Biomedical subjects

Kang Tu

Publications and source records attributed to Kang Tu.

7 recordsLinked to original sources

PubMed2006

Systematic analysis of head-to-head gene organization: evolutionary conservation and potential biological relevance.

Several "head-to-head" (or "bidirectional") gene pairs have been studied in individual experiments, but genome-wide analysis of this gene organization, especially in terms of transcriptional correlation and functional association, is still insufficient. We conducted a systematic investigation of head-to-head gene organization focusing on structural features, evolutionary conservation, expression correlation and functional association. Of the present 1,262, 1,071, and 491 head-to-head pairs identified in human, mouse, and rat genomes, respectively, pairs with 1- to 400-base pair distance between transcription start sites form the majority (62.36%, 64.15%, and 55.19% for human, mouse, and rat,respectively) of each dataset, and the largest group is always the one with a transcription start site distance of 101 to 200 base pairs. The phylogenetic analysis among Fugu, chicken, and human indicates a negative selection on the separation of head-to-head genes across vertebrate evolution, and thus the ancestral existence of this gene organization. The expression analysis shows that most of the human head-to-head genes are significantly correlated,and the correlation could be positive, negative, or alternative depending on the experimental conditions. Finally, head to-head genes statistically tend to perform similar functions, and gene pairs associated with the significant cofunctions seem to have stronger expression correlations. The findings indicate that the head-to-head gene organization is ancient and conserved, which subjects functionally related genes to correlated transcriptional regulation and thus provides an exquisite mechanism of transcriptional regulation based on gene organization. These results have significantly expanded the knowledge about head-to-head gene organization. Supplementary materials for this study are available at http://www.scbit.org/h2h.

Animals↗

PubMed2006

Combining gene expression profiles and protein-protein interaction data to infer gene functions.

The ever-increasing flow of gene expression profiles and protein-protein interactions has catalyzed many computational approaches for inference of gene functions. Despite all the efforts, there is still room for improvement, for the information enriched in each biological data source has not been exploited to its fullness. A composite method is proposed for classifying unannotated genes based on expression data and protein-protein interaction (PPI) data, which extracts information from both data sources in novel ways. With the noise nature of expression data taken into consideration, importance is attached to the consensus expression patterns of gene classes instead of the actual expression profiles of individual genes, thus characterizing the composite method with enhanced robustness against microarray data variation. With regard to the PPI network, the traditional clear-cut binary attitude towards inter- and intra-functional interactions is abandoned, whereas a more objective perspective into the PPI network structure is formed through incorporating the varied function-function interaction probabilities into the algorithm. The composite method was implemented in two numerical experiments, where its improvement over single-data-source based methods was observed and the superiority of the novel data handling operations was discussed.

Algorithms↗

PubMed2005

Broadly predicting specific gene functions with expression similarity and taxonomy similarity.

Previous studies on computational gene functional prediction have not fully exploited the taxonomy structure of Gene Ontology (GO). They just select a few classes from GO into a set, and conduct classwise learning of these classes. The pre-selection of learning classes, often done according to the annotation sizes, limits the prediction breadth and depth. This way of pre-selecting learning classes ignores the taxonomy relations among classes, and so wastes the valuable functional knowledge encoded in the DAG structure of GO. This paper proposes GESTS, a novel gene functional prediction approach based on both gene expression similarity and GO taxonomy similarity, which circumvents the problem of arbitrary learning class pre-selection. GESTS is a semi-supervised approach that reasonably and efficiently incorporates the ontology-formed gene functional knowledge into automated functional analyses of local gene clustering. By integrating both expression similarity and taxonomy similarity into the learning process, GESTS achieves better prediction breadth, depth, and precision than previous studies on the fibroblast serum response dataset and the yeast expression dataset.

Computational Biology↗

PubMed2005

MEGO: gene functional module expression based on gene ontology.

Existing analysis tools to study the collective properties of gene functional modules cannot return highly homogeneous modules and do not provide quantitative measures of module activity level. By partitioning genes according to multiple gene functional categorization principles and summarizing gene expression values into module expression values, MEGO (module expression based on gene ontology), a standalone microarray data analysis program, is able to extract highly activated gene functional modules that are of much interest to microarray experimenters. With multiple functional categorization principles simultaneously introduced in MEGO, the partition of genes is more delicate, and the collective property of a group of genes is sharpened and easier to capture. The quantitative measures of module activity levels returned by MEGO give users a quick impression of the direction and degree of module regulation. MEGO efficiently determines the answers to frequently asked questions, such as which functional classes have been induced or repressed under a specific experiment and to which levels these functional classes have been affected. MEGO is available free of charge for academic use and may be downloaded from http://www.dxy.cn/mego/MEGOInstall.EXE. Supplementary information can be found on the authors' web page at http://www.dxy.cn/mego/ and at the BioTechniques' web site at http://www. BioTechniques.com/February2005/TuSupplementary.html.

Algorithms↗

PubMed2005

Applying informatics in tissue engineering.

OBJECTIVE: To facilitate tissue engineering strategies determination with informatics tools. METHODS: Firstly, tissue engineering experimental data were standardized and integrated into a centralized database; secondly, we used data mining tools (e.g. artificial neural networks and decision trees) to predict the outcomes of tissue engineering strategies; thirdly, a strategy design algorithm was developed, and its efficacy was validated with animal experiments; lastly, we constructed an online database and a decision support system for tissue engineering. RESULTS: The artificial neural networks and the decision trees respectively predicted the outcomes of tissue engineering strategies with the predictive accuracy of 95.14% and 85.26%. Following the strategies generated by computer, we cured 18 of the 20 experimental animals with a significantly lower cost than usual. CONCLUSION: Informatics is beneficial for realizing safe, effective and economical tissue engineering.

Artificial Intelligence↗

PubMed2004

Learnability-based further prediction of gene functions in Gene Ontology.

Currently the functional annotations of many genes are not specific enough, limiting their further application in biology and medicine. It is necessary to push the gene functional annotations deeper in Gene Ontology (GO), or to predict further annotated genes with more specific GO terms. A framework of learnability-based further prediction of gene functions in GO is proposed in this paper. Local classifiers are constructed in local classification spaces rooted at qualified parent nodes in GO, and their classification performances are evaluated with the averaged Tanimoto index (ATI). Classification spaces with higher ATIs are selected out, and genes annotated only to the parent classes are predicted to child classes. Through learnability-based further predicting, the functional annotations of annotated genes are made more specific. Experiments on the fibroblast serum response dataset reported further functional predictions for several human genes and also gave interesting clues to the varied learnability between classes of different GO ontologies, different levels, and different numbers of child classes.

Computational Biology↗

PubMed2004

[Analysis of the mRNA expression similarity of genes in the same gene expression regulatory pathway].

In this work we analysed the relationship of gene expression from the point of view of gene expression regulatory pathway. Using seven sets of Saccharomyces cerevisiae gene chip expression profile data, and information from two pathway database (KEGG and CYGD), we analysed the mRNA expression similarity of genes in the same gene expression regulatory pathway by Genehub software, that involved totally 16 pathways with more than 495 genes. From the calculation of two different similarity measures-Pearson correlation coefficient and Spearman correlation coefficient, we found that about 94 percent of gene expression regulatory pathways are correlatively expressed in more than 4(including 4) sets of expression profile data, and it presents additional evidence for the correlation between gene function and its expression in the view of gene expression regulatory pathways.

Algorithms↗