The Journal of Data Mining in Genomics and Proteomics is one of the leading open access journals in scientific publishing, aiming to publish the most complete and authoritative source of information on discoveries and current developments.

Journal of Data Mining in Genomics & Proteomics (JDMGP) covers all areas related to genomic data warehousing, genomic data mining, genomic and proteomics data services, data mining applications in genomics, data proteomics mining applications, proteomics data warehousing quickly publish articles. , data warehousing, drug discovery data mining, statistical data mining, data algorithms, data modeling and intelligence, data mining tools, comparative proteomics, proteogenomics, metagenomics, comparative genomics, molecular modeling, genome mapping, cluster analysis, computational drug design , Genome Annotation.

Human imaging genetics has emerged as a powerful strategy for understanding the molecular basis of macroscopic neural phenotypes measured across the entire brain over the last two decades. This work has traditionally involved correlating allelic variation at one or more genetic loci with variation in one or more Imaging-Derived Phenotypes (IDPs), initially through candidate gene studies and more recently at the genome-wide level.

 The formation of large consortia, such as Enhancing Neuro Imaging Genetics through Meta-Analysis (ENIGMA), has aided the latter. A common assumption in this work is that variants associated with an IDP (or nearby variants tagged by the associated variant) influence gene expression or protein abundance, which affects cellular function and, ultimately, the studied IDP. However, a variety of environmental and other factors can influence gene activity, and the functional roles of many IDP-linked variants, which are typically discovered through large-scale statistical analyses, are frequently unknown. As a result, the mechanisms by which a particular variant may influence phenotypic variation are not always clear. Furthermore, the expression levels of many genes vary significantly across brain regions, and these spatial differences cannot be inferred solely from DNA sequence.

 Gene expression assays provide a more direct measure of gene function. Because expression assays are invasive and require direct access to neural tissue, studies of gene expression in the brain have historically been limited to small groups of areas studied in isolation. These constraints have recently been overcome by developments in high-throughput tissue processing and bioinformatics pipelines, leading to datasets of gene expression over a significant portion of the genome in numerous brain regions and at different phases of development. Only the Allen Human Brain Atlas (AHBA), which includes expression measurements for more than 20,000 genes obtained from 3702 geographically different tissue samples, offers high resolution coverage of virtually the entire brain, whereas several human atlases cover multiple brain regions.

Importantly, the samples' stereotaxic mapping enables researchers to directly link spatial changes in gene expression to spatial variations in IDP. The emerging field of imaging transcriptomics has emerged as a result of this unprecedented ability to connect molecular function to large-scale brain organization. This field has begun to reveal new information about how regional differences in gene expression relate to functional connectivity within canonical resting-state networks, fiber tract connectivity between brain regions, temporal and topological properties of large-scale brain functional networks, and the specialization of cortical and subcortical networks.