869 research outputs found
Learn to Generate Time Series Conditioned Graphs with Generative Adversarial Nets
Deep learning based approaches have been utilized to model and generate
graphs subjected to different distributions recently. However, they are
typically unsupervised learning based and unconditioned generative models or
simply conditioned on the graph-level contexts, which are not associated with
rich semantic node-level contexts. Differently, in this paper, we are
interested in a novel problem named Time Series Conditioned Graph Generation:
given an input multivariate time series, we aim to infer a target relation
graph modeling the underlying interrelationships between time series with each
node corresponding to each time series. For example, we can study the
interrelationships between genes in a gene regulatory network of a certain
disease conditioned on their gene expression data recorded as time series. To
achieve this, we propose a novel Time Series conditioned Graph
Generation-Generative Adversarial Networks (TSGG-GAN) to handle challenges of
rich node-level context structures conditioning and measuring similarities
directly between graphs and time series. Extensive experiments on synthetic and
real-word gene regulatory networks datasets demonstrate the effectiveness and
generalizability of the proposed TSGG-GAN
The phenotype paradox: lessons from natural transcriptome evolution on how to engineer plants
Plants have evolved genome complexity through iterative rounds of single gene and whole genome duplication. This has led to substantial expansion in transcription factor numbers following preferential retention and subsequent functional divergence of these regulatory genes. Here we review how this simple evolutionary network rewiring process, regulatory gene duplication followed by functional divergence, can be used to inspire synthetic biology approaches that seek to develop novel phenotypic variation for future trait based breeding programs in plants
Genes and Gene Networks Related to Age-associated Learning Impairments
The incidence of cognitive impairments, including age-associated spatial learning impairment (ASLI), has risen dramatically in past decades due to increasing human longevity. To better understand the genes and gene networks involved in ASLI, data from a number of past gene expression microarray studies in rats are integrated and used to perform a meta- and network analysis. Results from the data selection and preprocessing steps show that for effective downstream analysis to take place both batch effects and outlier samples must be properly removed. The meta-analysis undertaken in this research has identified significant differentially expressed genes across both age and ASLI in rats. Knowledge based gene network analysis shows that these genes affect many key functions and pathways in aged compared to young rats. The resulting changes might manifest as various neurodegenerative diseases/disorders or syndromic memory impairments at old age. Other changes might result in altered synaptic plasticity, thereby leading to normal, non-syndromic learning impairments such as ASLI.
Next, I employ the weighted gene co-expression network analysis (WGCNA) on the datasets. I identify several reproducible network modules each highly significant with genes functioning in specific biological functional categories. It identifies a “learning and memory” specific module containing many potential key ASLI hub genes. Functions of these ASLI hub genes link a different set of mechanisms to learning and memory formation, which meta-analysis was unable to detect. This study generates some new hypotheses related to the new candidate genes and networks in ASLI, which could be investigated through future research
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