Core promoter short tandem repeats as evolutionary switch codes for primate speciation

Alteration in gene expression levels underlies many of the phenotypic differences across species. Because of their highly mutable nature, proximity to the +1 transcription start site (TSS), and the emerging evidence of functional impact on gene expression, core promoter short tandem repeats (STRs) may be considered an ideal source of variation across species. In a genome-scale analysis of the entire Homo sapiens protein-coding genes, we have previously identified core promoters with at least one STR of ≥6-repeats, with possible selective advantage in this species. In the current study, we performed reverse analysis of the entire Homo sapiens orthologous genes in mouse in the Ensembl database, in order to identify conserved STRs that have shrunk as an evolutionary advantage to humans. Two protocols were used to minimize ascertainment bias. Firstly, two species sharing a more recent ancestor with Homo sapiens (i.e. Pan troglodytes and Gorilla gorilla gorilla) were also included in the study. Secondly, four non-primate species encompassing the major orders across Mammals, including Scandentia, Laurasiatheria, Afrotheria, and Xenarthra were analyzed as out-groups. We introduce STR evolutionary events specifically identical in primates (i.e. Homo sapiens, Pan troglodytes, and Gorilla gorilla gorilla) vs. non-primate out-groups. The average frequency of the identically shared STR motifs across those primates ranged between 0.00005 and 0.06. The identified genes are involved in important evolutionary and developmental processes, such as normal craniofacial development (TFAP2B), regulation of cell shape (PALMD), learning and long-term memory (RGS14), nervous system development (GFRA2), embryonic limb morphogenesis (PBX2), and forebrain development (APAF1). We provide evidence of core promoter STRs as evolutionary switch codes for primate speciation, and the first instance of identity-by-descent for those motifs at the interspecies level. © 2014 Wiley Periodicals, Inc

RMDL: Random Multimodel Deep Learning for Classification

The continually increasing number of complex datasets each year necessitates ever improving machine learning methods for robust and accurate categorization of these data. This paper introduces Random Multimodel Deep Learning (RMDL): a new ensemble, deep learning approach for classification. Deep learning models have achieved state-of-the-art results across many domains. RMDL solves the problem of finding the best deep learning structure and architecture while simultaneously improving robustness and accuracy through ensembles of deep learning architectures. RDML can accept as input a variety data to include text, video, images, and symbolic. This paper describes RMDL and shows test results for image and text data including MNIST, CIFAR-10, WOS, Reuters, IMDB, and 20newsgroup. These test results show that RDML produces consistently better performance than standard methods over a broad range of data types and classification problems.Comment: Best Paper award ACM ICISD