The heatmap of the high frequency genes and the tumor/normal samples.

<p>The green bars indicate the tumor samples and the blue bars indicate the normal samples. The tumor and normal samples were clearly differentiated by the high frequency genes.</p

Dysfunctions Associated with Methylation, MicroRNA Expression and Gene Expression in Lung Cancer

<div><p>Integrating high-throughput data obtained from different molecular levels is essential for understanding the mechanisms of complex diseases such as cancer. In this study, we integrated the methylation, microRNA and mRNA data from lung cancer tissues and normal lung tissues using functional gene sets. For each Gene Ontology (GO) term, three sets were defined: the methylation set, the microRNA set and the mRNA set. The discriminating ability of each gene set was represented by the Matthews correlation coefficient (MCC), as evaluated by leave-one-out cross-validation (LOOCV). Next, the MCCs in the methylation sets, the microRNA sets and the mRNA sets were ranked. By comparing the MCC ranks of methylation, microRNA and mRNA for each GO term, we classified the GO sets into six groups and identified the dysfunctional methylation, microRNA and mRNA gene sets in lung cancer. Our results provide a systematic view of the functional alterations during tumorigenesis that may help to elucidate the mechanisms of lung cancer and lead to improved treatments for patients.</p> </div

The work flow of dysfunctional methylation, microRNA and mRNA gene set analysis.

<p>First, for each Gene Ontology (GO) term, we defined three gene sets: the methylation set, the microRNA set and the mRNA set. Next, we calculated the Matthews's correlation coefficient (MCC), as evaluated by leave-one-out cross-validation (LOOCV), for each gene set. Next, we ranked the MCCs in the methylation sets, the microRNA sets and the mRNA sets, and we compared the MCC ranks of methylation, microRNA and mRNA for each Gene Ontology (GO) term and classified the GO sets into six groups. Finally, we identified the dysfunctional methylation, microRNA and mRNA gene sets in lung cancer.</p

The high frequency genes and microRNAs of the KEGG pathway “hsa05223 Non-small cell lung cancer”.

<p>The green nodes denote high frequency microRNAs. The red nodes denote high frequency genes in both methylation and mRNA dysfunctional sets. The yellow nodes indicate high frequency genes in mRNA dysfunctional sets only. There is no specific high frequency gene in methylation dysfunctional sets. The white nodes indicate non-high frequency genes. The black edges show interactions from the KEGG pathway “hsa05223 Non-small cell lung cancer”. The green edges show regulation by high frequency microRNAs on their target genes.</p

The MCC boxplot of methylation, microRNA and mRNA gene sets.

<p>The mean MCCs of the mRNA, microRNA and methylation gene sets were 0.897, 0.702 and 0.561, respectively. The MCCs of the mRNA sets were significantly greater than the MCCs of the microRNA sets with a one-sided t-test p-value of less than 2.2e-16, and the MCCs of the microRNA sets were, in turn, significantly greater than the MCCs of the methylation sets with a one-sided t-test p-value of less than 2.2e-16.</p

Predicting A-to-I RNA Editing by Feature Selection and Random Forest

<div><p>RNA editing is a post-transcriptional RNA process that provides RNA and protein complexity for regulating gene expression in eukaryotes. It is challenging to predict RNA editing by computational methods. In this study, we developed a novel method to predict RNA editing based on a random forest method. A careful feature selection procedure was performed based on the Maximum Relevance Minimum Redundancy (mRMR) and Incremental Feature Selection (IFS) algorithms. Eighteen optimal features were selected from the 77 features in our dataset and used to construct a final predictor. The accuracy and <i>MCC</i> (Matthews correlation coefficient) values for the training dataset were 0.866 and 0.742, respectively; for the testing dataset, the accuracy and <i>MCC</i> were 0.876 and 0.576, respectively. The performance was higher using 18 features than all 77, suggesting that a small feature set was sufficient to achieve accurate prediction. Analysis of the 18 features was performed and may shed light on the mechanism and dominant factors of RNA editing, providing a basis for future experimental validation.</p></div

Analysis of Tumor Suppressor Genes Based on Gene Ontology and the KEGG Pathway

<div><p>Cancer is a serious disease that causes many deaths every year. We urgently need to design effective treatments to cure this disease. Tumor suppressor genes (TSGs) are a type of gene that can protect cells from becoming cancerous. In view of this, correct identification of TSGs is an alternative method for identifying effective cancer therapies. In this study, we performed gene ontology (GO) and pathway enrichment analysis of the TSGs and non-TSGs. Some popular feature selection methods, including minimum redundancy maximum relevance (mRMR) and incremental feature selection (IFS), were employed to analyze the enrichment features. Accordingly, some GO terms and KEGG pathways, such as biological adhesion, cell cycle control, genomic stability maintenance and cell death regulation, were extracted, which are important factors for identifying TSGs. We hope these findings can help in building effective prediction methods for identifying TSGs and thereby, promoting the discovery of effective cancer treatments.</p></div

Discovery of New Candidate Genes Related to Brain Development Using Protein Interaction Information

<div><p>Human brain development is a dramatic process composed of a series of complex and fine-tuned spatiotemporal gene expressions. A good comprehension of this process can assist us in developing the potential of our brain. However, we have only limited knowledge about the genes and gene functions that are involved in this biological process. Therefore, a substantial demand remains to discover new brain development-related genes and identify their biological functions. In this study, we aimed to discover new brain-development related genes by building a computational method. We referred to a series of computational methods used to discover new disease-related genes and developed a similar method. In this method, the shortest path algorithm was executed on a weighted graph that was constructed using protein-protein interactions. New candidate genes fell on at least one of the shortest paths connecting two known genes that are related to brain development. A randomization test was then adopted to filter positive discoveries. Of the final identified genes, several have been reported to be associated with brain development, indicating the effectiveness of the method, whereas several of the others may have potential roles in brain development.</p></div

The prediction performance of the final model using 18 features, by 10-fold cross validation.

<p>The prediction performance of the final model using 18 features, by 10-fold cross validation.</p

The pedigree and haplotype analysis of the Chinese congenital nystagmus subject family

The proband is marked with an arrow. Eight markers are listed from top to bottom: telomere - DXS6807 - DXS7103 - DXS9902 - DXS9896 - GATA186D06 - DXS8015 - DXS6810 - DXS8035 - centromere. Blackened regions of haplotypes indicate linkage between the markers and the disease. Question marks indicate that the genotype is not determined.<p><b>Copyright information:</b></p><p>Taken from "Identification of a novel deletion in a Chinese family with X-linked congenital nystagmus"</p><p></p><p>Molecular Vision 2008;14():1015-1019.</p><p>Published online 30 May 2008</p><p>PMCID:PMC2408774.</p><p></p