About this Research Topic
With the rapid development of experimental technologies, it is possible to obtain multiple modalities or multiple-omics data in individual studies. For analyzing cognitive and affective disorders, several types of data could be used including Electroencephalography (EEG), functional Magnetic Resonance Imaging (fMRI), genomics, phenomics, transcriptomics, radiomics, and single-cell omics. To extract knowledge from multimodal omics data, data mining methods, such as statistical methods, unsupervised learning, supervised learning, and network-based methods, have become increasingly popular recently. These data and methods provide a chance for knowledge discovery in cognitive disorders and affective disorders, especially the interaction between cognitive disorders and affective disorders.
This research topic will provide a platform for researchers from different research fields to share advanced data mining methods based on multimodal omics to enhance our understanding of cognitive and affective disorders, especially the relations between these two types of disorders. Topics of interest include but are not limited to:
(1) Knowledge discovery on the interaction between cognitive disorders and affective disorders.
(2) Machine learning methods for cognitive and affective multimodal data.
(3) Computational methods for modeling emotion dynamics in response to physiological signals.
(4) Biomarker identification for cognitive and affective disorders.
(5) Data mining methods of affective computing.
(6) Data mining methods for revealing the interaction and relationship between cognitive disorders and affective disorders.
(7) Tools for processing the affective and cognitive data.
(8) Identification of influence factors associated with cognitive disorders and affective disorders.
(9) The effect of Cognitive disorders or affective disorders to certain groups of people.
Keywords: Cognitive disorders, Affective disorders, EEG, fMRI, Data mining methods, Multimodal omics, Machine learning methods, Computational methods
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