The conference on computational methods in neurobiology, which was organized by Skoltech in December, brought together experts in lipidomics, neuroimaging genetics, and machine learning. The human brain is currently the subject of extensive research worldwide, which generates vast amounts of information of a varied nature, ranging from genetic data to medical images of the brain. The data produced are so abundant and complex that conventional approaches are not adequate to analyze them with sufficient depth or high quality. One of the priority targets for neuroscience is to develop novel computational methods of multimodal data analysis that would measure up to these tough challenges.
The conference opened with two presentations by Philipp Khaitovich’s group focusing on the blood plasma and brain tissue lipidomic data analysis; this aimed to identify brain diseases and related biological changes. Other speakers included Boris Gutman of the Illinois Institute of Technology (USA) and Gennady Roshchupkin of the Medical Center at the Erasmus University (the Netherlands). Both have spent the last several years developing new computational methods of data analysis as part of the research carried out by two major neuroimaging genetics consortia: Enhancing Neuro Imaging Genetics by Meta-Analysis (ENIGMA) and Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE). The concluding session featured presentations on machine learning by Skoltech researchers Maxim Sharaev and Mikhail Belyaev.
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