Brain Imaging Genomics: Integrated Analysis and Machine Learning
Year: 2019
Type: article
Abstract: Brain imaging genomics is an emerging data science field, where integrated analysis of brain imaging and genomics data, often combined with other biomarker, clinical and environmental data, is performed to gain new insights into the phenotypic, genetic and molecular characteristics of the brain as well as their impact on normal and disordered brain function and behavior. It has enormous potential to contribute significantly to biomedical discoveries in brain science. Given the increasingly important role of statistical and machine learning in biomedicine and rapidly growing literature in brain imaging genomics, we provide an up-to-date and comprehensive review of statistical and machine learning methods for brain imaging genomics, as well as a practical discussion on method selection for various biomedical applications.
Source: Proceedings of the IEEE
Authors: Li Shen, Paul M. Thompson
Institutions: University of Pennsylvania, University of Southern California
Language: English
Subfield: Cognitive Neuroscience
Field: Neuroscience
Domain: Life Sciences
Open Access status: hybrid
Estimated APC paid: $2,045
Awards: G104586243, G1067818806, Bioinformatics Strategies for Multidimensional Brain Imaging Genetics, ENIGMA-SD: Understanding Sex Differences in Global Mental Health through ENIGMA, ENIGMA World Aging Center, Alzheimer's Disease Neuroimaging Initiative, G3146424788, Integrative Bioinformatics Approaches to Human Brain Genomics and Connectomics, Highly-sensitive imaging markers for early detection of Alzheimer's Disease using multi-view connectomics, ENIGMA Center for Worldwide Medicine, Imaging & Genomics, BIGDATA: IA: Collaborative Research: Asynchronous Distributed Machine Learning Framework for Multi-Site Collaborative Brain Big Data Mining, G6653948526, G7599342010, Growth factors, neuroinflammation, exercise, and brain integrity, Laboratory of Neuro Imaging Resource (LONIR), G8301434335, Multi-Source Sparse Learning to Identify MCI and Predict Decline, Advancing Analysis of Multi-omics Data in Alzheimer's Disease Research, G9000039018, G9000074849, G9000122335, G9000197713, G9000210308, ENIGMA World Aging Center, Genomic Architecture of Functional Brain Networks in PTSD, Perimenopause in Brain Aging and Alzheimer's Disease
OpenAlex ID: W2982638646