Using Unsupervised Patterns to Extract Gene Regulation Relationships for Network Construction

BACKGROUND: The gene expression is usually described in the literature as a transcription factor X that regulates the target gene Y. Previously, some studies discovered gene regulations by using information from the biomedical literature and most of them require effort of human annotators to build the training dataset. Moreover, the large amount of textual knowledge recorded in the biomedical literature grows very rapidly, and the creation of manual patterns from literatures becomes more difficult. There is an increasing need to automate the process of establishing patterns. METHODOLOGY/PRINCIPAL FINDINGS: In this article, we describe an unsupervised pattern generation method called AutoPat. It is a gene expression mining system that can generate unsupervised patterns automatically from a given set of seed patterns. The high scalability and low maintenance cost of the unsupervised patterns could help our system to extract gene expression from PubMed abstracts more precisely and effectively. CONCLUSIONS/SIGNIFICANCE: Experiments on several regulators show reasonable precision and recall rates which validate AutoPat's practical applicability. The conducted regulation networks could also be built precisely and effectively. The system in this study is available at http://ikmbio.csie.ncku.edu.tw/AutoPat/

The genetic architecture of the human cerebral cortex

The cerebral cortex underlies our complex cognitive capabilities, yet little is known about the specific genetic loci that influence human cortical structure. To identify genetic variants that affect cortical structure, we conducted a genome-wide association meta-analysis of brain magnetic resonance imaging data from 51,665 individuals. We analyzed the surface area and average thickness of the whole cortex and 34 regions with known functional specializations. We identified 199 significant loci and found significant enrichment for loci influencing total surface area within regulatory elements that are active during prenatal cortical development, supporting the radial unit hypothesis. Loci that affect regional surface area cluster near genes in Wnt signaling pathways, which influence progenitor expansion and areal identity. Variation in cortical structure is genetically correlated with cognitive function, Parkinson's disease, insomnia, depression, neuroticism, and attention deficit hyperactivity disorder

The high frequency of TF-TG relationships in HIF-1 global network.

<p>The high frequency of TF-TG relationships in HIF-1 global network.</p

The extraction precision of different thresholds.

<p>The extraction precision of different thresholds.</p

Top-K Precision of AutoPat.

<p>Note that the precision rates of baseline “TF-KV-TG” for AP1 and E2F1 are 48.1 and 53.3.</p

The performance comparison with Textpresso.

<p>The performance comparison with Textpresso.</p

The global network of HIF-1 TF.

<p>The global network of HIF-1 TF.</p

The overall architecture of AutoPat.

<p>The overall architecture of AutoPat.</p

The list of extraction results of HIF-1 TF pathway in PID.

<p> <i>P: Precision, R: Recall, the bold-faced target gene means this TG can be extracted in only one method.</i></p

The example of an indirect relationship between HIF-1 and <i>p53</i>.

<p>The example of an indirect relationship between HIF-1 and <i>p53</i>.</p