The development of high throughput omics technologies has enabled accurate measurement of multiple modalities simultaneously in individual studies or multi-omics integration from different studies, and rapid data accumulation in multimodal omics, including genomics, transcriptomics, proteomics, metabolomics, phenomics, radiomics and the cutting-edge 3D spatial omics, single-cell omics. These provide an unparalleled opportunity for knowledge discovery in intractable diseases, such as the discovery of biomarkers, functional modules, causal pathways and regulatory networks, etc., which have great potential to bolster the therapeutic pipelines.
Traditional statistical methods have been successfully applied to incorporate multi-omic datasets, such as genome-wide association studies (GWAS), molecular quantitative trait loci (QTL) analysis, and summarized Mendelian Randomization. However, limited pre-defined modalities have restricted the flexibility of available omics data, and the ability to incorporate different types of features of existing methods is still insufficient, which both decrease the power in knowledge discovery. Considering these aspects, advanced data mining, statistical and machine learning methods are urgently needed to perform cross-modal data integration and modeling.
In the first volume, we collected insights on statistical methods and applications for integrating multimodal omics data, machine learning methods of feature representation for multimodal omics, database and web tools for depositing and visualizing disease-related knowledge discovered based on multimodal omics, as well as identification of molecular biomarkers for complex diseases.
With this Volume II Research Topic we aim to build on the knowledge acquired in the first volume. We aim to further investigate the computational methods integrating multimodal data for disease and other traits exploration. Especially, we want to expand the scope of the investigated data modalities. We also would like to attract studies using Electroencephalography (EEG) and functional Magnetic Resonance Imaging (fMRI) to investigate neurodegenerative diseases, affective disorders, cognitive disorders and emotion recognition.
Topics of interest include but are not limited to:
(1) Computational methods investigating neurodegenerative diseases and other brain disorders based on EEG/fMRI data.
(2) Affective computing based on EEG/fMRI data.
(3) Methods and discoveries based on the integration of EEG/fMRI data and omics data.
(4) Statistical methods and applications for integrating multimodal omics data.
(5) Machine learning methods of feature representation for multimodal omics.
(6) Graph-based deep learning methods for disease-related node/linkage prediction.
(7) Identification of molecular biomarkers for complex diseases.
(8) Disease-related module identification and validation through integrating multimodal omics data.
(9) Database and web tools for depositing and visualizing disease-related knowledge discovered based on multimodal omics.
(10) Causal inference between exposure factors and important traits or diseases based statistical methods and machine learning methods.
The development of high throughput omics technologies has enabled accurate measurement of multiple modalities simultaneously in individual studies or multi-omics integration from different studies, and rapid data accumulation in multimodal omics, including genomics, transcriptomics, proteomics, metabolomics, phenomics, radiomics and the cutting-edge 3D spatial omics, single-cell omics. These provide an unparalleled opportunity for knowledge discovery in intractable diseases, such as the discovery of biomarkers, functional modules, causal pathways and regulatory networks, etc., which have great potential to bolster the therapeutic pipelines.
Traditional statistical methods have been successfully applied to incorporate multi-omic datasets, such as genome-wide association studies (GWAS), molecular quantitative trait loci (QTL) analysis, and summarized Mendelian Randomization. However, limited pre-defined modalities have restricted the flexibility of available omics data, and the ability to incorporate different types of features of existing methods is still insufficient, which both decrease the power in knowledge discovery. Considering these aspects, advanced data mining, statistical and machine learning methods are urgently needed to perform cross-modal data integration and modeling.
In the first volume, we collected insights on statistical methods and applications for integrating multimodal omics data, machine learning methods of feature representation for multimodal omics, database and web tools for depositing and visualizing disease-related knowledge discovered based on multimodal omics, as well as identification of molecular biomarkers for complex diseases.
With this Volume II Research Topic we aim to build on the knowledge acquired in the first volume. We aim to further investigate the computational methods integrating multimodal data for disease and other traits exploration. Especially, we want to expand the scope of the investigated data modalities. We also would like to attract studies using Electroencephalography (EEG) and functional Magnetic Resonance Imaging (fMRI) to investigate neurodegenerative diseases, affective disorders, cognitive disorders and emotion recognition.
Topics of interest include but are not limited to:
(1) Computational methods investigating neurodegenerative diseases and other brain disorders based on EEG/fMRI data.
(2) Affective computing based on EEG/fMRI data.
(3) Methods and discoveries based on the integration of EEG/fMRI data and omics data.
(4) Statistical methods and applications for integrating multimodal omics data.
(5) Machine learning methods of feature representation for multimodal omics.
(6) Graph-based deep learning methods for disease-related node/linkage prediction.
(7) Identification of molecular biomarkers for complex diseases.
(8) Disease-related module identification and validation through integrating multimodal omics data.
(9) Database and web tools for depositing and visualizing disease-related knowledge discovered based on multimodal omics.
(10) Causal inference between exposure factors and important traits or diseases based statistical methods and machine learning methods.
