The development of high-throughput sequencing technology and the advent of omics approaches have been providing a solid basis for the systematic understanding of the function of human genes and the mechanism of cancers and other diseases. In the last years, the integration of multiple omics data has provided many advantages over single omics approaches in providing a more comprehensive understanding of the molecular basis of disease. For instance: the integration of genome-wide association studies (GWAS) data using Mendelian Randomization (MR) has been used widely in identifying causal phenotypes of human diseases; the integration of GWAS data and expression quantitative trait loci (eQTL) data using Summarized MR (SMR) can facilitate mining causal genes of human diseases; and the integration of microarray data and Next Generation Sequencing data using machine learning technology has been used to successfully identify gene signatures associated with clinically relevant molecular subtypes and prognosis in complex diseases. Though successful, the emergence of sequencing technologies such as single cell sequencing and metagenomics sequencing have posed more challenges for data integration methods. It is, therefore, essential to apply novel statistical methods and artificial intelligence approaches for integrating multiple omics data, such as single cell sequencing data, microbial Quantitative Trait Loci (mbQTL) data, and microbiome GWAS (mGWAS) data.
This Research Topic aims to showcase recent progress on studies around the following topics:
• Machine learning approaches and applications for integrating multi-level omics data.
• Statistical methods and applications for integrating multi-level omics data.
• Identification and validation of molecular biomarker for cancers and diseases
• Tools and databases for integrating and analysing omics data.
• Pan-cancer analysis and validation through integrating multi-level omics data.
• Novel findings on molecular biomarkers and signatures of cancers and diseases.
• Identification of novel prognostic biomarkers of cancers and diseases.
Please note: The findings based on data integration should be also validated by observational/experimental data. Descriptive studies and studies consisting solely of bioinformatic investigation of publicly available genomic/transcriptomic data do not fall within the scope of the journal.
The development of high-throughput sequencing technology and the advent of omics approaches have been providing a solid basis for the systematic understanding of the function of human genes and the mechanism of cancers and other diseases. In the last years, the integration of multiple omics data has provided many advantages over single omics approaches in providing a more comprehensive understanding of the molecular basis of disease. For instance: the integration of genome-wide association studies (GWAS) data using Mendelian Randomization (MR) has been used widely in identifying causal phenotypes of human diseases; the integration of GWAS data and expression quantitative trait loci (eQTL) data using Summarized MR (SMR) can facilitate mining causal genes of human diseases; and the integration of microarray data and Next Generation Sequencing data using machine learning technology has been used to successfully identify gene signatures associated with clinically relevant molecular subtypes and prognosis in complex diseases. Though successful, the emergence of sequencing technologies such as single cell sequencing and metagenomics sequencing have posed more challenges for data integration methods. It is, therefore, essential to apply novel statistical methods and artificial intelligence approaches for integrating multiple omics data, such as single cell sequencing data, microbial Quantitative Trait Loci (mbQTL) data, and microbiome GWAS (mGWAS) data.
This Research Topic aims to showcase recent progress on studies around the following topics:
• Machine learning approaches and applications for integrating multi-level omics data.
• Statistical methods and applications for integrating multi-level omics data.
• Identification and validation of molecular biomarker for cancers and diseases
• Tools and databases for integrating and analysing omics data.
• Pan-cancer analysis and validation through integrating multi-level omics data.
• Novel findings on molecular biomarkers and signatures of cancers and diseases.
• Identification of novel prognostic biomarkers of cancers and diseases.
Please note: The findings based on data integration should be also validated by observational/experimental data. Descriptive studies and studies consisting solely of bioinformatic investigation of publicly available genomic/transcriptomic data do not fall within the scope of the journal.
