sequencing in the cancer genomics field. In this Research Topic, we will mainly focus on the research based on these big data sets, paying close attention to machine learning methods as well as their applications on conducting big data analysis that facilitates the use of genomic information in cancer, including treatment, diagnostics, and prevention.
Most cancer genomics research papers published today include bioinformatics analysis. However, in this special issue, we focus on studies that are big data-driven and led by bioinformatics analysis. This includes research related to state-of-the-art sequencing technologies like single-cell sequencing, 3D genome sequencing, and long-read sequencing. We emphasize novel findings and their translational value or potency through data analysis, which includes work using new strategies or tools or databases or ideas to analyze data in the public domain or studies that integrate multiple sources of published data.
This Research Topic welcomes, but is not limited to, the following subtopics:
• Big data in cancer genomics/epigenomics, including next-generation sequencing data (WGS/RNA-seq/ATAC-seq/WGBS/ChIP-seq/Hi-C) or long-read sequencing data (PacBio/Nanopore sequencing), and its roles and applications in experimental and translational medicine. High-throughput sequencing-based cancer related long noncoding RNA gene identification and functional analysis are also included.
• Development and application of machine learning methods in cancer genomics, especially but not limit to using big data and machine learning to develop new strategies in cancer risk evaluation, early intervention and treatment stratification, diagnosis/prognosis biomarker identification, and medical image processing. Cancer immunology and immunotherapy, such as neoantigen/CAR-T target prediction and cancer immunotherapy biomarker discovery, are included.
• Bioinformatics software and tools in cancer-related experimental and translational medicine, including novel bioinformatics software and pipelines in high-throughput sequencing data analysis, big data visualization, and biological network analysis. Novel cancer-related databases for clinical and basic medicine are also included.
We welcome all article types. Methods papers should report novel methods or significant advances in existing methods. Technology and Code or Data Report articles detailing new software, pipelines, or databases are also welcome. All data must be available (e.g., deposited in public domain or accessed by application). Code is encouraged to be free to the public.
Image courtesy of: Kras-Driven Lung Cancer. Photo by Eric Snyder on Unsplash
