Christopher Gaiteri

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The genetics of complex disease produce alterations in the molecular interactions of cellular pathways whose collective effect may become clear through the organized structure of molecular networks. To characterize molecular systems associated with late-onset Alzheimer's disease (LOAD), we constructed gene-regulatory networks in 1,647 postmortem brain(More)
In a research environment dominated by reductionist approaches to brain disease mechanisms, gene network analysis provides a complementary framework in which to tackle the complex dysregulations that occur in neuropsychiatric and other neurological disorders. Gene-gene expression correlations are a common source of molecular networks because they can be(More)
Coordinated gene transcript levels across tissues (denoted "gene synchrony") reflect converging influences of genetic, biochemical and environmental factors; hence they are informative of the biological state of an individual. So could brain gene synchrony also integrate the multiple factors engaged in neuropsychiatric disorders and reveal underlying(More)
Network analysis of positional candidate genes of schizophrenia highlightsymore thany myelin-related pathways Using a selection of positional susceptibility genes with high 'genetic potential' (i.e., heritability) and information obtained from peripheral gene expression , Rietkerk et al., 1 reported in the April issue of Molecular Psychiatry different(More)
The structure of gene coexpression networks reflects the activation and interaction of multiple cellular systems. Since the pathology of neuropsychiatric disorders is influenced by diverse cellular systems and pathways, we investigated gene coexpression networks in major depression, and searched for putative unifying themes in network connectivity across(More)
Biological functions are carried out by groups of interacting molecules, cells or tissues, known as communities. Membership in these communities may overlap when biological components are involved in multiple functions. However, traditional clustering methods detect non-overlapping communities. These detected communities may also be unstable and difficult(More)
Biological functions are often realized by groups of interacting molecules or cells. Membership in these groups may overlap when molecules or cells are reused in multiple functions. Traditional clustering methods assign components to no more than one group, and cannot identify multi-community nodes. Technical noise is common in high-throughput biological(More)
Physical interactions among molecules, cells, and tissues influence research in biology. While conferences and departments are created to study these interactions, previous attempts to understand the large-scale organization of science have only focused on social relationships among scientists. Here, we combine the structure of molecular interaction(More)
The value of research containing novel combinations of molecules can be seen in many innovative and award-winning research programs. Despite calls to use innovative approaches to address common diseases, an increasing majority of research funding goes toward " safe " incremental research. Counteracting this trend by nurturing novel and potentially(More)
Reconstructing biological networks using high-throughput technologies has the potential to produce condition-specific interactomes. But are these reconstructed networks a reliable source of biological interactions? Do some network inference methods offer dramatically improved performance on certain types of networks? To facilitate the use of network(More)
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