Ontology-Based Meta-Analysis of Global Collections of High-Throughput Public Data

The investigation of the interconnections between the molecular and genetic events that govern biological systems is essential if we are to understand the development of disease and design effective novel treatments. Microarray and next-generation sequencing technologies have the potential to provide this information. However, taking full advantage of these approaches requires that biological connections be made across large quantities of highly heterogeneous genomic datasets. Leveraging the increasingly huge quantities of genomic data in the public domain is fast becoming one of the key challenges in the research community today.We have developed a novel data mining framework that enables researchers to use this growing collection of public high-throughput data to investigate any set of genes or proteins. The connectivity between molecular states across thousands of heterogeneous datasets from microarrays and other genomic platforms is determined through a combination of rank-based enrichment statistics, meta-analyses, and biomedical ontologies. We address data quality concerns through dataset replication and meta-analysis and ensure that the majority of the findings are derived using multiple lines of evidence. As an example of our strategy and the utility of this framework, we apply our data mining approach to explore the biology of brown fat within the context of the thousands of publicly available gene expression datasets.Our work presents a practical strategy for organizing, mining, and correlating global collections of large-scale genomic data to explore normal and disease biology. Using a hypothesis-free approach, we demonstrate how a data-driven analysis across very large collections of genomic data can reveal novel discoveries and evidence to support existing hypothesis

Lower touch sensibility in the extremities of healthy Indians: further deterioration with age

Touch sensibility testing is a cost-effective, psychophysical measure of peripheral nerve function and impairment. However, there is limited information regarding the natural variability in touch sensibility across different populations and different age groups. We studied 568 healthy Indian volunteers without any clinical evidence of peripheral nerve disease. Touch sensibility was evaluated bilaterally in palms, feet, and heels, using Semmes-Weinstein monofilaments, with target forces ranging from 0.008 to 300 g. No differences were observed between the right and the left limbs. The lowest target force detected ranged from 0.4 to 2 g in the palms and 1.4 to 15 g in the feet. These values showed further increase with age. Women compared with men had higher sensibility in the palms in most age groups. Touch sensibility thresholds recorded in a large group of Indians were higher than that reported in other populations. These findings have clinical implications for the diagnosis of early nerve impairment in the elderly and in disease states drawing attention to geographic variations in touch sensation

Multi-Level Multi-Perspective Reasoning

Creation of formal decision models involves selecting the set of relevant factors to consider and the level of detail at which to represent them. In decision modeling, abstraction facilitates reasoning at multiple levels of detail by focusing on the important decision parameters; abstraction also allows reasoning under time-constraints or with insufficient data to support full specification of the numerical parameters. Existing abstraction methods emphasize coarsening the decision models in the solution phase to reduce computational costs. Our abstraction approach aims to facilitate model creation in a general framework that supports multiple perspective, dynamic decision modeling. In this paper, we present the relevant abstraction mechanisms and formalize some major constraints on the different abstraction levels in the framework. We also demonstrate how the abstraction operations are applied in the decision modeling process

Identification of humoral immune responses in protein microarrays using DNA microarray data analysis techniques

Motivation: We present a study of antigen expression signals from a newly developed high-throughput protein microarray technique. These signals are a measure of antibody–antigen binding activity and provide a basis for understanding humoral immune responses to various infectious agents and supporting vaccine and diagnostic development. Results: We investigate the characteristics of these expression profiles and show that noise models, normalization, variance estimation and differential expression analysis techniques developed in the context of DNA microarray analysis can be adapted and applied to these protein arrays. Using a high-dimensional dataset containing measurements of expression profiles of antibody reactivity against each protein (295 antigens and 9 controls) in 42 malaria (Plasmodium falciparum) protein arrays derived from 22 donors with various clinical presentations of malaria, we present a methodology for the analysis and identification of significantly expressed antigens targeted by immune responses for individual sera, groups of sera and across stages of infection. We also conduct a short study highlighting the top immunoreactive antigens where we identify three novel high priority antigens for future evaluation

From protein microarrays to diagnostic antigen discovery: a study of the pathogen Francisella tularensis. Bioinformatics 23:i508–i518. Kalantari-Dehaghi et al. 4338 jvi.asm.org Journal of Virology o n Septem ber 18, 2016 by PENN STATE UNIV http://jvi.asm.

ABSTRACT Motivation: An important application of protein microarray data analysis is identifying a serodiagnostic antigen set that can reliably detect patterns and classify antigen expression profiles. This work addresses this problem using antibody responses to protein markers measured by a novel high-throughput microarray technology. The findings from this study have direct relevance to rapid, broad-based diagnostic and vaccine development. Results: Protein microarray chips are probed with sera from individuals infected with the bacteria Francisella tularensis, a category A biodefense pathogen. A two-step approach to the diagnostic process is presented (1) feature (antigen) selection and (2) classification using antigen response measurements obtained from F.tularensis microarrays (244 antigens, 46 infected and 54 healthy human sera measurements). To select antigens, a ranking scheme based on the identification of significant immune responses and differential expression analysis is described. Classification methods including k-nearest neighbors, support vector machines (SVM) and k-Means clustering are applied to training data using selected antigen sets of various sizes. SVM based models yield prediction accuracy rates in the range of $90% on validation data, when antigen set sizes are between 25 and 50. These results strongly indicate that the top-ranked antigens can be considered high-priority candidates for diagnostic development. Availability: All software programs are written in R and available a