Biomedical subjects

Byoung-Tak Zhang

Publications and source records attributed to Byoung-Tak Zhang.

13 recordsLinked to original sources

PubMed2006

miTarget: microRNA target gene prediction using a support vector machine.

BACKGROUND: MicroRNAs (miRNAs) are small noncoding RNAs, which play significant roles as posttranscriptional regulators. The functions of animal miRNAs are generally based on complementarity for their 5' components. Although several computational miRNA target-gene prediction methods have been proposed, they still have limitations in revealing actual target genes. RESULTS: We implemented miTarget, a support vector machine (SVM) classifier for miRNA target gene prediction. It uses a radial basis function kernel as a similarity measure for SVM features, categorized by structural, thermodynamic, and position-based features. The latter features are introduced in this study for the first time and reflect the mechanism of miRNA binding. The SVM classifier produces high performance with a biologically relevant data set obtained from the literature, compared with previous tools. We predicted significant functions for human miR-1, miR-124a, and miR-373 using Gene Ontology (GO) analysis and revealed the importance of pairing at positions 4, 5, and 6 in the 5' region of a miRNA from a feature selection experiment. We also provide a web interface for the program. CONCLUSION: miTarget is a reliable miRNA target gene prediction tool and is a successful application of an SVM classifier. Compared with previous tools, its predictions are meaningful by GO analysis and its performance can be improved given more training examples.

Algorithms↗

PubMed2006

Identification of regulatory modules by co-clustering latent variable models: stem cell differentiation.

MOTIVATION: An important issue in stem cell biology is to understand how to direct differentiation towards a specific cell type. To elucidate the mechanism, previous studies have focused on identifying the responsible gene regulators, which have, however, failed to provide a systemic view of regulatory modules. To obtain a unified description of the regulatory modules, we characterized major stem cell species by employing a co-clustering latent variable model (LVM). The LVM-based method allowed us to elucidate the cell type-specific transcription factors, using genomic sequences as well as expression profiles. RESULTS: We used a list of genes enriched in each of 21 stem cell subpopulations, and their upstream genomic sequences. The LVM-based study allowed us to uncover the regulatory modules for each stem cell cluster, e.g. GABP and E2F for the proliferation phase, and Ap2alpha and Ap2gamma for the quiescence phase. Furthermore, the identities of the stem cell clusters were well revealed by the constituent genes that were directly targeted by the modules. Consequently, our analytical framework was demonstrated to be useful through a detailed case study of stem cell differentiation and can be applied to problems with similar characteristics.

Algorithms↗

PubMed2006

ProMiR II: a web server for the probabilistic prediction of clustered, nonclustered, conserved and nonconserved microRNAs.

ProMiR is a web-based service for the prediction of potential microRNAs (miRNAs) in a query sequence of 60-150 nt, using a probabilistic colearning model. Identification of miRNAs requires a computational method to predict clustered and nonclustered, conserved and nonconserved miRNAs in various species. Here we present an improved version of ProMiR for identifying new clusters near known or unknown miRNAs. This new version, ProMiR II, integrates additional evidence, such as free energy data, G/C ratio, conservation score and entropy of candidate sequences, for more controllable prediction of miRNAs in mouse and human genomes. It also provides a wider range of services, e.g. the prediction of miRNA genes in long nonrelated sequences such as viral genomes. Importantly, we have validated this method using several case studies. All data used in ProMiR II are structured in the MySQL database for efficient analysis. The ProMiR II web server is available at http://cbit.snu.ac.kr/~ProMiR2/.

Animals↗

PubMed2006

Construction of phylogenetic trees by kernel-based comparative analysis of metabolic networks.

BACKGROUND: To infer the tree of life requires knowledge of the common characteristics of each species descended from a common ancestor as the measuring criteria and a method to calculate the distance between the resulting values of each measure. Conventional phylogenetic analysis based on genomic sequences provides information about the genetic relationships between different organisms. In contrast, comparative analysis of metabolic pathways in different organisms can yield insights into their functional relationships under different physiological conditions. However, evaluating the similarities or differences between metabolic networks is a computationally challenging problem, and systematic methods of doing this are desirable. Here we introduce a graph-kernel method for computing the similarity between metabolic networks in polynomial time, and use it to profile metabolic pathways and to construct phylogenetic trees. RESULTS: To compare the structures of metabolic networks in organisms, we adopted the exponential graph kernel, which is a kernel-based approach with a labeled graph that includes a label matrix and an adjacency matrix. To construct the phylogenetic trees, we used an unweighted pair-group method with arithmetic mean, i.e., a hierarchical clustering algorithm. We applied the kernel-based network profiling method in a comparative analysis of nine carbohydrate metabolic networks from 81 biological species encompassing Archaea, Eukaryota, and Eubacteria. The resulting phylogenetic hierarchies generally support the tripartite scheme of three domains rather than the two domains of prokaryotes and eukaryotes. CONCLUSION: By combining the kernel machines with metabolic information, the method infers the context of biosphere development that covers physiological events required for adaptation by genetic reconstruction. The results show that one may obtain a global view of the tree of life by comparing the metabolic pathway structures using meta-level information rather than sequence information. This method may yield further information about biological evolution, such as the history of horizontal transfer of each gene, by studying the detailed structure of the phylogenetic tree constructed by the kernel-based method.

Archaeal Proteins↗

PubMed2006

Molecular basis for the recognition of primary microRNAs by the Drosha-DGCR8 complex.

The Drosha-DGCR8 complex initiates microRNA maturation by precise cleavage of the stem loops that are embedded in primary transcripts (pri-miRNAs). Here we propose a model for this process that is based upon evidence from both computational and biochemical analyses. A typical metazoan pri-miRNA consists of a stem of approximately 33 bp, with a terminal loop and flanking segments. The terminal loop is unessential, whereas the flanking ssRNA segments are critical for processing. The cleavage site is determined mainly by the distance (approximately 11 bp) from the stem-ssRNA junction. Purified DGCR8, but not Drosha, interacts with pri-miRNAs both directly and specifically, and the flanking ssRNA segments are vital for this binding to occur. Thus, DGCR8 may function as the molecular anchor that measures the distance from the dsRNA-ssRNA junction. Our current study thus facilitates the prediction of novel microRNAs and will assist in the rational design of small hairpin RNAs for RNA interference.

Animals↗

PubMed2006

Identification of biochemical networks by S-tree based genetic programming.

MOTIVATION: Most previous approaches to model biochemical networks have focused either on the characterization of a network structure with a number of components or on the estimation of kinetic parameters of a network with a relatively small number of components. For system-level understanding, however, we should examine both the interactions among the components and the dynamic behaviors of the components. A key obstacle to this simultaneous identification of the structure and parameters is the lack of data compared with the relatively large number of parameters to be estimated. Hence, there are many plausible networks for the given data, but most of them are not likely to exist in the real system. RESULTS: We propose a new representation named S-trees for both the structural and dynamical modeling of a biochemical network within a unified scheme. We further present S-tree based genetic programming to identify the structure of a biochemical network and to estimate the corresponding parameter values at the same time. While other evolutionary algorithms require additional techniques for sparse structure identification, our approach can automatically assemble the sparse primitives of a biochemical network in an efficient way. We evaluate our algorithm on the dynamic profiles of an artificial genetic network. In 20 trials for four settings, we obtain the true structure and their relative squared errors are <5% regardless of releasing constraints about structural sparseness. In addition, we confirm that the proposed algorithm is robust within +/-10% noise ratio. Furthermore, the proposed approach ensures a reasonable estimate of a real yeast fermentation pathway. The comparatively less important connections with non-zero parameters can be detected even though their orders are below 10(-2). To demonstrate the usefulness of the proposed algorithm for real experimental biological data, we provide an additional example on the transcriptional network of SOS response to DNA damage in Escherichia coli. We confirm that the proposed algorithm can successfully identify the true structure except only one relation.

Algorithms↗

PubMed2005

Protein sequence-based risk classification for human papillomaviruses.

Human papillomaviruses (HPVs) are small DNA tumor viruses which infect epithelial tissues and induce hyperproliferative lesions. Infection by high-risk genital HPVs is associated with the development of anogenital cancers. Classification of risk types is important in understanding the mechanisms in infection and in developing novel instruments for medical examination such as DNA microarrays. The sequence-based classification methods are useful in classifying risk types by considering residues in conserved positions. In this paper, we present a machine learning approach to the classification of HPV risk types by using the protein sequences. Our approach is based on the hidden Markov model and the kernel method. The former searches informative subsequence positions and the latter computes efficiently to classify protein sequences. In the experiments, the classifier predicted four unknown HPV types exactly. An additional result shows that the kernel-based classifiers learned with more informative subsequences outperform the classifiers learned with the whole sequence or random subsequences.

Amino Acid Sequence↗

PubMed2005

Human microRNA prediction through a probabilistic co-learning model of sequence and structure.

MicroRNAs (miRNAs) are small regulatory RNAs of approximately 22 nt. Although hundreds of miRNAs have been identified through experimental complementary DNA cloning methods and computational efforts, previous approaches could detect only abundantly expressed miRNAs or close homologs of previously identified miRNAs. Here, we introduce a probabilistic co-learning model for miRNA gene finding, ProMiR, which simultaneously considers the structure and sequence of miRNA precursors (pre-miRNAs). On 5-fold cross-validation with 136 referenced human datasets, the efficiency of the classification shows 73% sensitivity and 96% specificity. When applied to genome screening for novel miRNAs on human chromosomes 16, 17, 18 and 19, ProMiR effectively searches distantly homologous patterns over diverse pre-miRNAs, detecting at least 23 novel miRNA gene candidates. Importantly, the miRNA gene candidates do not demonstrate clear sequence similarity to the known miRNA genes. By quantitative PCR followed by RNA interference against Drosha, we experimentally confirmed that 9 of the 23 representative candidate genes express transcripts that are processed by the miRNA biogenesis enzyme Drosha in HeLa cells, indicating that ProMiR may successfully predict miRNA genes with at least 40% accuracy. Our study suggests that the miRNA gene family may be more abundant than previously anticipated, and confer highly extensive regulatory networks on eukaryotic cells.

Algorithms↗

PubMed2005

CrossChip: a system supporting comparative analysis of different generations of Affymetrix arrays.

SUMMARY: To increase compatibility between different generations of Affymetrix GeneChip arrays, we propose a method of filtering probes based on their sequences. Our method is implemented as a web-based service for downloading necessary materials for converting the raw data files (*.CEL) for comparative analysis. The user can specify the appropriate level of filtering by setting the criteria for the minimum overlap length between probe sequences and the minimum number of usable probe pairs per probe set. Our website supports a within-species comparison for human and mouse GeneChip arrays. AVAILABILITY: http://www.crosschip.org

Algorithms↗

PubMed2005

Bayesian model averaging of Bayesian network classifiers over multiple node-orders: application to sparse datasets.

Bayesian model averaging (BMA) can resolve the overfitting problem by explicitly incorporating the model uncertainty into the analysis procedure. Hence, it can be used to improve the generalization performance of Bayesian network classifiers. Until now, BMA of Bayesian network classifiers has only been performed in some restricted forms, e.g., the model is averaged given a single node-order, because of its heavy computational burden. However, it can be hard to obtain a good node-order when the available training dataset is sparse. To alleviate this problem, we propose BMA of Bayesian network classifiers over several distinct node-orders obtained using the Markov chain Monte Carlo sampling technique. The proposed method was examined using two synthetic problems and four real-life datasets. First, we show that the proposed method is especially effective when the given dataset is very sparse. The classification accuracy of averaging over multiple node-orders was higher in most cases than that achieved using a single node-order in our experiments. We also present experimental results for test datasets with unobserved variables, where the quality of the averaged node-order is more important. Through these experiments, we show that the difference in classification performance between the cases of multiple node-orders and single node-order is related to the level of noise, confirming the relative benefit of averaging over multiple node-orders for incomplete data. We conclude that BMA of Bayesian network classifiers over multiple node-orders has an apparent advantage when the given dataset is sparse and noisy, despite the method's heavy computational cost.

Algorithms↗

PubMed2005

Bayesian network learning with feature abstraction for gene-drug dependency analysis.

Combined analysis of the microarray and drug-activity datasets has the potential of revealing valuable knowledge about various relations among gene expressions and drug activities in the malignant cell. In this paper, we apply Bayesian networks, a tool for compact representation of the joint probability distribution, to such analysis. For the alleviation of data dimensionality problem, the huge datasets were condensed using a feature abstraction technique. The proposed analysis method was applied to the NCI60 dataset (http://discover.nci.nih.gov) consisting of gene expression profiles and drug activity patterns on human cancer cell lines. The Bayesian networks, learned from the condensed dataset, identified most of the salient pairwise correlations and some known relationships among several features in the original dataset, confirming the effectiveness of the proposed feature abstraction method. Also, a survey of the recent literature confirms the several relationships appearing in the learned Bayesian network to be biologically meaningful.

Algorithms↗

PubMed2004

Solving traveling salesman problems with DNA molecules encoding numerical values.

We introduce a DNA encoding method to represent numerical values and a biased molecular algorithm based on the thermodynamic properties of DNA. DNA strands are designed to encode real values by variation of their melting temperatures. The thermodynamic properties of DNA are used for effective local search of optimal solutions using biochemical techniques, such as denaturation temperature gradient polymerase chain reaction and temperature gradient gel electrophoresis. The proposed method was successfully applied to the traveling salesman problem, an instance of optimization problems on weighted graphs. This work extends the capability of DNA computing to solving numerical optimization problems, which is contrasted with other DNA computing methods focusing on logical problem solving.

Algorithms↗

PubMed2003

Identification of novel anti-angiogenic factors by in silico functional gene screening method.

Angiogenesis, the formation of new blood vessels out of pre-existing capillaries, occurs in a variety of pathophysiological conditions, and is regulated by a balance of angiogenic activators and inhibitors. To identify novel angiogenic factors, we developed a gene screening method by combining the prediction analysis of transcription factor (TF) binding site and the chromosomal localization analysis. First, we analyzed the promoter sequences from known angiogenesis-related factors using the MATINSPECTOR program in TRANSFAC database. Interestingly, we found that the binding site of LMO2 complex is highly conserved in the promoter regions of these factors. Second, we analyzed chromosome loci based on the hypothesis that angiogenesis-related factors might be co-localized in a specific chromosomal band. We found that angiogenesis-related factors are localized in specific 14 chromosomal bands including 5q31 and 19q13 using AngioDB and LocusLink database mining. From these two approaches, we identified 32 novel candidates that have the LMO2 complex binding site in their promoter and are located on one of 14 chromosomal bands. Among them, human recombinant troponin T and spectrin markedly inhibited the neovascularization in vivo and in vitro. Collectively, we suggest that the combination of the prediction analysis of TF binding site and the chromosomal localization analysis might be a useful strategy for gene screening of angiogenesis.

Angiogenesis Inducing Agents↗