Showing papers by "Neil R. Smalheiser published in 2012"

MicroRNA expression is down-regulated and reorganized in prefrontal cortex of depressed suicide subjects

Abstract: BACKGROUND: Recent studies suggest that alterations in expression of genes, including those which regulate neural and structural plasticity, may be crucial in the pathogenesis of depression. MicroRNAs (miRNAs) are newly discovered regulators of gene expression that have recently been implicated in a variety of human diseases, including neuropsychiatric diseases. METHODOLOGY/PRINCIPAL FINDINGS: The present study was undertaken to examine whether the miRNA network is altered in the brain of depressed suicide subjects. Expression of miRNAs was measured in prefrontal cortex (Brodmann Area 9) of antidepressant-free depressed suicide (n = 18) and well-matched non-psychiatric control subjects (n = 17) using multiplex RT-PCR plates. We found that overall miRNA expression was significantly and globally down-regulated in prefrontal cortex of depressed suicide subjects. Using individual tests of statistical significance, 21 miRNAs were significantly decreased at p = 0.05 or better. Many of the down-regulated miRNAs were encoded at nearby chromosomal loci, shared motifs within the 5'-seeds, and shared putative mRNA targets, several of which have been implicated in depression. In addition, a set of 29 miRNAs, whose expression was not pairwise correlated in the normal controls, showed a high degree of co-regulation across individuals in the depressed suicide group. CONCLUSIONS/SIGNIFICANCE: The findings show widespread changes in miRNA expression that are likely to participate in pathogenesis of major depression and/or suicide. Further studies are needed to identify whether the miRNA changes lead to altered expression of prefrontal cortex mRNAs, either directly (by acting as miRNA targets) or indirectly (e.g., by affecting transcription factors). Language: en

Literature-based discovery: Beyond the ABCs

Abstract: Literature-based discovery (LBD) refers to a particular type of text mining that seeks to identify nontrivial assertions that are implicit, and not explicitly stated, and that are detected by juxtaposing (generally a large body of) documents. In this review, I will provide a brief overview of LBD, both past and present, and will propose some new directions for the next decade. The prevalent ABC model is not “wrong”; however, it is only one of several different types of models that can contribute to the development of the next generation of LBD tools. Perhaps the most urgent need is to develop a series of objective literature-based interestingness measures, which can customize the output of LBD systems for different types of scientific investigations. © 2012 Wiley Periodicals, Inc.

Primary microRNA precursor transcripts are localized at post-synaptic densities in adult mouse forebrain.

Abstract: In a previous study, we reported that microRNA (miRNA) precursors are expressed in synaptic fractions within adult mouse forebrain, where they are enriched at post-synaptic densities (PSDs). However, because that study employed qRT-PCR primers that recognize the hairpin region, it was not able to distinguish between primary microRNA gene transcripts (pri-miRs) and small hairpin precursors (pre-miRs). Here, using primer sets that selectively measure regions upstream, downstream and flanking the hairpin, we demonstrate that pri-miRs are present in synaptic fractions (enriched several-fold relative to total tissue homogenate) and are especially enriched in isolated PSDs. Drosha and DGCR8 proteins are also expressed in synaptic fractions and PSDs, and are tightly associated with pri-miRs as assessed by coimmunoprecipitation under stringent conditions. Pri-miRs, drosha, and DGCR8 are highly enriched in fractions that contain mRNA transport particles, and cytosolic drosha is associated with kinesin heavy chain; these findings suggest that pri-miRs are transported to synaptic regions in a manner similar to mRNAs. This study supports the notion that miRNA biogenesis occurs locally near synapses in a regulated fashion.

A framework for entity resolution with efficient blocking

Abstract: In applications of Web data integration, we frequently need to identify whether data objects in different data sources represent the same entity in the real world. This problem is known as entity resolution. In this paper, we propose a generic framework for entity resolution for relational data sets, called BARM, consisting of the Blocker, Attribute matchers and the Record Matcher. BARM is convenient for different blocking and matching algorithms to fit into it. For the blocker, we apply the SPectrAl Neighborhood (SPAN), a state-of-the-art blocking algorithm, to our data sets and show that SPAN is effective and efficient. For attribute matchers, we propose the Context Sensitive Value Matching Library (CSVML) for matching attribute values and also an approach to evaluate the goodness of matching functions. CSVML takes the meaning and context of attribute values into consideration and therefore has good performance, as shown in experimental results. We adopt Bayesian network as the record matcher in the framework and propose a method of inference from Bayesian network based on Markov blanket of the network. As a comparison, we also apply three other classifiers, including Decision Tree, Support Vector Machines, and the Naive Bayes classifier to our data sets. Experiments show that Bayesian network is advantageous in the book domain.

Advances in Information Science Literature-Based Discovery: Beyond the ABCs

Abstract: Literature-based discovery (LBD) refers to a particular type of text mining that seeks to identify nontrivial assertions that are implicit, and not explicitly stated, and that are detected by juxtaposing (generally a large body of) documents. In this review, I will provide a brief overview of LBD, both past and present, and will propose some new directions for the next decade. The prevalent ABC model is not “wrong”; however, it is only one of several different types of models that can contribute to the development of the next generation of LBD tools. Perhaps the most urgent need is to develop a series of objective literaturebased interestingness measures, which can customize the output of LBD systems for different types of scientific investigations.