A New Multi-Layers Method to Analyze Gene Expression

In the paper a new Multi-Layers approach called Multi-Layers Model MLM) for the analysis of stochastic signals and its application to the analysis of gene expression data is presented. It consists in the generation of sub-samples from the input signal by applying a threshold technique based on cut-set optimal conditions. The MLM has been applied on synthetic and real microarray data for the identification of particular regions across DNA called nucleosomes and linkers. Nucleosomes are the fundamental repeating subunits of all eukaryotic chromatin, and their positioning provides useful information regarding the regulation of gene expression in eukaryotic cells. Results have shown a good recognition rate on synthetic data, moreover, the MLM shows a good agreement with a recently published method based on Hidden Markov Model when tested on the Saccharomyces cerevisiae chromosomes microarray data

DeepBrain: Functional Representation of Neural In-Situ Hybridization Images for Gene Ontology Classification Using Deep Convolutional Autoencoders

This paper presents a novel deep learning-based method for learning a functional representation of mammalian neural images. The method uses a deep convolutional denoising autoencoder (CDAE) for generating an invariant, compact representation of in situ hybridization (ISH) images. While most existing methods for bio-imaging analysis were not developed to handle images with highly complex anatomical structures, the results presented in this paper show that functional representation extracted by CDAE can help learn features of functional gene ontology categories for their classification in a highly accurate manner. Using this CDAE representation, our method outperforms the previous state-of-the-art classification rate, by improving the average AUC from 0.92 to 0.98, i.e., achieving 75% reduction in error. The method operates on input images that were downsampled significantly with respect to the original ones to make it computationally feasible

Alterations of FHIT and P53 genes in keratocystic odontogenic tumor, dentigerous and radicular cyst

BACKGROUND: The purpose of this study was to determine fragile histidine triad (FHIT) and p53 protein expression, and to analyze FHIT and p53 gene status in keratocystic odontogenic tumor (KOT), dentigerous cysts (DC) and radicular cysts (RC). ----- METHODS: The methods used were immunohistochemistry and molecular genetic methods including loss of heterozygosity (LOH) and gene sequencing. ----- RESULTS: FHIT protein expression was different among groups. Aberrant expression was the highest in KOT, then in RC and DC. p53 protein expression was different among groups. LOH in paraffin-embedded specimens was detected in 22.6% and 12.9% for FHIT and p53 respectively. Mutation of p53 gene at codon 237 was observed in only two specimens (one KOT and one DC). Of the six frozen specimens, three exhibited FHIT gene LOH (two RC and one KOT). KOT showed loss of exons 6-7 at FHIT locus and mutation at codon 237 at p53 locus, but this could be a chance result. ----- CONCLUSION: Aberrations of FHIT and p53 genes/proteins could be considered markers responsible for the development of odontogenic lesions

The INCF Digital Atlasing Program: Report on Digital Atlasing Standards in the Rodent Brain

The goal of the INCF Digital Atlasing Program is to provide the vision and direction necessary to make the rapidly growing collection of multidimensional data of the rodent brain (images, gene expression, etc.) widely accessible and usable to the international research community. This Digital Brain Atlasing Standards Task Force was formed in May 2008 to investigate the state of rodent brain digital atlasing, and formulate standards, guidelines, and policy recommendations.

Our first objective has been the preparation of a detailed document that includes the vision and specific description of an infrastructure, systems and methods capable of serving the scientific goals of the community, as well as practical issues for achieving
the goals. This report builds on the 1st INCF Workshop on Mouse and Rat Brain Digital Atlasing Systems (Boline et al., 2007, _Nature Preceedings_, doi:10.1038/npre.2007.1046.1) and includes a more detailed analysis of both the current state and desired state of digital atlasing along with specific recommendations for achieving these goals